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As a crucial part of the intelligent transportation system, traffic signal control will realize the boundary control of the traffic area, it will also lead to delays and excessive fuel consumption when the vehicle is driving at the intersection. To tackle this challenge, this research provides an optimized control framework based on reinforcement learning method and speed guidance strategy for the connected vehicle network. Prior to entering an intersection, vehicles are focused on in a specific speed guidance area, and important factors like uniform speed, acceleration, deceleration, and parking are optimized. Conclusion, derived from deep reinforcement learning algorithm, the summation of the length of the vehicle’s queue in front of the signal light and the sum of the number of brakes are used as the reward function, and the vehicle information at the intersection is collected in real time through the road detector on the road network.

Super-knock has been a significant obstacle for the development of highly turbocharged (downsized) gasoline engines with spark ignition, due to the catastrophic damage super-knock can cause to the engine. According to previous research by the authors, one combustion process leading to super-knock may be described as hot-spot induced pre-ignition followed by deflagration which can induce detonation from another hot spot followed by high pressure oscillation. The sources of the hot spots which lead to pre-ignition (including oil films, deposits, gas-dynamics, etc.) may occur sporadically, which leads to super-knock occurring randomly at practical engine operating conditions. In this study, a spark plasma was used to induce preignition and the correlation between super-knock combustion and the thermodynamic state of the reactant mixture was investigated in a four-cylinder production gasoline engine.