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102 results about "Heavy goods vehicle" patented technology

A heavy goods vehicle (HGV), also large goods vehicle (LGV) or medium goods vehicle, is the European Union (EU) term for any truck with a gross combination mass (GCM) of over 3,500 kilograms (7,716 lb). Sub-category N2 is used for vehicles between 3,500 kilograms (7,716 lb) and 12,000 kilograms (26,455 lb) and N3 for all goods vehicles over 12,000 kilograms (26,455 lb) as defined in Directive 2001/116/EC. The term medium goods vehicle is used within parts of the UK government to refer to goods vehicles of between 3.5 and 7.5 tonnes which according to the EU are also "large goods vehicles".

Heavy goods vehicle ramp running safety monitoring system

The invention discloses a heavy goods vehicle ramp running safety monitoring system which comprises a signal acquisition module, a signal processing module, a data communication module, a singlechip microcomputer and a man-machine interaction intelligent instrument, wherein the signal acquisition module is used for converting an acquired signal into a standard voltage signal or TTL (Time To Live) pulse signal which can be recognized by the singlechip microcomputer and is connected with the singlechip microcomputer by virtue of the signal processing module and the data communication module; the singlechip microcomputer is used for transferring the signal processed by the singlechip microcomputer to the man-machine interaction intelligent instrument by virtue of a CAN (Controller Area Network) communication module; the man-machine interaction intelligent instrument is used for displaying the monitoring data and an alarm signal to a driver. The heavy goods vehicle ramp running safety monitoring system can be used for effectively monitoring the safety parameters and alarming in real time in a running process, realizing intelligentization and visualization of a heavy goods vehicle running process, meeting the requirements on the running safety and stability and effectively improving the heavy goods vehicle ramp running safety.
Owner:CHANGAN UNIV

Flexible semicrystalline polyamides

Composition comprising, by weight, the total being 100 %: 50 to 100 % of at least one polyamide A1 of formula X.Y/Z or 6.Y2/Z in which: X denotes the residues of an aliphatic diamine having from 6 to 10 carbon atoms, Y denotes the residues of an aliphatic dicarboxylic acid having from 10 to 14 carbon atoms, Y2 denotes the residues of an aliphatic dicarboxylic acid having from 15 to 20 carbon atoms and Z denotes at least one unit chosen from the residues of a lactam, the residues of an a,-aminocarboxylic acid, the unit X1, Y1 in which X1 denotes the residues of an aliphatic diamine and Y1 denotes the residues of an aliphatic dicarboxylic acid, the weight ratios Z/(X+Y+Z) and Z/(6+Y2+Z) being between 0 and 15 %; 0 to 40 % of a plasticizer; 0 to 50 % of an impact modifier; and 0 to 50 % of a polyamide A2. The invention also relates to structures comprising a layer consisting of the above composition. This structure is useful for making devices for storing or transferring fluids, in particular in motor vehicles and heavy goods vehicles. The fluids may for example be petrol, diesel, hydraulic brake fluid, compressed air for the brake circuits of heavy goods vehicles, and hydraulic clutch fluid. The invention also relates to these devices. Such devices may be tanks, hoses, pipes or containers. These structures may include other layers consisting of other materials.
Owner:ARKEMA FRANCE SA

Road network heavy truck traffic flow prediction method based on data quality control

The invention relates to a road network heavy truck traffic flow prediction method based on data quality control, which is characterized in that two data sources of GPS flow data and toll station monitoring data are divided into three types of data road sections, and different prediction methods are respectively adopted. For a road section with GPS flow data, due to the fact that the GPS data arenot completely obtained, a method that sample expansion is conducted through a piecewise constant coefficient method and then long-short-term recurrent neural network prediction is conducted is adopted. And for a data-free road section without GPS data, a K nearest neighbor algorithm is adopted to predict the flow. For a toll station monitoring data road section, due to comprehensive data samples,a long-short-term recurrent neural network algorithm is directly adopted for prediction. According to the method, starting from the actual engineering problem faced by flow prediction, multiple datasource characteristics are analyzed to improve the data quality, then the road network heavy truck flow measurement and calculation method is established, and finally the road network heavy truck flowbased on data quality control is formed.
Owner:BEIJING JIAOTONG UNIV
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