Analysis of microalgal density estimation by using lasso and image texture features
- Nguyen, Linh, Nguyen, Dung, Nguyen, Thang, Nguyen, Binh, Nghiem, Truong
- Authors: Nguyen, Linh , Nguyen, Dung , Nguyen, Thang , Nguyen, Binh , Nghiem, Truong
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 5 (2023), p.
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- Description: Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively © 2023 by the authors.
- Authors: Nguyen, Linh , Nguyen, Dung , Nguyen, Thang , Nguyen, Binh , Nghiem, Truong
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 5 (2023), p.
- Full Text:
- Reviewed:
- Description: Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively © 2023 by the authors.
Real-time distributed trajectory planning for mobile robots
- Nguyen, Binh, Nghiem, Truong, Nguyen, Linh, Nguyen, Anh, Nguyen, Thang
- Authors: Nguyen, Binh , Nghiem, Truong , Nguyen, Linh , Nguyen, Anh , Nguyen, Thang
- Date: 2023
- Type: Text , Conference paper
- Relation: 22nd IFAC World Congress Vol. 56, p. 2152-2157
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- Description: Efficiently planning trajectories for nonholonomic mobile robots in formation tracking is a fundamental yet challenging problem. Nonholonomic constraints, complexity in collision avoidance, and limited computing resources prevent the robots from being practically deployed in realistic applications. This paper addresses these difficulties by modeling each mobile platform as a nonholonomic motion and formulating trajectory planning as an optimization problem using model predictive control (MPC). That is, the optimization problem is subject to both nonholonomic motions and collision avoidance. To reduce computing costs in real time, the nonholonomic constraints are convexified by finding the closest nominal points to the nonholonomic motion, which are then incorporated into a convex optimization problem. Additionally, the predicted values from the previous MPC step are utilized to form linear avoidance conditions for the next step, preventing collisions among robots. The formulated optimization problem is solved by the alternating direction method of multiplier (ADMM) in a distributed manner, which makes the proposed trajectory planning method scalable. More importantly, the convergence of the proposed planning algorithm is theoretically proved while its effectiveness is validated in a synthetic environment. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
- Authors: Nguyen, Binh , Nghiem, Truong , Nguyen, Linh , Nguyen, Anh , Nguyen, Thang
- Date: 2023
- Type: Text , Conference paper
- Relation: 22nd IFAC World Congress Vol. 56, p. 2152-2157
- Full Text:
- Reviewed:
- Description: Efficiently planning trajectories for nonholonomic mobile robots in formation tracking is a fundamental yet challenging problem. Nonholonomic constraints, complexity in collision avoidance, and limited computing resources prevent the robots from being practically deployed in realistic applications. This paper addresses these difficulties by modeling each mobile platform as a nonholonomic motion and formulating trajectory planning as an optimization problem using model predictive control (MPC). That is, the optimization problem is subject to both nonholonomic motions and collision avoidance. To reduce computing costs in real time, the nonholonomic constraints are convexified by finding the closest nominal points to the nonholonomic motion, which are then incorporated into a convex optimization problem. Additionally, the predicted values from the previous MPC step are utilized to form linear avoidance conditions for the next step, preventing collisions among robots. The formulated optimization problem is solved by the alternating direction method of multiplier (ADMM) in a distributed manner, which makes the proposed trajectory planning method scalable. More importantly, the convergence of the proposed planning algorithm is theoretically proved while its effectiveness is validated in a synthetic environment. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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