Blockchain technology has a wide range of applications in the fields of finance, credit reporting and intellectual property, etc. As the core of blockchain, consensus algorithm affects the security and performance of blockchain system directly. In the past 10 years, there have been about 30 consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Ripple Protocol Consensus Algorithm (RPCA) and AlgoRand. But their security, stability and operating efficiency still lag far behind our actual needs. This paper introduces the computing power competition of PoW into DPoS to design an improved consensus algorithm named Delegated Proof of Stake with Downgrade (DDPoS). Through the further modification, the impact of both computing resources and stakes on generating blocks is reduced to achieve higher efficiency, fairness, and decentralization in consensus process. Then a downgrade mechanism is proposed to quickly replace the malicious nodes to improve the security. The simulation experiments in blockchain system show that the proposed consensus algorithm is significantly more efficient than PoW and PoS, but slightly lower than DPoS. However, its degree of centralization remains far below that of DPoS. And through the downgrade mechanism, the proposed consensus algorithm can detect and downgrade the malicious nodes timely to ensure the security and good operation of system. INDEX TERMS Blockchain, consensus algorithm, delegated proof of stake with downgrade, downgrade mechanism, efficiency, fairness, decentralization.
Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.
The amount of information increases explosively in Internet of Things, because more and more data are sensed by large amount of sensors. The explosive growth of information makes it difficult to access information efficiently, so it is an effective method to decrease the amount of information to be transferred on network by text classification. This paper proposes a new text classification algorithm based on vector space model. This algorithm improves the feature selection and weighting methods by introducing synonym replacement to traditional text classification algorithms. The experimental results show that the proposed classification algorithm has considerably improved the precision and recall of classification.
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