Reviewerly/exHarmony huggingface dataset:
Characterization of meso-scale progressive failure model for fiber reinforced composites from high resolution experimental data

The damage initiation mechanisms of a carbon-epoxy single-edge notched cross-ply [90/0/90] laminate when loaded in tension were studied using Digital Volume Correlation (DVC). The specimen was loaded to approximately 13, 22, 24, and 26% of its ultimate load, and Computerized Tomography scans were taken in situ. The scans were then used to perform a DVC analysis using the open source code FIDVC [1]. Delamination of the interfaces, splitting of the 0 ply, and transverse cracking of the 90 plies were detected from the DVC results by observing strain field redistributions across the different stages. The interpretation of the DVC results were then supported by the construction of a ply-level micromechanics based Finite Element model to better understand deformation response in the presence of delamination. The DVC analysis herein presented was conducted to serve as a case study for verification of the assumptions in Progressive Failure Analysis models of composites such as the one presented in [2]. [1] E. Bar-Kochba, J. Toyjanova, E. Andrews, K. S. Kim and C. Franck, "A Fast Iterative Digital Volume Correlation Algorithm for Large Deformations," Experimental Mechanics, vol. 55, pp. 261-274, 2015. [2] M. H. Nguyen and A. M. Waas, "A novel mode-dependent and probabilistic semi-discrete damage model for progressive failure analysis of composite laminates - Part I: Meshing strategy and mixed-mode law," Composites Part C: Open Access 3, 2020.
Improving the Maneuver Automaton with Maneuver Interruption

This paper introduces a method for improving the re-planning ability of the Maneuver Automaton. The maneuver-to-maneuver transition, referred to as "Maneuver Interruption", is enabled by identifying near-identical dynamic states between different maneuvers. Naïve search first identifies viable maneuver connections within the motion primitive library, then the identified connections are pruned using Monte-Carlo simulations. Maneuver Interruption is tested on obstacle evasion tasks using a nonlinear ZOHD Drift model with a motion primitive library generated using reinforcement learning. Through Monte-Carlo simulations, the connection library was pruned to 1.5% of the size of the full library with minimal losses in performance. On randomly generated obstacle fields, Maneuver Interruption has enabled longer collision-free flights at the cost of some trajectory-tracking performance.
Synthetic proofs

Abstract This is a contribution to the idea that some proofs in first-order logic are synthetic. Syntheticity is understood here in its classical geometrical sense. Starting from Jaakko Hintikka’s original idea and Allen Hazen’s insights, this paper develops a method to define the ‘graphical form’ of formulae in monadic and dyadic fraction of first-order logic. Then a synthetic inferential step in Natural Deduction is defined. A proof is defined as synthetic if it includes at least one synthetic inferential step. Finally, it will be shown that the proposed definition is not sensitive to different ways of driving the same conclusion from the same assumptions.
Bayesian Active Sensing for Fault Estimation with Belief Space Tree Search

Autonomous spacecraft missions must be robust to system component faults such as sensor and actuator failures. An important setting to study fault tolerance is the Bayesian Active Sensing problem, where the system plans control inputs to gain information and estimate system failures quickly and with high confidence. We model the problem as a belief-state planning problem enabling simultaneous estimation of sensor and actuator failures in the presence of noise. Current belief-state tree search planners provide anytime, approximate solutions, but their underlying particle-filter belief update inhibits performance for information-gathering tasks. To address this issue, we propose POMCPMF, a belief-state tree search that uses an exact belief update in the tree search by exploiting the active sensing problem structure to decouple the belief update as a Kalman filter on the physical state and a particle filter on the failure modes. We validate our method on numerical experiments of spacecraft models with unknown sensor and actuator faults to demonstrate (i) the need for an active and planned sensing solution (as opposed to a passive and greedy solution) and (ii) the superior scalability of our method compared to existing active and planned methods. We then demonstrate the applicability of our algorithm to real systems by extending to a non-linear model and deploying our algorithm on a spacecraft simulator robot.
Development of Vision Guided Real-Time Trajectory Planning System for Autonomous Ground Refuelling Operations using Hybrid Dataset

Accurate and rapid object localisation and pose estimation are playing key roles during some of the real-time robotic operations such as object grasping and object manipulating. To do so, high-level robotic vision solutions need to be adopted. Computer vision approaches require a large amount of data to be able to create a perception pipeline robustly. Preparing such dataset to train the deep neural network could be challenging as the collection and manual annotation of huge amounts of data can take long hours and the development of the dataset needs to cover different conditions in weather and lighting. To ease this process, generating a synthetic dataset could be used. Due to the limitations of the synthetic dataset which will be described further down, instead of using a sole synthetic dataset, a hybrid dataset can be developed with the real dataset to overcome the limitations of both datasets. Even though the main objective of this study is to fulfil an autonomous nozzle insertion process for the ground refuelling operation of civil aircraft, the proposed approach is generic and can be adapted to any 3D visual robotic manipulation operation. This study is also offered to be the first visual trajectory planning control mechanism depending on the hybrid dataset to this date.
Verification of Adversarially Robust Reinforcement Learning Mechanisms in Aerospace Systems

In this paper, we present a detailed framework for the verification and validation of learning-based reinforcement learning (RL) mechanisms in aerospace control software. First, we integrate an adversarial input mitigation and moving target defense framework and verify its efficacy in real-time. Then, we provide a testing framework to verify the robustness of closed-loop RL mechanisms. The reliability of the adversarially robust RL mechanism is tested using the VerifAI toolkit and an X-plane 11 Cessna 172.
Tethered Multicopter Guidance in GPS-Denied Environments Through Reinforcement Learning

This paper presents a novel reinforcement learning (RL) approach for a tethered drone to follow a predefined three-dimensional trajectory in a GPS-denied environment. The adopted Q-learning strategy determines high-level actions using raw observations from the onoard accelerometers, gyros, and altimeter, which facilitates a low-level proportional-integral-derivative (PID) controller to drive the drone through the desired waypoints on a reference trajectory. The effectiveness of the proposed approach is demonstrated in a simulated environment.
Preliminary Development of Tomographic Wavelet-based Optical Flow Velocimetry (TwOFV)

The framework and implementation of a tomographic wavelet-based optical flow velocimetry (TwOFV) technique is demonstrated and assessed. Three-dimensional extensions for both optical flow and the wavelet transform are derived and implemented to extend wavelet-based optical flow velocimetry (wOFV) beyond planar measurements. A quantitative comparison is made for both TwOFV and tomographic particle image velocimetry (tomo-PIV) by applying the algorithms to synthetically generated volumetric particle fields dervied from a direct numerical simulation (DNS) of homogenous isotropic turbulence (HIT). In particular, the techniques were evaluated at four different seed densities that are representative of values used in experiments. TwOFV was shown to have a significant increase in spatial resolution and accuracy at higher seed densities as the TwOFV results show a great deal of similarity between the DNS velocity field ("truth”) and the TwOFV velocity estimate. This work represents a preliminary step towards the realization of high-resolution, volumetric velocimetry using TwOFV and will be utilized to start to guide the selection of a tomographic reconstruction technique along with setting experimental requirements.
Monocular In-flight Measurement of Airfoil Deflections

Abstract The knowledge of the actual shape of an aeroplane’s wing in midair is crucial to perform realistic flow simulations. Based on these analyses, the shape of a wing can be optimized by constructive measures and by the selective emptying of the fuel tanks installed inside the wings. As a result, the fuel consumption is reduced and fewer emissions occur. Furthermore, monitoring wing deflections allows for conclusions about the mechanical load and thus the service limit of an airfoil. To determine the wing deflection, we present the concept of a deployed measuring system consisting of measuring marks attached to the wing’s surface and a single camera. A basic model for the bending of a wing is explained and utilized, which assumes the preservation of arc lengths on the wing’s upper surface during bending. The measuring system was successfully applied during several long-distance flights with wide-body aircraft. The design of the measurement system, its setup and calibration, as well as obtained results are presented and discussed.
Structural plasticity‐based hydrogel optical Willshaw model for one‐shot on‐the‐fly edge learning

Abstract Autonomous one‐shot on‐the‐fly learning copes with the high privacy, small dataset, and in‐stream data at the edge. Implementing such learning on digital hardware suffers from the well‐known von‐Neumann and scaling bottlenecks. The optical neural networks featuring large parallelism, low latency, and high efficiency offer a promising solution. However, ex‐situ training of conventional optical networks, where optical path configuration and deep learning model optimization are separated, incurs hardware, energy and time overheads, and defeats the advantages in edge learning. Here, we introduced a bio‐inspired material‐algorithm co‐design to construct a hydrogel‐based optical Willshaw model (HOWM), manifesting Hebbian‐rule‐based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto‐chemical reactions. We first employed the HOWM as an all optical in‐sensor AI processor for one‐shot pattern classification, association and denoising. We then leveraged HOWM to function as a ternary content addressable memory (TCAM) of an optical memory augmented neural network (MANN) for one‐shot learning the Omniglot dataset. The HOWM empowered one‐shot on‐the‐fly edge learning leads to 1000× boost of energy efficiency and 10× boost of speed, which paves the way for the next‐generation autonomous, efficient, and affordable smart edge systems. image
Introduction

In this chapter we provide a brief introduction to machine learning (ML). We introduce some of the important steps in ML including data acquisition, feature engineering, model selection, model estimation, model evaluation, and model explanation.
Physics-Informed Radial Basis Function Networks: Solving Inverse Problems for Partial Differential Equations

The advantages of using neural network models as digital twins of objects with distributed parameters are shown. The analysis of using physically-informed neural networks to solve direct and inverse boundary value problems is presented. It is proposed to use radial basis function neural networks (RBFNNs) as physically-informed neural networks, which have a simple structure and the ability to adjust the non-linear parameters of the basis functions. The approach to the solving of the coefficient inverse boundary value problems, which allows determining the unknown function describing the physical environment, is proposed. The algorithm uses a unified approach to solve direct and inverse problems on RBFNN. RBFNN training is proposed to be performed using the fast algorithm of the Levenberg-Marquardt method developed by the authors. The analytical calculation of the Jacobi matrix in the Levenberg-Marquardt method is performed. An example of the solving of the inverse coefficient problem for a piecewise homogeneous medium is given. To solve the direct problem for a piecewise homogeneous medium we have used the algorithm developed by the authors which is based on solving separate problems for each region with different properties of the medium and on using the general error functional taking into account errors on the border of regions.
Robust Models of Distance Metric Learning by Interval-Valued Training Data

Two approaches for developing robust models of the distance metric learning under condition of interval-valued training data are proposed. Both approaches are based on a probabilistic representation of the feature intervals and on solving the minimax optimization problem which is stated for implementing robust decisions. The first approach is based on using the dual form representation of the linear maximization programming problem with probability distributions as optimization variables for computing optimal values of features within intervals. The second approach is based on considering extreme points of a polyhedron produced by the probability distribution constraints. The approaches can be regarded as a framework for developing a set of robust distance metric learning models which can be constructed on the basis of the Mahalanobis distance (linear transformation) as well as of Siamese neural networks (non-linear transformation).
Development and Production of Training Network Smart Control Systems

The quality of education directly affects the level of qualifications of graduates of higher education institutions. One of the most important factors is the practical experience gained during training. Unfortunately, most of the students of technical directions in the field of automation do not have the opportunity to gain practical experience of working with new equipment and, as a result, they do not have the skills to develop and manage modern automated control systems. This is influenced by several factors - the lack of Russian solutions in this area and the high cost of analogues offered by foreign companies. In this paper, the authors demonstrate their experience in developing highly specialized training stands and general-purpose stands. Step by step this process is described from design to ready-made smart automated control systems with accompanying documentation. The designed stands have sufficient flexibility and modifiability for use in a wide class of educational tasks.
Super resolution of underwater image based on generating generative adversarial networks

High-quality underwater images and videos play an important role for exploitation tasks in underwater environments, but the complexity of the underwater imaging environment makes the quality of the acquired underwater images generally low. In order to improve underwater image quality by enhancing the resolution of underwater images, we propose an underwater image super-resolution method based on the improvement of SRGAN. The images generated by conventional super-resolution methods lose details and high-frequency information, resulting in overly smooth images. We have improved SRGAN by replacing the original Residual Block with Residual Dense Block in the feature extraction network, which improves the network performance and speeds up the training and testing of the model. And the Shuffle Attention mechanism is incorporated after each residual block, which efficiently combines the channel attention mechanism and the spatial attention mechanism, significantly improving the network's ability to extract features and generating high-resolution images with richer detail information. At the same time, our method effectively solves the problem that the generator generates images that are too smooth and without grain details. Our method is compared with SRGAN and SRResnet methods in USR-248, UFO public underwater image dataset, and the experimental results demonstrate that our method generates super-resolution images with better image detail enhancement and higher PSNR and SSIM values.
The recovery scheme of improved double random phase coding encryption-hidden images based on deep learning and chaos

In order to improve the security of ciphertext image in transmission, a scheme of using neural network is proposed to restore the encryption-hiding images by using chaotic iris phase mask and double random phase encoding encrypted. To improve encryption performance, we replace the double random phase with chaotic iris phase masks. Firstly, the plaintext image is encrypted by improved double chaotic iris phase masks encoding, and the ciphertext image is generated. Then the ciphertext image is hidden into the carrier image through the Least Significant Bit (LSB) algorithm. It can avoid the detection to a certain extent. And a large number of hidden-plaintext image pairs are produced as a dataset. Secondly, the neural network is built in the process of continuous training and testing. The neural network selected in this paper is residual network (ResNet). The neural network to learn the logical relationship between input and output more efficiently, and successfully recovered the corresponding plaintext image. Finally, the trained neural network can fit the mapping relationship between the hidden image and the plaintext image. When decrypting, it is not necessary to extract the ciphertext image from the hidden image first and then decrypt it. This scheme can directly realize the decryption of hidden images. The paper elaborates the encryption, hiding and decryption process of the scheme in detail. Simulation experiments show that the scheme is feasible and has good robustness.
Clustering techniques for software product line feature identification

A Software Product Line (SPL) represents a family of products in one domain. It is often specified through a feature model representing the end-user system characteristics. Most proposed feature model construction methods examine a set of product variants code to identify common blocks of elements each of which represents a feature. In this identification process, they adopt different similarity measures and clustering techniques. Consequently, they differ in the granularity and cohesiveness of the identified features, which in turn affect the structure of the constructed feature model. Evidently, this difference also impacts the reuse of the SPL when deriving a new product. This paper compares widely adopted textual clustering techniques in terms of the recall and precision in their identification of the features. More specifically, it experimentally examines Latent Semantic Indexing (LSI), Principal Component Analysis (PCA), Support Vector Machine (SVM)and Term Frequency-Inverse Document Frequency (TF-IDF). The experimental results showed that LSI has better performance than the other textual similarity techniques with an average precision rate of 68.6
Optimize neural network based in-loop filters through iterative training

The latest video coding standard, named Versatile Video Coding (VVC), had been finalized in 2020. In our previous work, several neural network based in-loop filters are proposed to improve the compression performance beyond VVC. However, the effect of inter-frame referencing mechanism is not taken into the consideration, which leads to an inconsistency between the training process and the final testing process. To solve this problem, an iterative training method is proposed in this paper to further optimize the neural network based in-loop filters. Based on the proposed method, up to 1.74% additional YUV BD-rate saving can be achieved. Compared with VVC, the experiments show that on average 14.00% YUV BD-rate saving is achieved by the filter with 22 models while 11.21% YUV BD-rate saving is achieved by the filter with one single model. Moreover, the subjective evaluation has verified that the performance of the single model filter is better than VVC by a noticeable margin.
End-to-End Quality Controllable Image Compression

Quality control is an important topic in many application scenarios such as medical image compression. To achieve quality control in image compression network, in this paper, we propose a quality controllable image compression network, Quality Controllable Variational Autoencoder (QCVAE). QC-VAE consists of the Quality-Feature-Level (QFL) model we proposed and the Hyperprior Continuously Variable Rate (HCVR) image compression network which can adapt to multiple target qualities with only one single model. Note that even if the target quality is the same, different images should be quantized with different levels. With the help of the QFL model, we can obtain the estimated corresponding quantization level of the input image under the target quality, the selected level will be used to control the quantization loss. By this means, the QC-VAE can adapt to the target quality with high accuracy. Experimental results have shown that compared with the HCVR model, the proposed QC-VAE achieves accurate quality control without rate-distortion (RD) performance loss, indicating its superiority.
Trust Evolution Game in Blockchain

One of the main concepts behind Blockchain is trustlessness, where trust is shifted to a new paradigm through Cryptoeconomics. But how can people trust a trustless system? Blockchain protocols employ incentive structures predicated on game theory mechanisms in order to encourage the players (users and miners) in the system to act honestly. Users don't need to trust any single entity and there is no single point of failure that the system relies on(trustless system). In this paper, we developed an abstract game model for blockchain. The game model was for a repeated matrix game to be played by blockchain users. We studied how the game design enforces players to cooperate with each other to benefit the whole players in the network, which increase trust and make the system more trustworthy. Also, we applied two basic learning algorithms to see how the players of the game will evolve over time. We found that the players learn to cooperate in the game to get better payoffs.
Improving The Reconstruction Quality by Overfitted Decoder Bias in Neural Image Compression

End-to-end trainable models have reached the performance of traditional handcrafted compression techniques on videos and images. Since the parameters of these models are learned over large training sets, they are not optimal for any given image to be compressed. In this paper, we propose an instance-based fine-tuning of a subset of decoder’s bias to improve the reconstruction quality in exchange for extra encoding time and minor additional signaling cost. The proposed method is applicable to any end-to-end compression methods, improving the state-of-the-art neural image compression BD-rate by 3 – 5%.