Hong Kong Polytechnic University

About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Computer science & Tourism. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.

Bulk Metallic Glass Composites with Transformation‐Mediated Work‐Hardening and Ductility

Abstract: Bulk metallic glasses (BMGs) have shown a unique combination of mechanical, chemical, and physical properties, [ 1–5 ] but their room-temperature brittleness has been the stumbling block for real structural applications. [ 6 , 7 ] To answer this challenge, the concept of developing composite microstructures by combining the glassy matrix with crystalline phases at different length scales has been developed, through which an improvement in tensile ductility has been obtained in several zirconium and titanium-based BMG composites. [ 8–13 ] However, these BMG composites showed a macroscopic strain softening phenomenon with an early onset of necking (i.e., the maximum strength occurs at the yield point) because of a lack of workhardening mechanisms (endows the materials with minute damage tolerance), which would give rise to serious engineering problems therefrom. In this Communication, we report a BMG composite that exhibits large tensile ductility with signifi cant work-hardening capability. Our current fi nding offers a new paradigm for developing BMGs with improved ductility as practical engineering materials. A work-hardening phenomenon in compression has been reported in both BMGs and BMG composites and a few scenarios were proposed for understanding the strain-hardening capability, such as severe lattice distortion in the crystalline phases, pile ups of dislocations close to the interfaces between the reinforced phases and matrix, [ 14 ] atomic-scale inhomogeneity in the amorphous phase, and stress-induced martensitic transformation. [ 15–17 ] To date, however, a working-hardening capability has not been discovered in tension for BMGs or their composites. Referring to the concept of the TRIP (transformationinduced plasticity) steels, [ 18 ] in this study, we attempted to fabricate a BMG composite with isolated spherical crystalline phases that undergo martensitic transformation during tensile deformation. With such a special composite structure, large tensile ductility and signifi cant work-hardening capability could be induced. Figure 1a shows a representative cross section of the current BMG composite, which demonstrates a typical BMG composite microstructure that contains spherical crystalline phases homogeneously embedded in the amorphous matrix.

Supervisory and optimal control of building HVAC systems: A review

Abstract: HVAC systems are the major energy consumers in buildings. Operation and control of HVAC systems have significant impacts on the energy or cost efficiency of buildings besides their designs. Buildings nowadays are mostly equipped with comprehensive building automation systems (BASs) and building energy management control systems (EMCSs) that allow the possibility of enhancing and optimizing the operation and control of HVAC systems. Supervisory and optimal control, which addresses the energy or cost-efficient control of HVAC systems while providing the desired indoor comfort and healthy environment under the dynamic working conditions, is attracting more attention of the building professionals and the society and provides incentives to make more efforts in developing more extensive and robust control methods for HVAC systems. This paper provides a framework for categorizing the main supervisory and optimal control methods and optimization techniques developed and/or utilized in the HVAC field. The applicat...

Client-edge-cloud hierarchical federated learning

Abstract: Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.

Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Abstract: Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.