In the process of aquaculture,monitoring the number of fish bait particles is of great significance to improve the growth and welfare of fish.Although the counting method based on onvolutional neural network(CNN)achie...In the process of aquaculture,monitoring the number of fish bait particles is of great significance to improve the growth and welfare of fish.Although the counting method based on onvolutional neural network(CNN)achieve good accuracy and applicability,it has a high amount of parameters and computation,which limit the deployment on resource-constrained hardware devices.In order to solve the above problems,this paper proposes a lightweight bait particle counting method based on shift quantization and model pruning strategies.Firstly,we take corresponding lightweight strategies for different layers to flexibly balance the counting accuracy and performance of the model.In order to deeply lighten the counting model,the redundant and less informative weights of the model are removed through the combination of model quantization and pruning.The experimental results show that the compression rate is nearly 9 times.Finally,the quantization candidate value is refined by introducing a power-of-two addition term,which improves the matches of the weight distribution.By analyzing the experimental results,the counting loss at 3 bit is reduced by 35.31%.In summary,the lightweight bait particle counting model proposed in this paper achieves lossless counting accuracy and reduces the storage and computational overhead required for running convolutional neural networks.展开更多
单边直线感应电机(Single-sided Linear Induction Motors,SLIMs)作为中低速磁悬浮列车的驱动装置,其推力和法向力的耦合控制特性以及系统的抗扰动能力,对于磁浮列车的牵引及悬浮系统动态运行极为重要。本文分别建立SLIM的最大推力点和...单边直线感应电机(Single-sided Linear Induction Motors,SLIMs)作为中低速磁悬浮列车的驱动装置,其推力和法向力的耦合控制特性以及系统的抗扰动能力,对于磁浮列车的牵引及悬浮系统动态运行极为重要。本文分别建立SLIM的最大推力点和法向力过零点的转差频率-电流控制模型,研究了将两者协同优化控制的变电流分段变转差频率(Variant-Current Variable Slip-Frequency,VCVSF)策略。建立了在稳态转差频率下的法向力控制电流与悬浮气隙的二维状态空间Popov超稳定模型,通过在线性环节增加前馈补偿器保证等价反馈系统严格正实,从而实现零点处法向力稳态振动的自收敛。实验研究验证了本文所提控制算法的有效性。展开更多
基金supported by the National Key Research and Development Program of China(No.2019YFD0901000)。
文摘In the process of aquaculture,monitoring the number of fish bait particles is of great significance to improve the growth and welfare of fish.Although the counting method based on onvolutional neural network(CNN)achieve good accuracy and applicability,it has a high amount of parameters and computation,which limit the deployment on resource-constrained hardware devices.In order to solve the above problems,this paper proposes a lightweight bait particle counting method based on shift quantization and model pruning strategies.Firstly,we take corresponding lightweight strategies for different layers to flexibly balance the counting accuracy and performance of the model.In order to deeply lighten the counting model,the redundant and less informative weights of the model are removed through the combination of model quantization and pruning.The experimental results show that the compression rate is nearly 9 times.Finally,the quantization candidate value is refined by introducing a power-of-two addition term,which improves the matches of the weight distribution.By analyzing the experimental results,the counting loss at 3 bit is reduced by 35.31%.In summary,the lightweight bait particle counting model proposed in this paper achieves lossless counting accuracy and reduces the storage and computational overhead required for running convolutional neural networks.
