A Deterministic Affine-Quadratic Optimal Control Problem

A Deterministic affine quadratic optimal control problem is considered. Due to the nature of the problem, optimal controls exist under some very mild conditions. Further, it is shown that under some assumptions, the value function is differentiable and therefore satisfies the corresponding Hamilton-Jacobi-Bellman equation in the classical sense. Moreover, the so-called quasi-Riccati equation is derived and any optimal control admits a state feedback representation.Comment: 30 page

ShipGAN: Generative Adversarial Network based simulation-to-real image translation for ships

Recent advances in robotics and autonomous systems (RAS) have significantly improved the autonomy level of unmanned surface vehicles (USVs) and made them capable of undertaking demanding tasks in various environments. During the operation of USVs, apart from normal situations, it is those unexpected scenes, such as busy waterways or navigation in dust/nighttime, impose most dangers to USVs as these scenes are rarely seen during training. Such a rare occurrence also makes the manual collection and recording of these scenes into dataset difficult, expensive and inefficient, with the majority of existing public available datasets not able to fully cover them. One of many plausible solutions is to purposely generate these data using computer vision techniques with the assistance from high-fidelity simulations that can create various desirable motions/scenarios. However, the stylistic difference between the simulation images and the natural images would cause a domain shift problem. Hence, there is a need for designing a method that can transfer the data distribution and styles of the simulation images into the realistic domain. This paper proposes and evaluates a novel solution to fill this gap using a Generative Adversarial Network (GAN) based model, ShipGAN, to translate the simulation images into realistic images. Experiments were carried out to investigate the feasibility of generating realistic images using GAN-based image translation models. The synthetic realistic images from the simulation images were demonstrated to be reliable by the object detection and image segmentation algorithms trained with natural images

Machining-path mapping from free-state to clamped-state for thin-walled parts

Thin-walled parts with curved surface are widely used in industrial applications and the high-quality machining is still a major problem because of the low stiffness. By using the machining-path obtained from the design model of thin-walled parts with curved surface, the machining accuracy requirement may easily not be met due to the springback of clamping deformation when the machining process is finished. It is a novel idea that when the machining-path mapping from free-state to clamped-state is realized based on the clamping deformation, the final machining-path of thin-walled parts can be re-designed to directly ensure the machining accuracy requirement after the fixture is released. Based on the concomitant thought of curved surface and the elastic deformation theory of thin shell in this study, the mathematical model for the machining-path mapping from free-state to clamped-state is established for the thin-walled parts with curved surface. Then, the corresponding relationship of cutter contact (CC) points is calculated by grid mapping. Finally, the machining-path for the thin-walled parts with curved surface is re-designed under the clamped-state. Taking a thin-walled cylinder workpiece as an example, the experiment results show that the proposed method can achieve the avoiding purpose for the machining error caused by clamping deformation. These research achievements are of vital importance for realizing high-quality machining of the thin-walled parts with curved surface

Image segmentation in marine environments using convolutional LSTM for temporal context

Unmanned surface vehicles (USVs) carry a wealth of possible applications, many of which are limited by the vehicle's level of autonomy. The development of efficient and robust computer vision algorithms is a key factor in improving this, as they permit autonomous detection and thereby avoidance of obstacles. Recent developments in convolutional neural networks (CNNs), and the collection of increasingly diverse datasets, present opportunities for improved computer vision algorithms requiring less data and computational power. One area of potential improvement is the utilisation of temporal context from USV camera feeds in the form of sequential video frames to consistently identify obstacles in diverse marine environments under challenging conditions. This paper documents the implementation of this through long short-term memory (LSTM) cells in existing CNN structures and the exploration of parameters affecting their efficacy. It is found that LSTM cells are promising for achieving improved performance; however, there are weaknesses associated with network training procedures and datasets. Several novel network architectures are presented and compared using a state-of-the-art benchmarking method. It is shown that LSTM cells allow for better model performance with fewer training iterations, but that this advantage diminishes with additional training

A bioinspired bubble removal method in microchannels based on angiosperm xylem embolism repair

It is difficult to remove and eliminate bubbles in microchannels in many devices used in various biomedical fields, such as those needed for microfluidic immunoassays, point-of-care testing, and cell biology evaluations. Accumulated bubbles are associated with a number of negative outcomes, including a decrease in device sensitivity, inaccuracy of analysis results, and even functional failure. Xylem conduits of angiosperm have the ability to remove bubbles in obstructed conduits. Inspired by such an embolism repair mechanism, this paper proposes a bioinspired bubble removal method, which exhibits a prominent ability to dissolve bubbles continuously within a large range of flow rates (2 µL/min–850 µL/min) while retaining the stability and continuity of the flow without auxiliary equipment. Such a method also shows significant bubble removal stability in dealing with Newtonian liquids and non-Newtonian fluids, especially with high viscosity (6.76 Pa s) and low velocity (152 nL/min). Such advantages associated with the proposed bioinspired method reveal promising application prospects in macro/microfluidic fields ranging from 3D printing, implantable devices, virus detection, and biomedical fluid processing to microscale reactor operation and beyond

J-PLUS: Photometric Re-calibration with the Stellar Color Regression Method and an Improved Gaia XP Synthetic Photometry Method

We employ the corrected Gaia Early Data Release 3 (EDR3) photometric data and spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR7 to assemble a sample of approximately 0.25 million FGK dwarf photometric standard stars for the 12 J-PLUS filters using the Stellar Color Regression (SCR) method. We then independently validated the J-PLUS DR3 photometry, and uncovered significant systematic errors: up to 15 mmag in the results of Stellar Locus (SL) method, and up to 10 mmag mainly caused by magnitude-, color-, and extinction-dependent errors of the Gaia XP spectra with the Gaia BP/RP (XP) Synthetic Photometry (XPSP) method. We have also further developed the XPSP method using the corrected Gaia XP spectra by Huang et al. (2023) and applied it to the J-PLUS DR3 photometry. This resulted in an agreement of 1-5 mmag with the SCR method, and a two-fold improvement in the J-PLUS zero-point precision. Finally, the zero-point calibration for around 91% of the tiles within the LAMOST observation footprint is determined through the SCR method, with the remaining approximately 9% of tiles outside this footprint relying on the improved XPSP method. The re-calibrated J-PLUS DR3 photometric data establishes a solid data foundation for conducting research that depends on high-precision photometric calibration.Comment: 21 papes; 20 figures, submitted, see main results in Figures 5 and 1

A Deterministic Affine-Quadratic Optimal Control Problem

A deterministic affine-quadratic optimal control problem is considered. Due to the nature of the problem, optimal controls exist under some very mild conditions. Further, it is shown that under some assumptions, the optimal control is unique which leads to the differentiability of the value function. Therefore, the value function satisfies the corresponding Hamilton-Jacobi- Bellman equation in the classical sense, and the optimal control admits a state feedback representation. Under some additional conditions, it is shown that the value function is actually twice differentiable and the so-called quasi-Riccati equation is derived, whose solution can be used to construct the state feedback representation for the optimal control. © EDP Sciences, SMAI, 2014