scholarly journals ZmGRAS11 , transactivated by Opaque2, positively regulates kernel size in maize

2021 ◽  
Author(s):  
Ye Li ◽  
Shuai Ma ◽  
Qianqian Zhao ◽  
Di Lv ◽  
Baobao Wang ◽  
...  
Keyword(s):  
Kernel Size

The Effect of Water Stress on Wheat Kernel Size, Color and Protein Composition

2007 ◽  
Vol 22 (2) ◽  
pp. 157-171 ◽  
Author(s):  
Iwona Konopka ◽  
Małgorzata Tańska ◽  
Agnieszka Pszczółkowska ◽  
Gabriel Fordoński ◽  
Witold Kozirok ◽  
...  
Keyword(s):  
Water Stress ◽  
Protein Composition ◽  
Kernel Size ◽  
Effect Of Water ◽  
Wheat Kernel

scholarly journals sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network

2021 ◽  
Vol 11 (10) ◽  
pp. 4678
Author(s):  
Chao Chen ◽  
Weiyu Guo ◽  
Chenfei Ma ◽  
Yongkui Yang ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Motion Control ◽  
Large Scale ◽  
Human Motion ◽  
Kernel Size ◽  
Continuous Motion ◽  
Convolutional Network ◽  
Continuous Estimation ◽  
Discrete Motion ◽  
Precision Rate

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information—is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel’s size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN’s problem: that it is difficult to fully extract the sEMG’s temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 × 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 × 3), LS-TCN (kernel size of 1 × 31) improves the precision rate by 6.6%.

scholarly journals Video Frame Interpolation via Deformable Separable Convolution

2020 ◽  
Vol 34 (07) ◽  
pp. 10607-10614 ◽  
Author(s):  
Xianhang Cheng ◽  
Zhenzhong Chen
Keyword(s):  
State Of The Art ◽  
Video Frame ◽  
Kernel Size ◽  
Frame Interpolation ◽  
Interpolation Methods ◽  
Video Frames ◽  
Convolution Process ◽  
Strong Performance ◽  
Existing Frames ◽  
Better Than

Learning to synthesize non-existing frames from the original consecutive video frames is a challenging task. Recent kernel-based interpolation methods predict pixels with a single convolution process to replace the dependency of optical flow. However, when scene motion is larger than the pre-defined kernel size, these methods yield poor results even though they take thousands of neighboring pixels into account. To solve this problem in this paper, we propose to use deformable separable convolution (DSepConv) to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels. In addition, we show that the kernel-based methods and conventional flow-based methods are specific instances of the proposed DSepConv. Experimental results demonstrate that our method significantly outperforms the other kernel-based interpolation methods and shows strong performance on par or even better than the state-of-the-art algorithms both qualitatively and quantitatively.

scholarly journals Kernel size‐related genes revealed by an integratedeQTLanalysis during early maize kernel development

2019 ◽  
Vol 98 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Junling Pang ◽  
Junjie Fu ◽  
Na Zong ◽  
Jing Wang ◽  
Dandan Song ◽  
...  
Keyword(s):  
Kernel Size ◽  
Kernel Development ◽  
Maize Kernel

Comparison of Peanut Dryer Control Strategies1

1996 ◽  
Vol 23 (2) ◽  
pp. 86-90 ◽  
Author(s):  
C. L. Butts
Keyword(s):  
Control Algorithm ◽  
Energy Savings ◽  
Control Strategies ◽  
Size Distributions ◽  
Curing Time ◽  
Kernel Size ◽  
Milling Quality ◽  
Set Point ◽  
Net Revenue ◽  
Hourly Basis

Abstract Peanuts were mechanically cured from field moisture contents ranging from 11.5 to 32.8% wet basis to levels acceptable for marketing (< 10.5%) using two dryer control strategies. The first control algorithm consisted of a constant thermostat setting of 39 C, while the second required manual thermostat control on an hourly basis such that the minimum plenum relative humidity was between 40 and 60% and the maximum plenum temperature was less than 39 C. The average drying rate using the variable thermostat set point (0.3%/hr) was half that obtained with the constant set point (0.6%/hr). Average curing time for the variable thermostat setting was 56% longer than for the peanuts cured using the constant thermostat. Fuel consumption was reduced by approximately 30% using the variable set point. Kernel size distributions and milling quality indicated by bald kernels were significantly better (P ≤ 0.1) for peanuts cured using the variable thermostat control. Increasing available dryer capacity by 40% would allow the buying point manager to handle the same amount of peanuts during the same harvest interval. Economic analysis showed that the annual capital cost for additional drying equipment could not be offset by energy savings alone. Based on increased shelled product value and energy savings, shellers could realize an increase in net revenue of approximately $14/1000 kg of farmers stock peanuts by using a variable thermostat set point.

Motion-blur kernel size estimation via learning a convolutional neural network

2019 ◽  
Vol 119 ◽  
pp. 86-93 ◽  
Author(s):  
Lerenhan Li ◽  
Nong Sang ◽  
Luxin Yan ◽  
Changxin Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Blur ◽  
Size Estimation ◽  
Kernel Size ◽  
Blur Kernel
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