Estimating Disturbance Torque Effects on the Stability and Control Performance of Two-Axis Gimbal Systems

Introduction. Two-axis gimbal systems are applied for stabilizing and controlling the line of sight (LOS) of an optical or imaging system mounted on a moving vehicle. Gimbal systems are intended to isolate various disturbance torques and control the LOS toward the direction of a target. Two-axis gimbals can be of two main types, namely Yaw-Pitch and Swing-Roll type. In this article, we focus on investigating mathematical models of two-axis gimbals, which describe the impact of cross-disturbance torques on their stability and control performance. Simulations were conducted to compare advantages and disadvantages of the two types of two-axis gimbals.Aim. To study mathematical models describing the impact of cross-disturbance torques on the stability and control performance of two-axis gimbals.Materials and methods. Mathematical models of two-axis gimbal systems were synthesized by the Lagrange method. The operation of two-axis gimbal systems was simulated in the Matlab-Simulink environment. Results. Mathematical models and structural diagrams of the synthesized Yaw-Pitch and Swing-Roll gimbals were obtained. The conducted simulations of typical cases revealed different cross-disturbance effects.Conclusion. Motion equations for Swing-Roll and Yaw-Pitch gimbals were derived using similar methodology. The impact of cross-disturbance torques on gimbal systems was evaluated. The obtained results form a basis for selecting an optimal structure of tracking systems meeting the desired characteristics.Introduction. Two-axis gimbal systems are applied for stabilizing and controlling the line of sight (LOS) of an optical or imaging system mounted on a moving vehicle. Gimbal systems are intended to isolate various disturbance torques and control the LOS toward the direction of a target. Two-axis gimbals can be of two main types, namely Yaw-Pitch and Swing-Roll type. In this article, we focus on investigating mathematical models of two-axis gimbals, which describe the impact of cross-disturbance torques on their stability and control performance. Simulations were conducted to compare advantages and disadvantages of the two types of two-axis gimbals.Aim. To study mathematical models describing the impact of cross-disturbance torques on the stability and control performance of two-axis gimbals.Materials and methods. Mathematical models of two-axis gimbal systems were synthesized by the Lagrange method. The operation of two-axis gimbal systems was simulated in the Matlab-Simulink environment. Results. Mathematical models and structural diagrams of the synthesized Yaw-Pitch and Swing-Roll gimbals were obtained. The conducted simulations of typical cases revealed different cross-disturbance effects.Conclusion. Motion equations for Swing-Roll and Yaw-Pitch gimbals were derived using similar methodology. The impact of cross-disturbance torques on gimbal systems was evaluated. The obtained results form a basis for selecting an optimal structure of tracking systems meeting the desired characteristics

Optical properties of Eu3+ ions in boro-tellurite glass

The excitation, emission spectra and and lifetime of Eu-doped borotellurite glasses (BTe) have been investigated. The sideband phonon energy and electron-phonon coupling strength (g) have been found. The intensity parameters Ωλ were calculated from the emission spectrum. These parameters were used to predict radiative properties such as transition probabilities (AR), calculated branching ratios (βR) and stimulated emission cross-sections (σλp) for 5D0→7HJHFJ transitions

Determining reaction forces in planar mechanisms

In the paper, it is introduced a method to determine joint reaction forces, constraint forces and internal forces at the cross section of linkages. Based on the principle of compatibility and the ideality of constraints, the methodology is presented to analyze and determine reaction forces in planar mechanisms

Flat Seeking Bayesian Neural Networks

Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior distribution based on observed data. The model sampled from the posterior distribution can be used for providing ensemble predictions and quantifying prediction uncertainty. It is well-known that deep learning models with lower sharpness have better generalization ability. However, existing posterior inferences are not aware of sharpness/flatness in terms of formulation, possibly leading to high sharpness for the models sampled from them. In this paper, we develop theories, the Bayesian setting, and the variational inference approach for the sharpness-aware posterior. Specifically, the models sampled from our sharpness-aware posterior, and the optimal approximate posterior estimating this sharpness-aware posterior, have better flatness, hence possibly possessing higher generalization ability. We conduct experiments by leveraging the sharpness-aware posterior with state-of-the-art Bayesian Neural Networks, showing that the flat-seeking counterparts outperform their baselines in all metrics of interest.Comment: Accepted at NeurIPS 202

ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese

Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several significant drawbacks, including imbalanced data and noisy data on social media platforms. Imbalanced and noisy are two essential issues that need to be addressed in Vietnamese social media texts. Graph Convolutional Networks can address the problems of imbalanced and noisy data in text classification on social media by taking advantage of the graph structure of the data. This study presents a novel approach based on contextualized language model (PhoBERT) and graph-based method (Graph Convolutional Networks). In particular, the proposed approach, ViCGCN, jointly trained the power of Contextualized embeddings with the ability of Graph Convolutional Networks, GCN, to capture more syntactic and semantic dependencies to address those drawbacks. Extensive experiments on various Vietnamese benchmark datasets were conducted to verify our approach. The observation shows that applying GCN to BERTology models as the final layer significantly improves performance. Moreover, the experiments demonstrate that ViCGCN outperforms 13 powerful baseline models, including BERTology models, fusion BERTology and GCN models, other baselines, and SOTA on three benchmark social media datasets. Our proposed ViCGCN approach demonstrates a significant improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized Language Models, including multilingual and monolingual, on three benchmark datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our integrated model ViCGCN achieves the best performance compared to other BERTology integrated with GCN models