Image-Based Approach for Road Profile Analyses

Han, Jen-Yu; Chen, Ai-Chin; Lin, Yan-Ting

doi:10.1061/(asce)su.1943-5428.0000160

Cited by 17 publications

(5 citation statements)

References 11 publications

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“…However, it is still necessary to consider the collection of large samples is time-consuming and labor-intensive work, and the heritability obtained through GWAS research. Therefore, in addition to increasing the sample size, new methods are needed to discover more genetic mechanisms behind mental illness [ 13 ].…”

Section: Neural Network and Imaging Geneticsmentioning

confidence: 99%

Image Genetic Analysis and Application Research Based on QRFPR and Other Neural Network-Related SNP Loci

Liu

Zeng

Quan

2022

BioMed Research International

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The development of neuroimaging technology and molecular genetics has produced a large amount of imaging genetics data, which has greatly promoted the study of complex mental diseases. However, because the feature dimension of the data is too high, the correlation measure assumes that the data obey Gaussian distribution, and traditional algorithms often cannot explain these two types of data well. This article mainly studies image genetics analysis and its application based on neural network. In this paper, based on the theory and application technology of neural network, the tree structure is established by prior knowledge, that is, each SNP site is used as a leaf node of the tree, and the LD block and genome formed by the linkage imbalance of multiple SNP sites are used as intermediate nodes. Then, the hierarchical relationship of features was introduced. On this basis, a sparse learning method based on tree structure guidance is used to select features from multiple features of multiple SNPs locus regression candidate brain regions. Finally, the identification of SNPs in feature selection is used to predict quantitative traits of brain regions. The distribution of the typical vector values obtained by the algorithm in the experimental data is basically consistent with the distribution of the median of the actual data, and the correlation coefficient obtained is closest to the actual correlation coefficient in the data set. The average correlation coefficient of the algorithm reaches 82.3%, which is about 4.2% higher than the control algorithm. Experimental results show that this method can not only significantly improve the regression performance but also detect the risk gene SNPs loci with spatial clustering features and functional interpretation significance. It is practical and effective to use it in clinical trials.

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Section: Neural Network and Imaging Geneticsmentioning

confidence: 99%

Image Genetic Analysis and Application Research Based on QRFPR and Other Neural Network-Related SNP Loci

Liu

Zeng

Quan

2022

BioMed Research International

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“…For this purpose, it is necessary to foreknowledge the road profile and the suspension system's internal state variables. Means of direct measurement are available (e.g., road profile [5], [6], sprung mass displacement [7], unsprung mass displacement [8]), but limitations are apparent as they cannot be practically implemented. Therefore, it is necessary to estimate the desired variables using measurable sensor signals by utilizing observer systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

Kim

Lee

et al. 2021

IEEE Access

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The vehicle suspension control unit serves as a critical component to the vehicle system, as it ensures the steering stability and sound ride quality of the vehicle. To effectively realize control strategies, it is essential to foreknowledge the road profile and the suspension system's internal state variables. While the mentioned variables are not practically measurable using commercial sensors, it is necessary to estimate the desired variables by utilizing observer systems. Conventional means have mainly employed modelbased approaches, in which model uncertainties and high computational cost pose limitations for practical implementation. Herein, we propose a data-driven deep learning method as an alternative because no explicit physical modeling is required, and evaluation is computationally cheap. We first propose a novel encoderdecoder structured recurrent neural network model with a two-phase attention mechanism to estimate the unknown road profile and four state variables of the vehicle suspension system. Based on a simulated data set, we assess the proposed model's qualitative and quantitative results and demonstrate that our model can achieve highly accurate estimation results with fast computation time. Besides, we validate our black-box model's reliability by comparing its interpretation with the suspension system's actual physical characteristics. Furthermore, we compare the proposed model with existing baseline methods, and the results show that our proposed deep learning model significantly outperforms the baseline. Lastly, we experiment with our network's autoregressive capability and demonstrate the feasibility of estimating a sequence of future values, which has not been presented in previous works.INDEX TERMS Deep learning, long short term memory (LSTM), observer systems, recurrent neural networks (RNNs), vehicle suspension control.

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“…13 Therefore, it is essential to measure or estimate road roughness through sensors or some estimation algorithms for designing the suspension control system. Road roughness can be directly measured by a distance sensor, 14 camera, 15 and the light detection and range system. 16 However, these methods are not suitable for mass-produced commercial vehicles because these sensors are expensive or the signal-processing algorithm is very complex.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter

Kim

Kang

Kim

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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This study presents an improved discrete Kalman filter for simultaneously estimating both all state variables and the unknown road roughness input for a vehicle suspension control system that plays a key role in the ride quality and handling performance while driving the vehicle. The suspension system is influenced by the road roughness input, which causes undesirable vibrations associated with vehicle instability. It is therefore important to estimate the road roughness and state variables information when designing the model-based controller for the vehicle suspension control system associated with the vehicle’s vertical dynamics. However, the implementation of conventional estimation theories for the suspension control system is challenging because the road roughness acts as an unknown input and is difficult to be measured or estimated while driving. This study presents an improved Kalman filter with unknown input, which can simultaneously estimate the state variables and road roughness without any prior information about the vehicle suspension control system. The proposed road roughness input estimator is evaluated by using an in-vehicle test bed with a laser-type profilometer. Finally, the state estimation performance of the proposed estimator for a vehicle suspension control system is validated by using CarSim software.

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Image-Based Approach for Road Profile Analyses

Cited by 17 publications

References 11 publications

Image Genetic Analysis and Application Research Based on QRFPR and Other Neural Network-Related SNP Loci

Image Genetic Analysis and Application Research Based on QRFPR and Other Neural Network-Related SNP Loci

Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter

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