AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing

With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensive attention. And these applications necessitate the mobile edge computing scheme, i.e., offloading partial computation from mobile devices to edge devices for inference acceleration and transmission load reduction. The current practices have relied on collaborative DNN partition and offloading to satisfy the predefined latency requirements, which is intractable to adapt to the dynamic deployment context at runtime. AdaMEC, a context-adaptive and dynamically-combinable DNN deployment framework is proposed to meet these requirements for mobile edge computing, which consists of three novel techniques. First, once-for-all DNN pre-partition divides DNN at the primitive operator level and stores partitioned modules into executable files, defined as pre-partitioned DNN atoms. Second, context-adaptive DNN atom combination and offloading introduces a graph-based decision algorithm to quickly search the suitable combination of atoms and adaptively make the offloading plan under dynamic deployment contexts. Third, runtime latency predictor provides timely latency feedback for DNN deployment considering both DNN configurations and dynamic contexts. Extensive experiments demonstrate that AdaMEC outperforms state-of-the-art baselines in terms of latency reduction by up to 62.14% and average memory saving by 55.21%

A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine

The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods.</p

Adaptive Model Quantization Method for Intelligent Internet of Things Terminal

With the rapid development of deep learning and the Internet of Everything,the combination of deep learning and mobile terminal devices has become a major research hotspot.While deep learning improves the performance of terminal devices,it also faces many challenges when deploying models on resource-constrained terminal devices,such as the limited computing and storage resources of terminal devices,and the inability of deep learning models to adapt to changing device context.We focus on the adaptive quantization of deep models with resource adaptive.Specifically,a resource-adaptive mixed-precision model quantization method is proposed,which firstly uses the gated network and the backbone network to construct the model and partitioned model at layer as the granularity to find the best quantization policy of the model,and combines the edge devices to reduce the model resource consumption.In order to find the optimal model quantization policy,FPGA-based deep learning model deployment is adopted.When the model needs to be deployed on resource-constrained edge devices,adaptive training is performed according to resource constraints,and a quantization-aware method isadopted to reduce the accuracy loss caused by model quantization.Experimental results show that our method can reduce the storage space by 50% while retaining 78% accuracy,and reduce the energy consumption by 60% on the FPGA device with no more than 2% accuracy loss

Residual flux density estimation of the three-phase transformer using BP neural network

When the off-line transformer is re-energized, the phase-controlled switching strategy can avoid the generation of inrush current by controlling the phase. To determine the closing phase, the residual flux density (Br) in the transformer core needs to be accurately measured. This paper proposes a Br estimation method for three-phase transformers based on the finite element method and BP neural network. Firstly, the direction of Br in each phase core is determined based on the transient current characteristics. Then, the three-phase transformer is simulated and the BP neural network is trained to estimate the Br based on the simulation results. The experimental results on a three-phase transformer show that the proposed method can accurately determine the direction and amplitude of Br in each phase of the three-phase transformer

Fast demagnetization method for power transformers combined with residual flux measurement

When power transformers are re-energized, the inrush current may be generated and damage electrical equipment. In order to suppress the generation of serious inrush current, the residual flux (RF) in the power transformer core needs to be eliminated. However, existing methods require first magnetizing the core to saturation to calibrate the magnetic flux due to the unknown residual flux density (Br), which increases demagnetization power and time. In addition, the lack of effective Br measurement methods makes it impossible to evaluate the demagnetization effect. Therefore, this paper proposes a demagnetization method that considers the amount of Br, which achieves accurate measurement and quantitative elimination of Br in the transformer core. The measurement is first performed by applying DC voltages of different polarities, and then a specific demagnetization DC voltage is applied based on the measurement results. The magnetic flux in the transformer core is reduced directly to zero without calibrating flux throughout the entire process. Experimental studies are performed on core-type transformers and 10 kV, 250 kVA power transformers, and the experiment results show that the Br is reduced to within 1 % of the knee-point flux and the measurement error is within 4 %, which proves the effectiveness of the proposed method. Compared with existing methods, the demagnetization time can be reduced to less than 1 s and the demagnetization power can be reduced by 95 % using the proposed method