In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.

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A pressure control method for emulsion pump station based on elman neural network

In this paper, normalised state vectors are used to demonstrate the state equations of Extended Kalman Filter (EKF) based sensorless Permanent Magnet Synchronous Motor (PMSM) drive. Based on the normalised EKF equations, Simple Genetic Algorithm (SGA) is employed to optimize the noise covariance and weight matrices of EKF parameters, which thereby reduces the parameter adjusting time and ensures stability of filters in estimations of position and speed. The simulations for SGA training are carried out by MATLAB/Simulink. The experimental sensorless drive system employing Field Oriented Control (FOC) method and SGA is implemented on STM32F103. The simulating and experimental results indicate the effectiveness of the proposed method.

Sensorless Permanent Magnet Synchronous Motor drive using an optimized and normalized Extended Kalman filter

The AC motor control by neural networks includes the reconstruction errors in a certain degree, which can cause the non-convergence in the control results. To learn the complete system dynamics of the sensorless PMSM, a neural network adaptive speed control strategy is proposed to eliminate the NN reconstruction errors. A robust modification term, which is a function of estimation error and an additional tunable parameter, is introduced to guarantee the asymptotic stability of the speed estimation. A rotor-flux-oriented vector control is employed as the basic control strategy for the sensorless PMSM drive system. The simulation results demonstrated the validity and feasibility of the proposed control strategy.

Sensorless PMSM Speed Control Based on NN Adaptive Observer

Abstract Bilateral filtering is an image processing technique commonly adopted as intermediate step of several computer vision tasks. Opposite to the conventional image filtering, which is based on convolving the input pixels with a static kernel, the bilateral filtering computes its weights on the fly according to the current pixel values and some tuning parameters. Such additional elaborations involve nonlinear weighted averaging operations, which make difficult the deployment of bilateral filtering within existing vision technologies based on real-time and low-energy hardware architectures. This paper presents a new approximation strategy that aims to improve the energy efficiency of circuits implementing the bilateral filtering function, while preserving their real-time performances and elaboration accuracy. In contrast to the state-of-the-art, the proposed technique allows the filtering action to be on the fly adapted to both the current pixel values and to the tuning parameters, thus avoiding any architectural modification or tables update. When hardware implemented within the Xilinx Zynq XC7Z020 FPGA device, a 5 × 5 filter based on the proposed method processes 237.6 Mega pixels per second and consumes just 0.92 nJ per pixel, thus improving the energy efficiency by up to 2.8 times over the competitors. The impact of the proposed approximation on three different imaging applications has been also evaluated. Experiments demonstrate reasonable accuracy penalties over the accurate counterparts. 

Approximate bilateral filters for real-time and low-energy imaging applications on FPGAs

This study introduces an optimal topology of vision transformers for real-time video action recognition in a cloud-based solution. Although model performance is a key criterion for real-time video analysis use cases, inference latency plays a more crucial role in adopting such technology in real-world scenarios. Our objective is to reduce the inference latency of the solution while admissibly maintaining the vision transformer’s performance. Thus, we employed the optimal cloud components as the foundation of our machine learning pipeline and optimized the topology of vision transformers. We utilized UCF101, including more than one million action recognition video clips. The modeling pipeline consists of a preprocessing module to extract frames from video clips, training two-dimensional (2D) vision transformer models, and deep learning baselines. The pipeline also includes a postprocessing step to aggregate the frame-level predictions to generate the video-level predictions at inference. The results demonstrate that our optimal vision transformer model with an input dimension of 56 × 56 × 3 with eight attention heads produces an F1 score of 91.497% for the testing set. The optimized vision transformer reduces the inference latency by 40.70%, measured through a batch-processing approach, with a 55.63% faster training time than the baseline. Lastly, we developed an enhanced skip-frame approach to improve the inference latency by finding an optimal ratio of frames for prediction at inference, where we could further reduce the inference latency by 57.15%. This study reveals that the vision transformer model is highly optimizable for inference latency while maintaining the model performance. 

Optimal Topology of Vision Transformer for Real-Time Video Action Recognition in an End-To-End Cloud Solution

This paper presents an approach of position sensorless control for Permanent Magnet Linear Synchronous Motors (PMLSM) based on Elman neural network. The Elman neural network observer can be considered as a special kind of feed-forward neural network with additional memory neurons and local feedback. Because of the context neurons and local recurrent connections between the context layer and the hidden layer, it facilitates the nonlinear states estimation for the sensorless control of PMLSM. The Elman neural network is trained both off-line and on-line. In the off-line training process with the training data, the connective weights of the Elman neural network are trained by the Levenberg-Marquardt algorithm, while on-line learning, the connective weights of the Elman neural network are trained using supervised gradient decent method. The effectiveness of the proposed observer is confirmed by the digital simulations results. Keywords-permanent magnet linear synchronous motor; position sensorless control; Elman neural network; observer

Position Sensorless Control for PMLSM Using Elman Neural Network

High anti-interference and FPGA-oriented method for real-time ship detection based on structured LBP features

Edge computing has been emerging as a popular scenario for model inference. However, the inference performance on edge devices (e.g., Multi-Core DSP, FGPA, etc.) suffers from inefficiency due to the lack of highly optimized inference frameworks. Previous model inference frameworks are mainly developed in an operator-centric way, which provides insufficient acceleration to edge-based inference. Besides, the operator-centric framework incurs significant costs for continuous development and maintenance. In this paper, we propose Xenos, which can automatically conduct dataflow-centric optimization of the computation graph and accelerate inference in two dimensions. Vertically, Xenos develops operator linking technique to improve data locality by restructuring the inter-operator dataflow. Horizontally, Xenos develops DSP-aware operator split technique to enable higher parallelism across multiple DSP units. Our evaluation proves the effectiveness of vertical and horizontal dataflow optimization, which reduce the inference time by 21.2\%--84.9\% and 17.9\%--96.2\% , respectively. Besides, Xenos also outperforms the widely-used TVM by 3.22$\times$--17.92$\times$. Moreover, we extend Xenos to a distributed solution, which we call d-Xenos. d-Xenos employs multiple edge devices to jointly conduct the inference task and achieves a speedup of 3.68x--3.78x compared with the single device.

Xenos: Dataflow-Centric Optimization to Accelerate Model Inference on Edge Devices

Convolutional Neural Networks (CNNs) have achieved remarkable performance at a huge computational cost. By improving the model sparsity, it can effectively reduce the complexity. However, with deepening of sparsity, the problems of unbalanced workloads, computing fragmentation and mapping access conflict caused by irregular sparsity have become more and more remarkable. These problems pose great challenges for efficient computation of sparse CNN s. In order to make full use of two side of sparsity introduced by activations and weights, and overcome the above problems, this paper proposes an efficient sparse CNN s accelerator on FPGA to achieve the inference acceleration. We designed and implemented the accelerator on the Zynq UltraScale+ MPSoC ZCU102 evaluation board. By running AlexNet, VGG16 and ResNet50 networks on the accelerator to evaluated the peeformance. Experimental results show that the method proposed in this paper can achieve more than 97% reduction in collision rate and 2.35x improvement in computing performance and 9.37x improvement in energy efficiency.

An Efficient Sparse CNNs Accelerator on FPGA

LOCP: Latency-optimized channel pruning for CNN inference acceleration on GPUs

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