Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmark

Contextual features are important data sources for building citywide crowd mobility prediction models. However, the difficulty of applying context lies in the unknown generalizability of contextual features (e.g., weather, holiday, and points of interests) and context modeling techniques across different scenarios. In this paper, we present a unified analytic framework and a large-scale benchmark for evaluating context generalizability. The benchmark includes crowd mobility data, contextual data, and advanced prediction models. We conduct comprehensive experiments in several crowd mobility prediction tasks such as bike flow, metro passenger flow, and electric vehicle charging demand. Our results reveal several important observations: (1) Using more contextual features may not always result in better prediction with existing context modeling techniques; in particular, the combination of holiday and temporal position can provide more generalizable beneficial information than other contextual feature combinations. (2) In context modeling techniques, using a gated unit to incorporate raw contextual features into the deep prediction model has good generalizability. Besides, we offer several suggestions about incorporating contextual factors for building crowd mobility prediction applications. From our findings, we call for future research efforts devoted to developing new context modeling solutions

The Moore-Penrose inverse of 2 x 2 matrices over a certain *-regular ring

In this paper, we study representations of the Moore-Penrose inverse of a 2 x 2 matrix M over a *-regular ring with two term star-cancellation. As applications, some necessary and sufficient conditions for the Moore-Penrose inverse of M to have different types are given.This research is supported by the National Natural Science Foundation of China (11201063) and (11371089), the Specialized Research Fund for the Doctoral Program of Higher Education (20120092110020), the Foundation of Graduate Innovation Program of Jiangsu Province(CXLX13-072) and the Fundamental Research Funds for the Central Universities (22420135011), `FEDER Funds through "Programa Operacional Factores de Competitividade-COMPETE' and the Portuguese Funds through FCT-`Fundação para a Ciência e a Tecnologia', within the project PEst-OE/MAT/UI0013/2014

Active vibration isolation using a six-axis orthogonal vibration isolation platform with piezoelectric actuators

Piezoelectric actuators (PEA) act an important role in active vibration control area due to the advantages of fast response, high output force, small size and light weight. A 6-axis orthogonal vibration isolation platform based on PEAs is designed, which satisfies the demands of heavy payload, small installation space and multi degree of freedom vibration isolation. The dynamic model of the six-axis orthogonal vibration isolation platform with PEAs is established using Newton-Euler method. With the layout of six PEAs around the axis of symmetry, the dynamic equations could be decoupled into two single-input-single-output (SISO) subsystems and two multi-input-multi-output (MIMO) subsystems. Based on the modal superposition method, the two MIMO subsystems are further decoupled. The control strategy for each SISO system is developed with LQR control method. To evaluate the effectiveness of the control method, the simulation and verification experiment are conducted. The simulation result and experimental data indicate that the decoupling control of the proposed six-axis orthogonal vibration isolation platform with piezoelectric actuators effectively reduces the vibration response of payload within the target frequency range of 20 Hz to 200 Hz