Miracle-dz/UniFAR-FTUs huggingface dataset:
Steel Defect Detection Based on Modified RetinaNet

The detection of possible defects on the steel surface is the last step and considered as the last line of defense for manufacturers to ensure the quality of products. To identify defects on the steel surface more quickly and accurately, this paper proposes a modified network based on RetinaNet in view of the current difficulties in detecting large-area steel defects and the overall performance to be improved of detectors. The depth of the FPN is increased to improve the ability to extract semantic information; the detail information enhancement structure, the feature information optimization structure, and the semantic information enhancement structure are combine into the augmented feature information to make the network better in using of image information; finally, we increase the sub-network’s layers to improve the ability to classify. Experiments were carried out on DGAM 2007 and PASCAL VOC. DGAM 2007 is a steel defect dataset, and PASCAL VOC is a general dataset to verify the overall performance of the detector. The results show that our proposed network has greatly improved the detection accuracy of the steel surface defects to 99.73%, and to some extent, large-area defects can be easily detected, and the mAP on DGAM 2007 and PASCAL VOC is both higher than that of the original RetinaNet network, which proves that the modified algorithm can play as an outstanding detector.
Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection

Anomaly Detection (AD) is an important research topic, with very diverse applications such as industrial defect detection, medical diagnosis, fraud detection, intrusion detection, etc. Within the last few years, deep learning-based methods have become the standard approach for AD. In many practical cases, the anomalies are unknown in advance. Therefore, most of challenging AD problems need to be addressed in an unsupervised or weakly supervised framework. In this context, deep generative models are widely used, in particular Variational Autoencoder (VAE) models. VAEs have been extended to Vector-Quantized VAEs (VQ-VAEs), a model increasingly popular because of its versatility enabled by the discrete latent space. We present for the first time a robust approach which takes advantage of the inner metrics of VQ-VAEs for AD. We show that the distance between the output of the encoder and the codebook vectors of a VQ-VAE provides a valuable information which can be used to localize the anomalies. In our approach, this metric complements a reconstruction-based metric to improve AD results. We compare our model with state-of-the-art AD models on three standards datasets, including the MVTec, UCSD-Ped1 and CIFAR-10 datasets. Experiments show that the proposed method yields high competitive results.
Modeling of the innovative magnetic pulse joining technology

Magnetic pulse joining, a technique increasingly used in industry, is a process that enables the rapid assembly of similar or dissimilar materials in the solid state. This process is based on the generation of electromagnetic forces in a conductive part by means of an inductor carrying a variable and intense current, of pulse form. The numerical models developed under COMSOL environment are intended to study, analyze and predict the behavior of flat or tubular parts, assembled by magnetic pulse. The applications of this work are related to innovative industrial systems, particularly used in the automotive industry. The numerical results obtained are compared and validated by previous experimental works.
Enhancing Robustness in Federated Learning by Supervised Anomaly Detection

Recent years have seen the increasing attention and popularity of federated learning (FL), a distributed learning framework for privacy and data security. However, by its fundamental design, federated learning is inherently vulnerable to model poisoning attacks: a malicious client may submit the local updates to influence the weights of the global model. Therefore, detecting malicious clients against model poisoning attacks in federated learning is useful in safety-critical tasks.However, existing methods either fail to analyze potential malicious data or are computationally restrictive. To overcome these weaknesses, we propose a robust federated learning method where the central server learns a supervised anomaly detector using adversarial data generated from a variety of state-of-the-art poisoning attacks. The key idea of this powerful anomaly detector lies in a comprehensive understanding of the benign update through distinguishing it from the diverse malicious ones. The anomaly detector would then be leveraged in the process of federated learning to automate the removal of malicious updates (even from unforeseen attacks). Through extensive experiments, we demonstrate its effectiveness against backdoor attacks, where the attackers inject adversarial triggers such that the global model will make incorrect predictions on the poisoned samples. We have verified that our method can achieve 99.0% detection AUC scores while enjoying longevity as the model converges. Our method has also shown significant advantages over existing robust federated learning methods in all settings. Furthermore, our method can be easily generalized to incorporate newly-developed poisoning attacks, thus accommodating ever-changing adversarial learning environments.
On Bernstein–Schnabl Operators on the Unit Interval

In this paper we study the Bernstein-Schnabl operators associated with a continuous selection of Borel measures on the unit interval. We investigate their approximation properties by presenting several estimates of the rate of convergence in terms of suitable moduli of smoothness. We also study some shape preserving properties as well as the preservation of the convexity. Moreover we show that their iterates converge to a Markov semigroup whose generator is a degenerate second order elliptic differential operator on the unit interval. Qualitative properties of this semigroup are also investigated together with its asymptotic behaviour.
Impact of distributed energy resources on power system resilience against earthquakes

Distributed energy resources (DERs) can enhance power system resilience against natural disasters. However, a high penetration of DERs changes the structure and dynamics of power systems at the transmission level. Their overall impact on resilience requires a thorough analysis. This paper describes a probabilistic resilience assessment method to assess how different levels of DERs penetration affect the system dynamics at the transmission level. Using a modified version of the IEEE39 test system, it quantifies how DERs can affect the resilience of the system to earthquakes. Additional probabilistic sensitivity studies investigate the effect of inertia on the resilience. Finally, the analysis of a specific scenario illustrates the effect of the DERs on the dynamics of the power system.
The Effect of Network Environment on Traffic Classification

One of the challenges of network traffic classification and mobile app identification is model generalization. The accuracy and efficiency of classification models are strongly influenced by the network environment and user behavior. By changing the network environmental parameters such as mobile type or app version, the accuracy of the trained classification model may be reduced. In this paper, we investigate “how different network environments can affect the performance of a classification model? ” and To this end, we have collected a dataset of 60 popular apps in Iran using different network environments.
A Formulation and Numerical Scheme for Fractional Optimal Control Problems

This article presents a general formulation and general numerical scheme for a class of fractional optimal control problems (FOCPs). The fractional derivative is described in the Caputo sense. The performance index of a FOCP is considered as a function of both the state and the control variables, and the dynamic constraints are expressed by a set of fractional differential equations. The calculus of variations, the Lagrange multiplier, and the formula for fractional integration by parts are used to obtain Euler-Lagrange equations for the FOCP. The formulation presented, and the resulting equations, are very similar to those that appear in classical optimal control theory. Thus, the present formulation essentially extends classical control theory to fractional dynamic systems. The formulation is used to derive the control equations for a quadratic linear fractional control problem. An iterative numerical scheme for finding the approximate numerical solution of the resulting equations is presented. For a linear system, this method results in a set of linear simultaneous equations, which can be solved directly. Numerical results for a FOCP are presented to demonstrate the feasibility of the method. It is shown that the solutions converge as the number of grid points increases, and the solutions approach the classical solutions as the order of the fractional derivatives approaches 1. The formulation presented is simple, and can be extended to other FOCPs.
Mass loss from evolved stars. VI: Mass-loss mechanisms and luminosity evolution

Molecular line data for the circumstellar envelopes of AGB red giant stars is analyzed to identify the mass-loss mechanisms and any relationships between mass loss and succeeding planetary nebulae. Mass-loss rates and outflow velocities are derived and, when compared with the stellar luminosities and optical depths of the dust envelopes, indicate that radiation pressure drives outflowing dust to its terminal velocity. Departures from this model, observed in OH 2312.8 + 4.2 and planetary and protoplanetary nebulae, had luminosities which declined after planetary nebula formation commenced. 58 references.
Rietveld analysis of strontium titanate in the Müller state

We report a neutron and x-ray Rietveld analysis of strontium titanate down to 1.5 K. The results do not show evidence for any supplementary structural phase transition or local frozen polar ordering. These results are a supplementary indication that at very low temperature strontium titanate should display only weak local modifications of the static tetragonal structure with no strong signature in the diffraction pattern.
Se2NAS: Self-Semi-Supervised architecture optimization for Semantic Segmentation

In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an optimized neural network (NN) model for this task by solving a jigsaw pretext problem identified by self-supervised learning over unlabeled training data, and, leveraging the structure of the unlabeled data with semi-supervised learning. Dynamic routing with a gradient descent approach is used to find the architecture of the NN model. Experiments on the Cityscapes and PASCAL VOC 2012 datasets show that the found neural network is four times more efficient than a state-of-the-art hand designed NN model in terms of floating-point operations.
An 80A 48V-Input Capacitor-Assisted Dual-Inductor Hybrid Dickson Converter for Large-Conversion Ratio Applications

This paper presents a capacitor-assisted dual-inductor hybrid Dickson (CADIHD) converter to provide single-step large-ratio voltage conversions. It combines a capacitor-assisted dual-inductor (CADI) filter with a N:1 Sc Dickson network to achieve complete soft-charging of all flying capacitors and to efficiently deliver high output currents. The proposed converter features a much larger duty ratio for a given voltage conversion and the reduced voltage and current stress of power switches, thereby significantly improving the efficiency in high output currents. A 4:1 CADIHD converter prototype with silicon MOSFETs was implemented to support conversions from a wide input range of 40 - 60V to an output of 1 - 2V with a maximum load of 80A. Both the peak power efficiency of 94.3% under 48-to-1 V conversion and the power density of 636W/in3 in the proposed converter outperform other prior hybrid converters.
On the Distance Protection of Power Grids dominated by Grid-forming Inverters

Grid-forming (GFM) inverter architectures have many promising features and offer several advantages over their grid-following (GFL) counterparts in the operation and control of inverter-dominated grids. However, operation of GFM inverters during unbalanced faults and their interaction with existing distance protection have been largely unexplored so far. In this paper, we investigate the performance of traditional distance protection schemes under the GFM paradigm. In particular, we focus on fault-loop impedance computation—which is one of the prevalent functions of contemporary distance protection and fault-zone identification—with different flavours of GFM control architectures. We uncover that GFM control with virtual-impedance limiting yields reliable results for fault-zone identification compared to other alternatives. To validate our analysis and findings, we furnish full-order EMT simulation results as well as experiments on a hardware-in-the-loop (HIL) test bed with a commercial protective relay.
Improving DNN Fault Tolerance in Semantic Segmentation Applications

Semantic segmentation of images is essential for autonomous driving and modern DNNs now achieve high accuracy. Automotive systems must comply with safety standards, requiring hardware fault detection. We present an analysis of the effect of faults using Google’s DeepLabV3+ network processing an industrial data-set. A new symptom-based fault detection algorithm is shown to detect >99% of critical faults with zero false positives and a compute overhead of 0.2%. Further, these faults can be masked, virtually eliminating all critical errors. To the authors’ knowledge this is the first fault tolerance study of a DNN semantic segmentation application.
Batched Mean-Variance Bandits

The issue of exploration-related risk has been the focus of recent research on bandit decision making in addition to the cumulative rewards given the ubiquity of uncertainty in sequential decision making. Whereas most existing risk-averse sequential decision making algorithms are fully-sequential assume that the player can switch actions at every time step, batched policies, under which players need to specify which actions to take in batches, are more relevant in real-time applications such as stock investments and experimental trials because the rewards and risks involved are often not immediately observable right after actions are taken. Despite such relevance, the effect of batched policies on risk-averse bandits has not been well studied. Using the common mean-variance risk measure as the risk criteria, we prove that O(log n) batches suffice to help the batched bandit algorithm attain the optimal instance-dependent regret upper bound of O(log n), where n is the length of time horizon. We also empirically demonstrate the effectiveness of our algorithms over different bandit instances, thereby providing insight on balancing risk and return for batched policies in multi-armed bandits. Finally, we point out several research directions for future work starting from our analysis.
Translational and rotational dynamic analysis of a superconducting levitation system

The rotational dynamics of a disc-shaped permanent magnet rotor levitated over a high temperature superconductor was studied experimentally and theoretically. The interaction between the rotor magnet and the superconductor was modelled by assuming the magnet to be a magnetic dipole and the superconductor a diamagnet. In the magnetomechanical analysis of the superconductor part, the frozen image concept was combined with the diamagnetic image, and the damping in the system was neglected. The interaction potential of the system is the combination of magnetic and gravitational potentials. From the dynamical analysis the equations of motion of the permanent magnet were stated as a function of lateral, vertical, tilt, precision and rotating angles. The vibration behaviour and correlation of the vibration of one direction with that of another were determined with a numerical calculation based on the Runge–Kutta method. The various vibrational frequencies identified were vertical, radial, tilt, precession and rotation. The tests performed for experimental verifications were translational and rotational. The permanent magnet was ‘spun up’ under vacuum conditions to analyse the dynamics of the free ‘spin down’ behaviour of the permanent magnet.
EFP-Net: High-Precision Remote Sensing Image Object Detection

Due to its advantages of high resolution and wide capturing range, satellite remote sensing images have been widely used in object detection nowadays. Nevertheless, the multi-scale character of satellite remote sensing images poses difficulties to our research and applications. To solve this problem, most people choose to use a convolution neural network (CNN) for feature extraction. However, the existing methods are not able to extract useful information efficiently and accurately. This paper proposes a new feature extraction network based on ResNet50, which introduces an Atrous convolution module to enhance the feature extraction of the object. On this basis, we propose an Enhanced Feature Pyramid Network (EFPN), which can effectively extract the multi-scale information by Bi-directional feature fusion. Furthermore, experiments were conducted in three datasets, Dota, NWPU VHR-10, and UCAS-AOD, in which the detection accuracy on NWPU VHR-10 reached 98.2%. The experiments show that our method not only improve the efficiency of multi-scale detection, but also greatly promote detection accuracy.
Galactic rotation and solar motion from stellar kinematics

I present three methods to determine the distance to the Galactic Centre R0, the solar azimuthal velocity in the Galactic rest frame Vg, ⊙ and hence the local circular speed Vc at R0. These simple, model-independent strategies reduce the set of assumptions to near-axisymmetry of the disc and are designed for kinematically hot stars, which are less affected by spiral arms and other effects. The first two methods use the position-dependent rotational streaming in the heliocentric radial velocities (U). The resulting rotation estimate θ from U velocities does not depend on Vg, ⊙. The first approach compares this with rotation from the Galactic azimuthal velocities to constrain Vg, ⊙ at an assumed R0. Both Vg, ⊙ and R0 can be determined using the proper motion of Sgr A* as a second constraint. The second strategy makes use of θ being roughly proportional to R0. Therefore a wrong R0 can be detected by an unphysical trend of Vg, ⊙ with the intrinsic rotation of different populations. From these two strategies I estimate R0 = (8.27 ± 0.29) kpc and Vg, ⊙ = (250 ± 9) km s−1 for a stellar sample from Sloan Extension for Galactic Understanding and Exploration, or, respectively, Vc = (238 ± 9) km s−1. The result is consistent with the third estimator, where I use the angle of the mean motion of stars, which should follow the geometry of the Galactic disc. This method also gives the solar radial motion with high accuracy. The rotation effect on U velocities must not be neglected when measuring the solar radial velocity U⊙. It biases U⊙ in any extended sample that is lop-sided in position angle α by of the order of 10 km s−1. Combining different methods I find U⊙ ∼ 14 km s−1, moderately higher than previous results from the Geneva–Copenhagen Survey.
The Interplay of Sirtuin-1, LDL-Cholesterol, and HDL Function: A Randomized Controlled Trial Comparing the Effects of Energy Restriction and Atorvastatin on Women with Premature Coronary Artery Disease

Introduction: HDL function has gained prominence in the literature as there is a greater predictive capacity for risk in early coronary artery disease when compared to the traditional parameters. However, it is unclear how dietary energy restriction and atorvastatin influence HDL function. Methods: A randomized controlled trial with 39 women with early CAD divided into three groups (n = 13): energy restriction (30% of VET), atorvastatin (80 mg), and control. Analyses of traditional biochemical markers (lipid and glucose profile), circulating Sirt-1, and HDL function (lipid composition, lipid transfer, and antioxidant capacity). Results: Participants’ mean age was 50.5 ± 3.8 years. Energy restriction increased Sirt-1 by 63.6 pg/mL (95%CI: 1.5–125.7; p = 0.045) and reduced BMI by 0.8 kg/m2 (95%CI: −1.349–−0.273; p = 0.004) in a manner independent of other cardiometabolic factors. Atorvastatin reduced LDL-c by 40.0 mg/dL (95%CI: −69.910–−10.1; p = 0.010). Increased Sirt-1 and reduced BMI were independently associated with reduced phospholipid composition of HDL (respectively, β = −0.071; CI95%:−0.136–−0.006; p = 0.033; β = 7.486; CI95%:0.350–14.622; p = 0.040). Reduction in BMI was associated with lower HDL-free cholesterol (β = 0.818; CI95%:0.044–1.593; p = 0.039). LDL-c reduction by statins was associated with reduced maximal lipid peroxide production rate of HDL (β = 0.002; CI95%:0.000–0.003; p = 0.022) and total conjugated diene generation (β = 0.001; CI95%:0.000–0.001; p = 0.029). Conclusion: This study showed that energy restriction and atorvastatin administration were associated with changes in lipid profile, serum Sirt-1 concentrations, and HDL function.
Solution Uptake in Cylindrical Carbon-Fibre-Reinforced Polymer (CFRP) Tendons

Salt water exposure conditions relevant to carbon-fibre-reinforced polymer (CFRP) prestressed concrete structures in marine environments are investigated. The diffusion into relatively small diameter CFRP tendons can be a lengthy process so the prediction of the long-term moisture uptake using short-term experiments on thin films of epoxy would be advantageous. However, the fibre inclusions within a composite introduce complexities, and factors are typically required for correlation with pure epoxy specimens. Diffusion parameters based on moisture uptake result from CFRP tendons exposed to salt water solution at 20°C and 60°C are compared with those obtained using equivalent thin film specimens. The higher temperature is selected to accelerate the moisture uptake. It is found that the measured ratios of tendon and epoxy diffusivity were temperature dependent, and the combination of the higher temperature and salt solution leads to an increased propensity for moisture uptake in the tendon. Existing analytical models to predict the CFRP tendon diffusivity from that of a thin film of epoxy did not appear to capture the observed trends. However, predictions using a unit cell with a fibre interface zone suggest that this may be due to an increased diffusivity in the interphase region.