The energy demand of electric buses (EBs) is a very important parameter that should be considered by transport companies when introducing electric buses into the urban bus fleet. This article proposes a novel deep-learning-based model for predicting energy consumption of an electric bus traveling in an urban area. The model addresses two important issues: accuracy and cost of prediction. The aim of the research was to develop the deep-learning-based prediction model, which requires only the data readily available to bus fleet operators, such as location of the bus stops (coordinates, altitude), route traveled, schedule, travel time between stops, and to find the most suitable type and configuration of neural network to evaluate the model. The developed prediction model was assessed with different types of deep neural networks using real data collected for several bus lines in a medium-sized city in Poland. Conducted research has shown that the deep learning network with autoencoders (DLNA) neural network allows for the most accurate energy consumption estimation of 93%. The proposed model can be used by public transport companies to plan driving schedules and energy management when introducing electric buses.
Electrical activity variations in a circuit are one of the information leakage used in side channel attacks. In this work, we present GF(2 m) multipliers with reduced activity variations for asymmetric cryptography. Useful activity of typical multiplication algorithms is evaluated. The results show strong shapes, which can be used as a small source of information leakage. We propose modified multiplication algorithms and multiplier architectures to reduce useful activity variations during an operation.
Abstract-The paper presents arithmetic level protections for ECC processor against some side channel attacks. The proposed protection is based on random recodings of the secret key in the double base number system (DBNS). DBNS is a highly redundant and sparse number system. Here, the high redundancy level of DBNS is used to randomly modify on-the-fly the ki digits during the scalar multiplication [k]P . The proposed solution leads to random numbers and orders of curve level operations (point addition, doubling and tripling) during the computation of [k]P operations. Our random recoding method provides [k]P computation time comparable to the best w-NAF recoding methods. But standard w-NAF recodings are deterministic ones while our solution is a random one.
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