Single-defect thermometer as a probe of electron heating in Bi

1994 ◽  
Vol 49 (4) ◽  
pp. 2959-2962 ◽  
Author(s):  
Kookjin Chun ◽  
Norman O. Birge
Author(s):  
L. Adhikari ◽  
G.P. Zank ◽  
L.-L. Zhao ◽  
M. Nakanotani ◽  
S. Tasnim

2019 ◽  
Vol 26 (10) ◽  
pp. 103101
Author(s):  
Chong Lv ◽  
Bao-Zhen Zhao ◽  
Feng Wan ◽  
Hong-Bo Cai ◽  
Xiang-Hao Meng ◽  
...  

2009 ◽  
Vol 708 (2) ◽  
pp. 1545-1550 ◽  
Author(s):  
Jian Ding ◽  
Feng Yuan ◽  
Edison Liang

Crystals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 643
Author(s):  
Soo-Ho Jo ◽  
Byeng D. Youn

Several previous studies have been dedicated to incorporating double defect modes of a phononic crystal (PnC) into piezoelectric energy harvesting (PEH) systems to broaden the bandwidth. However, these prior studies are limited to examining an identical configuration of the double defects. Therefore, this paper aims to propose a new design concept for PnCs that examines differently configured double defects for broadband elastic wave energy localization and harvesting. For example, a square-pillar-type unit cell is considered and a defect is considered to be a structure where one piezoelectric patch is bonded to a host square lattice in the absence of a pillar. When the double defects introduced in a PnC are sufficiently distant from each other to implement decoupling behaviors, each defect oscillates like a single independent defect. Here, by differentiating the geometric dimensions of two piezoelectric patches, the defects’ dissimilar equivalent inertia and stiffness contribute to individually manipulating defect bands that correspond to each defect. Hence, with adequately designed piezoelectric patches that consider both the piezoelectric effects on shift patterns of defect bands and the characteristics for the output electric power obtained from a single-defect case, we can successfully localize and harvest the elastic wave energy transferred in broadband frequencies.


Metals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 223
Author(s):  
Dongcheng Wang ◽  
Yanghuan Xu ◽  
Bowei Duan ◽  
Yongmei Wang ◽  
Mingming Song ◽  
...  

The edge of a hot rolling strip corresponds to the area where surface defects often occur. The morphologies of several common edge defects are similar to one another, thereby leading to easy error detection. To improve the detection accuracy of edge defects, the authors of this paper first classified the common edge defects and then made a dataset of edge defect images on this basis. Subsequently, edge defect recognition models were established on the basis of LeNet-5, AlexNet, and VggNet-16 by using a convolutional neural network as the core. Through multiple groups of training and recognition experiments, the model’s accuracy and recognition time of a single defect image were analyzed and compared with recognition models with different learning rates and sample batches. The experimental results showed that the recognition model based on the AlexNet had a maximum accuracy of 93.5%, and the average recognition time of a single defect image was 0.0035 s, which could meet the industry requirement. The research results in this paper provide a new method and thought for the fine detection of edge defects in hot rolling strips and have practical significance for improving the surface quality of hot rolling strips.


2012 ◽  
Vol 614-615 ◽  
pp. 1629-1632
Author(s):  
Gang Xu ◽  
Yun Sun

Applying transfer matrix method, we get reflection and transmission coefficient of finite one dimensional photonic crystals. At the same time, we consider the position influence of single defect. We find the frequency of defect mode is same, but the height of transmission peak is not same when single defect is in different position of crystal. The transmission peak is maximum when the defect is in center of finite one dimensional photonic crystals.


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