Real-time hierarchical risk assessment for UAVs based on recurrent fusion autoencoder and dynamic FCE: A hybrid framework

Su, Xuanyuan; Tao, Laifa; Liu, Hongmei; Wang, Lizhi; Suo, Mingliang

doi:10.1016/j.asoc.2021.107286

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…This elevator group consists of six Elevator-ABCDEF; they work in coordination with each other. This example simulates a 60-story building where users randomly booking elevators [43][44][45]. There will be a waste of power and too long of a waiting time for users at both ends floor when the operating cycle of all elevators are set 1st to 60th floors.…”

Section: Visualization Of Numerical Resultsmentioning

confidence: 99%

A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination

Chen

Zheng

Liu³

et al. 2021

Electronics

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Elevators are an essential indoor transportation tool in high-rise buildings. The world is advocating the design concept of safety, energy-saving, and intelligence. We focus on improving operation speed and utilization efficiency of the elevator group. This paper proposed a real-time reservation elevator groups optimization algorithm, and a dynamic matrix iterative model has been established. The indoor navigation technology UWB is applied, which can help users to quickly find elevators. The manned equilibrium efficiency and running time equilibrium efficiency of elevator group are given. Moreover, the data filtering criterion formulas for user waiting time and elevator remaining space are defined. In this paper, three numerical examples are given. Example 1 is a single elevator in n-storey building. Example 2 is compared with different scheduling algorithms, such as FCFS, SSTF, LOOK, and SCAN algorithms, and the results show that our method has the advantages of short total running time and less round-trip frequency. At last, the matrix of numerical iteration results are visualized, and the data movement status of people on each floor can be observed. Example 3 introduced elevator group algorithms. For high-rise buildings, this paper adopts a high, medium, and low hierarchical management model; this model has high coordination, as well as fast response, batch process, and adaptive function. Finally, we also discussed and compared the complexity of single elevator and elevator group algorithms. Therefore, this method has great development potential and practical application value, which deserves further study.

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Section: Visualization Of Numerical Resultsmentioning

confidence: 99%

A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination

Chen

Zheng

Liu³

et al. 2021

Electronics

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show abstract

“…Fuzzy comprehensive evaluation (FCE) can reasonably combine these attributes and factors with items affected by multiple attributes or their overall strengths or weaknesses [43,48]. Therefore, the FCE method based on a fuzzy set evaluates subordinate-level status evaluation from multiple indicators [41].…”

Section: Principle Of Fuzzy Comprehensive Evaluation(fce) Methodsmentioning

confidence: 99%

“…Due to the large number of experts, the average value was taken as the final evaluation result of a factor. Thus, fuzzy relation R from U to V could be determined by all single factor evaluation results [44,48].…”

Section: Principle Of Fuzzy Comprehensive Evaluation(fce) Methodsmentioning

confidence: 99%

“…This can be used as fuzzy comprehensive evaluation vector after the normalization of B obtained by the operation, where A is the index weight. The FCE result of HRSs is then obtained [44,48].…”

Section: Principle Of Fuzzy Comprehensive Evaluation(fce) Methodsmentioning

confidence: 99%

“…The FCE method can solve these problems well [45][46][47]. However, the calculation process of FCE is more complicated, and implementation is easily influenced by subjective factors [48]. Using fuzzy control theory to quantify unquantifiable factors, and using ANN to analyze experimental data, error is small, there is high precision, and complex forecasting problems have good generalization ability [35,36].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

Zhou

Qin

et al. 2022

Energies

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With the gradual popularization of hydrogen fuel cell vehicles (HFCVs), the construction and planning of hydrogen refueling stations (HRSs) are increasingly important. Taking operational HRSs in China’s coastal and major cities as examples, we consider the main factors affecting the site selection of HRSs in China from the three aspects of economy, technology and society to establish a site selection evaluation system for hydrogen refueling stations and determine the weight of each index through the analytic hierarchy process (AHP). Then, combined with fuzzy comprehensive evaluation (FCE) method and artificial neural network model (ANN), FCE method is used to evaluate HRS in operation in China's coastal areas and major cities, and we used the resulting data obtained from the comprehensive evaluation as the training data to train the neural network. So, an intelligent site selection model for HRSs based on fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) is proposed. The planned HRSs in Shanghai are evaluated, and an optimal site selection of the HRS is obtained. The results show that the optimal HRSs site selected by the FCE-ANN model is consistent with the site selection obtained by the FCE method, and the accuracy of the FCE-ANN model is verified. The findings of this study may provide some guidelines for policy makers in planning the hydrogen refueling stations.

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Multi-source fuzzy comprehensive evaluation

Pedrycz

et al. 2023

Applied Soft Computing

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Real-time hierarchical risk assessment for UAVs based on recurrent fusion autoencoder and dynamic FCE: A hybrid framework

Cited by 11 publications

References 32 publications

A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination

A Real-Time Matrix Iterative Optimization Algorithm of Booking Elevator Group and Numerical Simulation Formed by Multi-Sensor Combination

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

Multi-source fuzzy comprehensive evaluation

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