The accessory neural arch: development, morphology, and systematic distribution

The accessory neural arch is an oddly distributed character present in several non-acanthomorph teleostean taxa. Its homology was often implied but never satisfyingly tested. In this study, we attended this pending problem. We analyzed the morphology, development, and systematic distribution of the accessory neural arch in teleosts. Using a comprehensive taxon sampling of cleared and stained specimens, we evaluated if the accessory neural arch fulfils existing homology criteria. We then combined these data with recent genetic phylogenies and ancestral character state estimation to reconstruct the evolutionary history of the accessory neural arch. While its gross morphology and development fit homology criteria, results from ancestral character state estimations suggest multiple independent evolutions within teleosts. Although the accessory neural arch cannot be homologous between several teleostean taxa, the concept of parallelism may explain the presence of such a similar character in a variety of non-acanthomorph teleostean taxa.

Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning

Welding operations may be subjected to different types of defects when the process is not properly controlled and most defect detection is done a posteriori. The mechanical variables that are at the origin of these imperfections are often not observable in situ. We propose an offline/online data assimilation approach that allows for joint parameter and state estimations based on local probabilistic surrogate models and thermal imaging in real-time. Offline, the surrogate models are built from a high-fidelity thermomechanical Finite Element parametric study of the weld. The online estimations are obtained by conditioning the local models by the observed temperature and known operational parameters, thus fusing high-fidelity simulation data and experimental measurements.

Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

<div>In the presence of uncertainty, one of the most difficult issues for tracking in control systems is to estimate the accuracy and precision of hidden variables. Kalman filter is considered as the widely adapted estimation algorithm for tracking applications. However, tracking of multiple objects is still a challenging task to achieve better results for prediction and correction. To solve this problem, a multi-dimensional Kalman filter is proposed using state estimations for tracking multiple objects. This paper also presents the performance analysis of proposed tracking model for linear measurements. The steady?state and covariance equations are derived and their co-efficients are updated. The multi-dimensional Kalman filter is evaluated mathematically for linear dynamic systems. The path tracking based on Kalman filter and multi-dimensional Kalman filter is also analyzed. The true and filtered responses of our proposed filtering algorithm for multiple object tracking are observed. The output covariance produces steady state values after four number of samples. The simulation results shows that the performance of our proposed filtering algorithm is 2x times effective than conventional Kalman filter for objects moving in linear motion and proves that proposed filter is suitable for real?time implementation.</div><div><br> </div>

Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

<div>In the presence of uncertainty, one of the most difficult issues for tracking in control systems is to estimate the accuracy and precision of hidden variables. Kalman filter is considered as the widely adapted estimation algorithm for tracking applications. However, tracking of multiple objects is still a challenging task to achieve better results for prediction and correction. To solve this problem, a multi-dimensional Kalman filter is proposed using state estimations for tracking multiple objects. This paper also presents the performance analysis of proposed tracking model for linear measurements. The steady?state and covariance equations are derived and their co-efficients are updated. The multi-dimensional Kalman filter is evaluated mathematically for linear dynamic systems. The path tracking based on Kalman filter and multi-dimensional Kalman filter is also analyzed. The true and filtered responses of our proposed filtering algorithm for multiple object tracking are observed. The output covariance produces steady state values after four number of samples. The simulation results shows that the performance of our proposed filtering algorithm is 2x times effective than conventional Kalman filter for objects moving in linear motion and proves that proposed filter is suitable for real?time implementation.</div><div><br> </div>

A Novel Multi-Area Distribution State Estimation Approach for Active Networks

This paper presents a hierarchically distributed algorithm for the execution of distribution state estimation function in active networks equipped with some phasor measurement units. The proposed algorithm employs voltage-based state estimation in rectangular form and is well-designed for large-scale active distribution networks. For this purpose, as the first step, the distribution network is supposed to be divided into some overlapped zones and local state estimations are executed in parallel for extracting operating states of these zones. Then, using coordinators in the feeders and the substation, the estimated local voltage profiles of all zones are coordinated with the local state estimation results of their neighboring zones. In this regard, each coordinator runs a state estimation process for the border buses (overlapped buses and buses with tie-lines) of its zones and based on the results for voltage phasor of border buses, the local voltage profiles in non-border buses of its zones are modified. The performance of the proposed algorithm is tested with an active distribution network, considering different combinations of operating conditions, network topologies, network decompositions, and measurement scenarios, and the results are presented and discussed.

Ocean state estimations for synthesis of ocean-mixing observations

Vertical mixing in oceans is an essential component of the dynamics of ocean circulation, including meridional circulation. Nevertheless, various aspects of mixing, particularly in conjunction with global ocean energetics, remain debatable. One of the biggest reasons is the lack of observational facts. With the recent expansion of global vertical-mixing observations, attempts have been made to estimate the ocean state using vertical-mixing observation data to better understand the role of mixing in oceanography. In this review, we discuss the current status of the ocean state estimation and future synthesis of vertically mixing observation data into the oceanic basin-scale state estimation, including progress of data assimilation studies using numerical models. These will contribute to the construction of the future line of observation, model, and data synthesis studies along which the issues on ocean mixing can be consistently resolved.

Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-Based SLAM

With the increasing demand for autonomous systems in the field of inspection, the use of unmanned aerial vehicles (UAVs) to replace human labor is becoming more frequent. However, the Global Positioning System (GPS) signal is usually denied in environments near or under bridges, which makes the manual operation of a UAV difficult and unreliable in these areas. This paper addresses a novel hierarchical graph-based simultaneous localization and mapping (SLAM) method for fully autonomous bridge inspection using an aerial vehicle, as well as a technical method for UAV control for actually conducting bridge inspections. Due to the harsh environment involved and the corresponding limitations on GPS usage, a graph-based SLAM approach using a tilted 3D LiDAR (Light Detection and Ranging) and a monocular camera to localize the UAV and map the target bridge is proposed. Each visual-inertial state estimate and the corresponding LiDAR sweep are combined into a single subnode. These subnodes make up a “supernode” that consists of state estimations and accumulated scan data for robust and stable node generation in graph SLAM. The constraints are generated from LiDAR data using the normal distribution transform (NDT) and generalized iterative closest point (G-ICP) matching. The feasibility of the proposed method was verified on two different types of bridges: on the ground and offshore.