Global Optimization of the Hydraulic-Electromagnetic Energy-Harvesting Shock Absorber for Road Vehicles With Human-Knowledge-Integrated Particle Swarm Optimization Scheme

Zhou, Quan; Guo, Sijing; Xu, Lin; Guo, Xuexun; Williams, Howard; Xu, Hongming; Yan, Fuwu

doi:10.1109/tmech.2021.3055815

Cited by 27 publications

(17 citation statements)

References 49 publications

(51 reference statements)

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“…There have been many studies into disruptions of URNs, such as transit strikes [38][39][40], bridge closure/collapse [41][42][43], special events [44,45] and earthquakes [46][47][48]. Broadly, these disruptions can be categorized as expected or unexpected in nature [49,50], and as occurring in a regular, predictable way, or not [11].…”

Section: Classification Of Disruptionsmentioning

confidence: 99%

Benchmark Analysis for Robustness of Multi-Scale Urban Road Networks Under Global Disruptions

Shang

Gao

Daina

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful -if partialrepresentation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multiscale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI's ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs.

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Section: Classification Of Disruptionsmentioning

confidence: 99%

Benchmark Analysis for Robustness of Multi-Scale Urban Road Networks Under Global Disruptions

Shang

Gao

Daina

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…where σ 0 is the initial variation value; σ is the variance decay rate; and E is the number of epis ParaFirstLine-Indodes that the learning agent experienced in online learning. Once the power demand signal is regulated by (16), the control command is represented by…”

Section: B Ddpg Network For Adaptive Knowledge Transfermentioning

confidence: 99%

“…This will be achieved by finding the optimal parameters in power management control models that achieve the minimum mean square error (MSE) with the DP results. Meta-heuristic algorithms, e.g., particle swarm optimization (PSO) [13]- [16] and genetic algorithms (GAs) [17], [18], have been developed to minimize the MSEs between model data and testing data. The learning performance heavily depends on the data used in training and validation.…”

mentioning

confidence: 99%

Knowledge Implementation and Transfer With an Adaptive Learning Network for Real-Time Power Management of the Plug-in Hybrid Vehicle

Zhou

Zhao

Shuai

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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Essential decision-making tasks such as power management in future vehicles will benefit from the development of artificial intelligence technology for safe and energy-efficient operations. To develop the technique of using neural network and deep learning in energy management of the plug-in hybrid vehicle and evaluate its advantage, this article proposes a new adaptive learning network that incorporates a deep deterministic policy gradient (DDPG) network with an adaptive neuro-fuzzy inference system (ANFIS) network. First, the ANFIS network is built using a new global K-fold fuzzy learning (GKFL) method for real-time implementation of the offline dynamic programming result. Then, the DDPG network is developed to regulate the input of the ANFIS network with the real-world reinforcement signal. The ANFIS and DDPG networks are integrated to maximize the control utility (CU), which is a function of the vehicle's energy efficiency and the battery state-of-charge. Experimental studies are conducted to testify the performance and robustness of the DDPG-ANFIS network. It has shown that the studied vehicle with the DDPG-ANFIS network achieves 8% higher CU than using the MATLAB ANFIS toolbox on the studied vehicle. In five simulated real-world driving conditions, the DDPG-ANFIS network increased the maximum mean CU value by 138% over the ANFIS-only network and 5% over the DDPG-only network.

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“…Optimization algorithms are classified into global and local optimization techniques according to the required solution level. The global optimization technique [21] aims at finding the best solution in the entire search area even though it takes time to process, whereas the local optimization technique [22] aims at finding the best solution in a partial search area within a short time. There are various methodologies for optimization techniques, and the representative methods are genetic algorithm (GA) and particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

A Design and Optimization of a CGK-Based Fuzzy Granular Model Based on the Generation of Rational Information Granules

Yeom

Kwak

2022

Applied Sciences

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This study proposes an optimized context-based Gustafson Kessel (CGK)-based fuzzy granular model based on the generation of rational information granules and an optimized CGK-based fuzzy granular model with the aggregated structure. The conventional context-based fuzzy-c-means (CFCM) clustering generates clusters considering the input and output spaces. However, the prediction performance decreases when the specific data points with geometric features are used. The CGK clustering solves the above situation by generating valid clusters considering the geometric attributes of data in input and output spaces with the aid of the Mahalanobis distance. However, it is necessary to generate rational information granules (IGs) because there is a significant change in performance according to the context generated in the output space and the shape, size, and several clusters generated in the input space. As a result, the rational IGs are obtained by considering the relationship between the coverage and specificity of IG using the genetic algorithm (GA). Thus, the optimized CGK-based fuzzy granular models with the aggregated structure are designed based on rational IGs. The prediction performance was compared using the two databases to verify the validity of the proposed method. Finally, the experiments revealed that the performance of the proposed method is higher than that of the previous model.

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Global Optimization of the Hydraulic-Electromagnetic Energy-Harvesting Shock Absorber for Road Vehicles With Human-Knowledge-Integrated Particle Swarm Optimization Scheme

Cited by 27 publications

References 49 publications

Benchmark Analysis for Robustness of Multi-Scale Urban Road Networks Under Global Disruptions

Benchmark Analysis for Robustness of Multi-Scale Urban Road Networks Under Global Disruptions

Knowledge Implementation and Transfer With an Adaptive Learning Network for Real-Time Power Management of the Plug-in Hybrid Vehicle

A Design and Optimization of a CGK-Based Fuzzy Granular Model Based on the Generation of Rational Information Granules

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