73 research outputs found
Human Factors Design Research with Persona for Kids Furniture in Shanghai Middle-Class Family
No full textResearch on the Furniture Design Criteria for Children’s Psychological Development in Home Environment
No full textThe Research on Customers' satisfaction on Rough Set in Vegetable Quality Tracking System and its Influencing Factors
No full textAesthetic study of patterns based on event‐related potential and eye‐tracking: Taking the decorative patterns of the Tang dynasty as an example
No full textFractal Analysis on the Mapping Relationship of Conductivity Properties in Porous Material
No full textThe mapping relationships between the conductivity properties are not only of great importance for understanding the transport phenomenon in porous material, but also benefit the prediction of transport parameters. Therefore, a fractal pore-scale model with capillary bundle is applied to study the fluid flow and heat conduction as well as gas diffusion through saturated porous material, and calculate the conductivity properties including effective permeability, thermal conductivity and diffusion coefficient. The results clearly show that the correlations between the conductivity properties of saturated porous material are prominent and depend on the way the pore structure changes. By comparing with available experimental results and 2D numerical simulation on Sierpinski carpet models, the proposed mapping relationships among transport properties are validated. The present mapping method provides a new window for understanding the transport processes through porous material, and sheds light on oil and gas resources, energy storage, carbon dioxide sequestration and storage as well as fuel cell etc
Multi-Time Interval Forecasting-Aided State Estimation Incorporating Phasor Measurements for Power Systems with Renewable Energy Sources
No full textTo achieve more precise monitoring of state fluctuations in the power network close to renewable energy sources, it is necessary to utilize phasor measurements and shorten the time interval between state estimations. For large-scale power systems, however, estimating all of their states with shorter time intervals means a drastic increase in computational burden. As a tradeoff between accuracy and computational efficiency, a multi-time interval forecasting-aided state estimation approach is proposed in this paper, where states with various degrees of fluctuations are estimated asynchronously with different time intervals. Based on the newest state estimate, forecasting-aided state estimators are employed to predict states at time moments prior to the next round of measurement update and state estimation. Extensive numerical tests have demonstrated the effectiveness of the proposed approach
SASRM: A Semantic and Attention Spatio-temporal Recurrent Model for Next Location Prediction
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