Effects of Swirl and Spark Plug Shape on Combustion Characteristic in a High Speed Single-Shot Visualized SI Engine

1995 ◽  
Author(s):  
Seong-Soo Kim ◽  
Sung-Soo Kim
Keyword(s):  
High Speed ◽  
Combustion Characteristic ◽  
Single Shot ◽  
Si Engine ◽  
Spark Plug

Effects of Swirl on High-Speed Combustion in a Single-Shot Optical SI Engine

1992 ◽  
Vol 206 (4) ◽  
pp. 237-247 ◽  
Author(s):  
J O Han ◽  
S S Kim
Keyword(s):  
Growth Rate ◽  
Flame Front ◽  
High Speed ◽  
Engine Speed ◽  
Single Shot ◽  
Combined Effects ◽  
Cylinder Pressure ◽  
Si Engine ◽  
Compression And Expansion ◽  
Overall Performance

Single-shot tests in a single-cylinder, optical SI engine operated by a rapid compression and expansion machine were performed in order to investigate the combined effects of engine speed, ignition position and swirl on early combustion and overall performance. For central ignition, swirl showed consistently favourable effects on combustion-related performance. However, at half-radius ignition the desirable swirl effect persisted at low engine speeds but faded away as the speed increased. This reversing of trends can be partially explained by differences in the maximum cylinder pressure, flame growth rate and flame front wrinkling.

scholarly journals High-Speed Railway Clearance Intrusion Detection with Improved SSD Network

2019 ◽  
Vol 9 (15) ◽  
pp. 2981 ◽  
Author(s):  
Baoqing Guo ◽  
Jiafeng Shi ◽  
Liqiang Zhu ◽  
Zujun Yu
Keyword(s):  
Intrusion Detection ◽  
High Speed ◽  
Rapid Development ◽  
Single Shot ◽  
L1 Norm ◽  
Low Level ◽  
Model Size ◽  
Public Dataset ◽  
The Mean ◽  
High Level

With the rapid development of high-speed railways, any objects intruding railway clearance will do great threat to railway operations. Accurate and effective intrusion detection is very important. An original Single Shot multibox Detector (SSD) can be used to detect intruding objects except small ones. In this paper, high-level features are deconvolved to low-level and fused with original low-level features to enhance their semantic information. By this way, the mean average precision (mAP) of the improved SSD algorithm is increased. In order to decrease the parameters of the improved SSD network, the L1 norm of convolution kernel is used to prune the network. Under this criterion, both the model size and calculation load are greatly reduced within the permitted precision loss. Experiments show that the mAP of our method on PASCAL VOC public dataset and our railway datasets have increased by 2.52% and 4.74% respectively, when compared to the original SSD. With our method, the elapsed time of each frame is only 31 ms on GeForce GTX1060.

scholarly journals Dense-RefineDet for Traffic Sign Detection and Classification

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6570
Author(s):  
Chang Sun ◽  
Yibo Ai ◽  
Sheng Wang ◽  
Weidong Zhang
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Design Method ◽  
Contextual Information ◽  
Real Life ◽  
Single Shot ◽  
Traffic Sign ◽  
Traffic Signs ◽  
High Level ◽  
Anchor Design

Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (RefineDet) to maintain a suitable accuracy–speed trade-off. We constructed a dense connection-related transfer-connection block to combine high-level feature layers with low-level feature layers to optimize the use of the higher layers to obtain additional contextual information. Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs (recall: 84.3%, 95.2%, and 92.6%; precision: 83.9%, 95.6%, and 94.0%). Moreover, experiments using the Caltech pedestrian dataset indicated that the miss rate of Dense-RefineDet was 54.03% (pedestrian height > 20 pixels), which outperformed other state-of-the-art methods.

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