Finding Sparse Structures for Domain Specific Neural Machine Translation

Neural machine translation often adopts the fine-tuning approach to adapt to specific domains. However, nonrestricted fine-tuning can easily degrade on the general domain and over-fit to the target domain. To mitigate the issue, we propose Prune-Tune, a novel domain adaptation method via gradual pruning. It learns tiny domain-specific sub-networks during fine-tuning on new domains. Prune-Tune alleviates the over-fitting and the degradation problem without model modification. Furthermore, Prune-Tune is able to sequentially learn a single network with multiple disjoint domain-specific sub-networks for multiple domains. Empirical experiment results show that Prune-Tune outperforms several strong competitors in the target domain test set without sacrificing the quality on the general domain in both single and multi-domain settings. The source code and data are available at https://github.com/ohlionel/Prune-Tune.Comment: Accepted to AAAI 202

Seismic retrofit design and risk assessment of an irregular thermal power plant building

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154642/1/tal1719_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154642/2/tal1719.pd

8-MW wind turbine tower computational shell buckling benchmark. Part 1:an international ‘round-robin’ exercise

An assessment of the elastic-plastic buckling limit state for multi-strake wind turbine support towers poses a particular challenge for the modern finite element analyst, who must competently navigate numerous modelling choices related to the tug-of-war between meshing and computational cost, the use of solvers that are robust to highly nonlinear behaviour, the potential for multiple near-simultaneously critical failure locations, the complex issue of imperfection sensitivity and finally the interpretation of the data into a safe and economic design.This paper reports on an international ‘round-robin’ exercise conducted in 2022 aiming to take stock of the computational shell buckling expertise around the world which attracted 29 submissions. Participants were asked to perform analyses of increasing complexity on a standardised benchmark of an 8-MW multi-strake steel wind turbine support tower segment, from a linear elastic stress analysis to a linear bifurcation analysis to a geometrically and materially nonlinear buckling analysis with imperfections. The results are a showcase of the significant shell buckling expertise now available in both industry and academia.This paper is the first of a pair. The second paper presents a detailed reference solution to the benchmark, including an illustration of the Eurocode-compliant calibration of two important imperfection forms

In-Store Shopping Experience Enhancement: Designing a Physical Object-Recognition Interactive Renderer

Following the rapid spread of online-shopping services on both internet and smart devices, the traditional way of promoting and trading in physical retail stores has been challenged. To increase sales, retailers have spent an enormous amount of resources to maintain the attractions of ‘traditional’ physical store in a digital shopping behaviour dominated world. Unfortunately, the outcome leaves much to be desired. This study discusses the need of such hybrid-shopping style through an integrated process of customer investigation, observation and user testing. This paper using footwear shopping as a case study. The authors proposed an inventive installation to re-strengthen the inter-connections among customers, products and retailers using physical object recognition and 3D projection mapping technologies. This interactive installation allows customers to personalize their preferences through manipulating the physical products with Augmented Reality (AR) rendering effects. Furthermore, this system also provides an alternative solution to reform the product-promotion and production progress. This design can be applied to the promotion of many other kind of products

Two-Stage Battery Energy Storage System (BESS) in AC Microgrids with Balanced State-of-Charge and Guaranteed Small-Signal Stability

In this paper, a two-stage battery energy storage system (BESS) is implemented to enhance the operation condition of conventional battery storage systems in a microgrid. Particularly, the designed BESS is composed of two stages, i.e., Stage I: integration of dispersed energy storage units (ESUs) using parallel DC/DC converters, and Stage II: aggregated ESUs in grid-connected operation. Different from a conventional BESS consisting of a battery management system (BMS) and power conditioning system (PCS), the developed two-stage architecture enables additional operation and control flexibility in balancing the state-of-charge (SoC) of each ESU and ensures the guaranteed small-signal stability, especially in extremely weak grid conditions. The above benefits are achieved by separating the control functions between the two stages. In Stage I, a localized power sharing scheme based on the SoC of each particular ESU is developed to manage the SoC and avoid over-charge or over-discharge issues; on the other hand, in Stage II, an additional virtual impedance loop is implemented in the grid-interactive DC/AC inverters to enhance the stability margin with multiple parallel-connected inverters integrating at the point of common coupling (PCC) simultaneously. A simulation model based on MATLAB/Simulink is established, and simulation results verify the effectiveness of the proposed BESS architecture and the corresponding control diagram

A Novel High Efficiency Quasi-Resonant Converter

In this paper, a new constant-frequency quasi-resonant converter is proposed. Compared with the traditional LLC converter, the proposed converter can effectively reduce the range of the operating frequency. The output voltage is changed to adjust the reactance of the resonant cavity. The proposed converter has a better loss factor. To verify the theoretical analysis and soft-switching condition, a 250 W, 100 V output prototype was built and compared with the full-bridge LLC converter. Analysis and experimental results verify that a smaller operating frequency range and volume of the transformers, a soft-switching condition, and a higher overall efficiency are achieved with the proposed converter

Two-Stage Battery Energy Storage System (BESS) in AC Microgrids with Balanced State-of-Charge and Guaranteed Small-Signal Stability

In this paper, a two-stage battery energy storage system (BESS) is implemented to enhance the operation condition of conventional battery storage systems in a microgrid. Particularly, the designed BESS is composed of two stages, i.e., Stage I: integration of dispersed energy storage units (ESUs) using parallel DC/DC converters, and Stage II: aggregated ESUs in grid-connected operation. Different from a conventional BESS consisting of a battery management system (BMS) and power conditioning system (PCS), the developed two-stage architecture enables additional operation and control flexibility in balancing the state-of-charge (SoC) of each ESU and ensures the guaranteed small-signal stability, especially in extremely weak grid conditions. The above benefits are achieved by separating the control functions between the two stages. In Stage I, a localized power sharing scheme based on the SoC of each particular ESU is developed to manage the SoC and avoid over-charge or over-discharge issues; on the other hand, in Stage II, an additional virtual impedance loop is implemented in the grid-interactive DC/AC inverters to enhance the stability margin with multiple parallel-connected inverters integrating at the point of common coupling (PCC) simultaneously. A simulation model based on MATLAB/Simulink is established, and simulation results verify the effectiveness of the proposed BESS architecture and the corresponding control diagram

Bilayer-Skyrmion based design of Neuron and Synapse for Spiking Neural Network

Magnetic skyrmion technology is promising for the next-generation spintronics-based memory and neuromorphic computing due to their small size, non-volatility and low depinning current density. However, the Magnus force originating from the skyrmion Hall effect causes the skyrmion to move along a curved trajectory, which may lead to the annihilation of the skyrmion in a nanotrack during current-induced skyrmion motion. Consequently, circuits utilizing skyrmionic motion need to be designed to limit the impact of the skyrmion Hall effect. In this work, we propose a design of an artificial neuron, and a synapse using the bilayer device consisting of two antiferromagnetically exchange coupled ferromagnetic layers, which achieves robustness against the skyrmion Hall effect by nullifying the Magnus force. Using micromagnetic simulations, we show that the bilayer device can work as an artificial neuron and also as a synapse by modifying its uniaxial anisotropy. We also demonstrate that our proposed skyrmionic synapse has an intrinsic property of perfectly linear and symmetric weight update, which is highly desirable for the synapse operation. A spiking neural network implemented using our proposed synapse and neuron was simulated and showed to achieve 96.23\% accuracy in performing classification on the MNIST handwritten digit dataset

Enhanced Load Power Sharing Accuracy in Droop-Controlled DC Microgrids with Both Mesh and Radial Configurations

The rational power sharing among different interface converters should be determined by the converter capacity. In order to guarantee that each converter operates at the ideal condition, considering the radial and mesh configuration, a modified strategy for load power sharing accuracy enhancement in droop-controlled DC microgrid is proposed in this paper. Two compensating terms which include averaging output power control and averaging DC voltage control of neighboring converters are employed. Since only the information of the neighboring converter is used, the complexity of the communication network can be reduced. The rational distribution of load power for different line resistance conditions is realized by using modified droop control that can be regarded as a distributed approach. Low bandwidth communication is used for exchanging sampled information between different converters. The feasibility and effectiveness of the proposed method for different network configurations and line resistances under different communication delay is analyzed in detail. Simulation results derived from a DC microgrid with three converters is implemented in MATLAB/Simulink to verify the proposed approach. Experimental results from a 3 × 10 kW prototype also show the performance of the proposed modified droop control scheme

Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control

In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems