Road Bank Angle Estimation for Three Wheel Tilting Vehicle Using Multi Model Estimator

In recent years, the need for micro-mobility, especially three-wheel vehicles, is increasing to address pollution and traffic congestion problems. With regard to the development of tilting vehicles, the precise tilt angle is important information in the tilting mechanism. Since the road environment affects the vehicle tilt angle, the road bank and hill angle have to be estimated to optimize the tilt control system. Furthermore, a new tilt mechanism is required due to the energy consumption of the previous active tilting mechanism. This paper introduces the road state observer. In this paper, the observer that combines a kinematic model with a dynamic model of a three wheel vehicle is proposed. The dynamic model has four states, including lateral velocity, yaw rate, tilt angle, and tilt angle ratio. Similarly, kinematic model has two states, including roll and pitch angles. It is assumed that the data set received from the six-dimensional inertial measurement unit including the vehicle acceleration and angular velocity of all axes is available. To verify the proposed algorithm, simulation verification using Carsim ADAMS and Matlab&Simulink is performed and a discussion of the result is provided. In addition, this paper proposes a semi-active tilt system

Road Bank Angle Estimation for Three Wheel Tilting Vehicle Using Multi Model Estimator

In recent years, the need for micro-mobility, especially three-wheel vehicles, is increasing to address pollution and traffic congestion problems. With regard to the development of tilting vehicles, the precise tilt angle is important information in the tilting mechanism. Since the road environment affects the vehicle tilt angle, the road bank and hill angle have to be estimated to optimize the tilt control system. Furthermore, a new tilt mechanism is required due to the energy consumption of the previous active tilting mechanism. This paper introduces the road state observer. In this paper, the observer that combines a kinematic model with a dynamic model of a three wheel vehicle is proposed. The dynamic model has four states, including lateral velocity, yaw rate, tilt angle, and tilt angle ratio. Similarly, kinematic model has two states, including roll and pitch angles. It is assumed that the data set received from the six-dimensional inertial measurement unit including the vehicle acceleration and angular velocity of all axes is available. To verify the proposed algorithm, simulation verification using Carsim ADAMS and Matlab&Simulink is performed and a discussion of the result is provided. In addition, this paper proposes a semi-active tilt system

Detection Method and Removing Filter for Corner Outliers in Highly Compressed Video

Abstract. We propose a detection method and a removal filter for corner outliers in order to improve visual quality in highly compressed video. Corner outliers are detected using the direction of edge going through a block-corner and the properties of blocks around the block-corner. The proposed filter for removing corner outliers, which compensates the stair-shaped discontinuities around edges using the adjacent pixels, is applied to the detected area. Simulation results show that the proposed method improves, particularly in combination with deblocking filters, the visual quality remarkably. Keywords: corner outlier, low bit-rate video, block-based coding, MPEG-4 video

Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test

With the technological advances led by the fourth industrial revolution, automation has been implemented in road earthworks and paving in the road construction sector. For preparation of construction works, achieving an optimal degree of compaction of the subgrade soil is one of the key factors required for automation of construction and digitalization of quality control. The degree of compaction is greatly affected by water content in geotechnical aspects, and measurement of water content is a necessary process in construction sites. However, conventional methods of water content measurement have limitations and drawbacks and have low efficiency considering the recent trend of construction automation and digitalization of quality control. Therefore, in this study, hyperspectral remote sensing was applied for efficient large-scale measurement of water content over a wide area. To this end, first, through laboratory tests, soil water content was normalized with spectral information. A spectrum was derived with a varying water content using standard sand, and reflectance was obtained for specific ranges of wavelength. Finally, we obtained the relationship between the reflectance and the water content by comparing with various fitting models. At this time, the ranges of wavelength to be used in the equation were specified and presented in an exponential model

Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test

With the technological advances led by the fourth industrial revolution, automation has been implemented in road earthworks and paving in the road construction sector. For preparation of construction works, achieving an optimal degree of compaction of the subgrade soil is one of the key factors required for automation of construction and digitalization of quality control. The degree of compaction is greatly affected by water content in geotechnical aspects, and measurement of water content is a necessary process in construction sites. However, conventional methods of water content measurement have limitations and drawbacks and have low efficiency considering the recent trend of construction automation and digitalization of quality control. Therefore, in this study, hyperspectral remote sensing was applied for efficient large-scale measurement of water content over a wide area. To this end, first, through laboratory tests, soil water content was normalized with spectral information. A spectrum was derived with a varying water content using standard sand, and reflectance was obtained for specific ranges of wavelength. Finally, we obtained the relationship between the reflectance and the water content by comparing with various fitting models. At this time, the ranges of wavelength to be used in the equation were specified and presented in an exponential model

Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms

Abstract This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks’ gestation), E1 (11–13 weeks’ gestation) and M1 (14–24 weeks’ gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model