A novel parking-lot intelligent selection algorithm based on gray relational analysis considering user preferences

Abstract

In order to select and recommend the optimal parking lot for the users, a novel intelligent selection algorithm based on the grey relational analysis considering user preferences is proposed. The parking-lot evaluation index system consisting of indicators in effectiveness, convenience, economy, environmental attribute and agreeableness is firstly established. The comprehensive weight determination method combining the expert estimation method with the user preference settings is proposed to determine the comprehensive weight of each evaluation index, and this method can meet the personalized needs of individual user to the greatest extent. The evaluation indexes considering various representative objective and subjective influential factors including the driving distance between the current location and the target parking lot, the walking distance between the target parking lot and the destination, the remaining parking spaces, the parking fee, the condition of the hardware facilities and service quality are determined, and the corresponding comprehensive weight of each evaluation index is calculated. Then the parking lots near the destination are evaluated by the grey relational analysis in order to select the optimal one to the user for parking reservation. This algorithm has been proven to be effective by the APP on Android mobile phones.

Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

“…Extraction of battery health indicator (HI) using the CALCE of the University of Maryland includes CS2-35, CS2-36, CS2-37, and CS2-38. 28 The reasonableness of HI selection was verified by using gray correlation analysis (GRA) and compared with other network models, 29 and the estimation accuracy of the hybrid neural network model was verified by various evaluation indexes. The main contributions of this paper are as follows.…”