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Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.

With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establish a distributed real time system to process streaming data of the IoT. Two urgent issues have been solved in our study that are (1) multisource heterogeneous sensor data integration and (2) processing streaming sensor data in real time manner with low latency. Furthermore, we set up a real time system to process streaming heterogeneous sensor data from multiple sources with low latency. Our tests are performed using field test data derived from environmental monitoring sensor data collected from indoor environment for system validation. The results show that our proposed system is valid and efficient for multisource heterogeneous sensor data integration and streaming data processing in real time manner.

Taking a hybrid electric vehicle using double-row planetary gear power coupling mechanism as a research object, this study proposes a coordinated control algorithm of “torque distribution, engine torque monitoring, and motor torque compensation” in an attempt to realize coordinated control for driving mode switching. Characteristic analysis of the power coupling mechanism was carried out, and the control strategy model in MATLAB/Simulink was built. Subsequently, the analysis of mode switching from the electric mode into joint driving mode was simulated. In addition, a multibody dynamics model of the power coupling mechanism was established and the simulation analysis during mode switching process was carried out. The results show that the proposed coordinated control strategy serves to effectively reduce the torque fluctuation and the impact degree during the mode switching process and improve the ride comfort of the vehicle. In the meantime, the time-domain and frequency-domain characteristics of gear meshing force and bearing restraint force indicate that the mode switching process of the dynamic coupling mechanism is quite stable and this control strategy contributes to improving the characteristics such as vibration and noise.