Abstract: An anti-rollover apparatus and control method for heavy-duty vehicles with a pneumatic brake system includes an anti-yaw module, an anti-roll module, an electronic control unit (ECU) (10), a yaw velocity sensor (12), and a vehicle roll angle sensor (18). The ECU (10) controls solenoid valves (4, 9, 11, 19, and 24) to achieve braking of part of wheels to obtain anti-yaw torques and improve the yaw stability of the heavy-duty vehicles. The ECU (10) controls gas switch valves (21 and 22) to spray high-pressure gases recovered in brake chambers (1, 13, 16, and 26) out, anti-roll torques are obtained through the jet reactive force, and the roll stability of the heavy-duty vehicles is improved.

Abstract: An anti-rollover apparatus and control method for heavy-duty vehicles with a pneumatic brake system includes an anti-yaw module, an anti-roll module, an electronic control unit (ECU) (10), a yaw velocity sensor (12), and a vehicle roll angle sensor (18). The ECU (10) controls solenoid valves (4, 9, 11, 19, and 24) to achieve braking of part of wheels to obtain anti-yaw torques and improve the yaw stability of the heavy-duty vehicles. The ECU (10) controls gas switch valves (21 and 22) to spray high-pressure gases recovered in brake chambers (1, 13, 16, and 26) out, anti-roll torques are obtained through the jet reactive force, and the roll stability of the heavy-duty vehicles is improved.

Abstract: An information processing method and apparatus, where the method includes: obtaining driving data of a target vehicle (210); and determining an actual vehicle model of the target vehicle based on the driving data (220). According to the vehicle information processing method and apparatus in the embodiments of this application, an actual vehicle model of a vehicle can be identified with relatively high accuracy.

Abstract: A method for live migration of a virtual machine includes receiving a data packet that is sent to a migrated virtual machine on the source physical machine in a stage when the migrated virtual machine is suspended, and caching the received data packet; and sending the cached data packet to the migrated virtual machine on the destination physical machine after it is sensed that the migrated virtual machine is restored at the destination, to speed up restoration of a TCP connection inside the virtual machine. The apparatus of the present disclosure includes a caching unit and a data restoration unit. The method and apparatus of the present disclosure improve a restoration speed of the TCP connection, make live migration of a virtual machine more imperceptible for users, and improve user experience.

Abstract: A method for live migration of a virtual machine includes receiving a data packet that is sent to a migrated virtual machine on the source physical machine in a stage when the migrated virtual machine is suspended, and caching the received data packet; and sending the cached data packet to the migrated virtual machine on the destination physical machine after it is sensed that the migrated virtual machine is restored at the destination, to speed up restoration of a TCP connection inside the virtual machine. The apparatus of the present disclosure includes a caching unit and a data restoration unit. The method and apparatus of the present disclosure improve a restoration speed of the TCP connection, make live migration of a virtual machine more imperceptible for users, and improve user experience.

Abstract: A system and method for analyzing social media data by obtaining social media data from a social media platform, where the social media data includes documents from multiple users of the social media platform; classifying the documents using a sentiment classifier; tokenizing the documents into terms; associating a sentiment with each term; detecting a first event based on a number of occurrences of a first term in the documents; and providing information associated with the event to a user, where the information includes the first term and a sentiment associated with the first term.

Abstract: A system and method for analyzing social media data by obtaining social media data from a social media platform, where the social media data includes documents from multiple users of the social media platform; classifying the documents using a sentiment classifier; tokenizing the documents into terms; associating a sentiment with each term; detecting a first event based on a number of occurrences of a first term in the documents; and providing information associated with the event to a user, where the information includes the first term and a sentiment associated with the first term.

Abstract: Methods, systems and processor-readable media for simultaneous sentiment analysis and topic classification with multiple labels. A sentiment and topic associated with a post can be classified at similar time and a result can be incorporated to predict a feature so that a label of two (or more) tasks can promote and reinforce each other iteratively. A feature extraction and selection can be performed on the tasks and a multi-task multi-label classification model can be trained for each task with maximum entropy utilizing multiple labels to ascertain information derived from an extra label and to manage class ambiguities. Each task has a separate classification model with different predicting features and they can be trained collectively which allows flexibility in model construction. The multi-task multi-label classification model produces a probabilistic result and the classes can be ranked by the probabilistic result and the post can be classified with the multi-label.