Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of Concrete Shear Walls Retrofitted with SMA Tension Braces

ABSTRACT A recently developed Shape Memory Alloy (SMA) model is implemented to simulate the seismic retrofit of squat concrete shear walls with novel tension-only SMA braces. The model follows a tri-linear envelope that captures the flag-shaped behavior of superelastic SMAs and the accumulation of plastic straining at large strains. The response of the SMA braces is modeled with tension-only truss elements that are assigned the stress-strain parameters obtained from bare brace testing. The results provide satisfactory predictions of strength, drift, damage, energy, and displacement recovery. The effect of axial load and size of the SMA on the performance of the retrofitted walls are assessed through a parametric study.
Stochastic Optimization and Risk Problems for Engineering Applications

In this paper a technique of handling Gaussian stochastic optimization problems is presented. These optimization problems are experienced in various industrial processes, where random disturbances take action directly on the constraints defining the operational process domain and on the parameters of the criterion function. The objective of our scientific work aims to find a solution to the stochastic problem reformulated as an equivalent deterministic optimization problem with totally admissible solutions. This problem is referred to as a risk problem, and it is handled using nonlinear mathematical programming approaches and represents the optimal decision for efficient exploitation of the industrial applications. A numerical Gaussian stochastic optimization case study for an optimal solar energy system exploitation is considered to validate the theoretical results.
Multiobjective Atomic Orbital Search (MOAOS) for Global and Engineering Design Optimization

In the real world, many optimization problems have such levels of uncertainty and complexity that a single objective function cannot represent all the characteristics of the considered system. Hence, multi-objective optimization algorithms are needed to account for multiple aspects of the problem, represented by multiple objective functions, to achieve reasonable and useful results through the optimization procedure. In this paper, we introduce the multi-objective version of a recently-developed single-objective metaheuristic algorithm known as Atomic Orbital Search (AOS), which will be called Multi-Objective Atomic Orbital Search (MOAOS). To this end, the general aspects and main searching loop of the AOS algorithm are modified to make it capable of dealing with problems with multiple objectives. For the performance evaluation of this algorithm, the mathematical benchmark problems ZDT and DTLZ, alongside several real-world engineering design problems and the CEC- 2020 MMO test problems, are utilized. Based on the results obtained in this study, we can conclude that MOAOS is capable of producing either superior or closely comparable results when evaluated in competition with alternative state-of-the-art metaheuristic methods.
Big Cyber Security Data Analysis with Apache Mahou

Machine learning classifiers are known algorithms used to classify network intrusion detection due to the drastic growth of data, new tools are being required to handle such a large amount of data within a short time frame. In this Paper, we present a Model using the Apache Mahout Framework to train machine learning classifiers Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB) on CSE-CIC-IDS2018 dataset using Chi-Square and ANOVA f-test filter-based feature selection technique on an Apache Hadoop Framework. The performance of classifiers is measured in terms of Accuracy, Kappa, Precision, Recall, and Fl-Score for a comparative analysis of the various machine learning classifiers.
Blood vessel segmentation of retinal fundus images using dynamic preprocessing and mathematical morphology

Accurate segmentation of blood vessels can make an important and effective contribution to the identification and diagnosis of ocular diseases such as diabetic retinopathy, glaucoma, and hypertension. Contrast enhancement is an essential component of any retinal blood vessel segmentation process. The consistency of contrast within an image will define the reliability of the segmentation. A new approach to dynamic segmentation of retinal blood vessels is proposed in this paper. Preprocessing, vessel segmentation, and post-processing are the three main stages of this method. The enhancement technique was integrated with dynamic preprocessing to improve segmentation performance. The DRIVE database was used to test the proposed method and analyze the results. The experimental results confirmed an improvement in segmentation where the use of dynamic processing increased the accuracy from 91.65% to 93.23%.
Optimization of 3-D Sensor Design for Electrical Capacitance Tomography

When electrical capacitance tomography (ECT) is applied for 3-D imaging, the distribution for electrode arrays in the sensor has a great influence on the reconstructed image. In this work, a 24-electrode ECT sensor that covers a fixed imaging volume was optimized. The performance for image reconstruction of the sensors with different layers was compared and the 4-layer sensor was selected. Then the lengths of the electrodes for the 4-layer ECT sensor were further optimized based on the conformal transformation method. Simulations were carried out to evaluate the performance of the 3-D sensor. Numerical results show that using the designed sensor, low dynamic range, uniform sensitivity in the imaging volume and high correlation coefficient of reconstructed images can be obtained.
Deep Learning-Based Prediction Models for Freeway Traffic Flow under Non-Recurrent Events

This paper concerns predictions of freeway traffic flow under non-recurrent events using multivariate machine learning models, including the multilayer perceptron network and the one-dimensional CNN long short-term memory network. The machine learning architectures and loss functions for training neural networks are presented. The study region is a portion of the Kwinana Freeway northbound in Perth, Western Australia. The study dataset, obtained by matching the timestamp of all available data, has various features, including traffic volume (flow rate), speed, density and road incident. Using the root mean squared error and mean absolute error, results from the two learning models are compared to the baseline model to determine the suitable model for traffic prediction under non-recurrent events.
Harmonic Selection-based Analysis for High Impedance Fault Location Using Stockwell Transform and Random Forest

High Impedance Faults (HIFs) originate from the contact between an energized conductor and a high impedance surface. In distribution systems, the HIFs location is an issue that has not been completely solved due to the low fault current and varying impedance, which inhibits traditional fault location techniques from correctly functioning. Thus, this paper assesses the potential of the Random Forest algorithm to be employed to locate HIFs in power distribution systems. The main idea is based on the frequencies extracted by the Stockwell Transform from the phase and neutral currents measured only at the system substation using real HIF signals, thus performing a power quality data analysis. The results are promising, with high identification rates, even with noisy current signals. Additionally, the methodology can help researchers to better select their datasets for supervised-learning-based HIF location methods.
Liveness Enforcement for Time Petri Nets

This paper addresses the liveness enforcement problem of time Petri nets through the control of time intervals of transitions. We design a structure named a critical state class graph, whose nodes contain the critical constraints of the time intervals of transitions. Then, we propose an algorithm to control the time intervals of controllable transitions such that a non-live time Petri net can be transformed into a live time Petri net. The results show that a maximally permissive solution of liveness enforcement can be computed if it exists.
Operationalizing Machine Learning Models - A Systematic Literature Review

Deploying machine learning (ML) models to production with the same level of rigor and automation as traditional software systems has shown itself to be a non-trivial task, requiring extra care and infrastructure to deal with the additional challenges. Although many studies focus on adapting ML software engineering (SE) approaches and techniques, few studies have summarized the status and challenges of operationalizing ML models. Model operationalization encompasses all steps after model training and evaluation, including packaging the model in a format appropriate for deployment, publishing to a model registry or storage, integrating the model into a broader software system, serving, and monitoring. This study is the first systematic literature review investigating the techniques, tools, and infrastructures to operationalize ML models. After reviewing 24 primary studies, the results show that there are a number of tools for most use cases to operationalize ML models and cloud deployment in particular. The review also revealed several research opportunities, such as dynamic model-switching, continuous model-monitoring, and efficient edge ML deployments. CCS CONCEPTS • General and reference → Surveys and overviews; • Computing methodologies → Machine learning; • Software and its engineering → Software development techniques.
Parameter estimation for fault detection and classification in centrifugal pumps

Fault detection and diagnosis, have been topics of great interest in both academia and industry. A well-designed strategy can considerably save operating costs by minimizing services downtime and replaceable items costs. A good knowledge of the operating machine’s dynamics, a good measurements technique can significantly add value to the strategy design. Different approaches are nowadays used to effectively detect and diagnose faults. They can be summarized as data-driven, analytically based, and knowledge-based methods. This paper discussed a parametric approach on a steady-state for fault detection on pumps. The parametric estimation method requires good modeling of the system dynamics and proper use of the parameters as features for designing a diagnosis algorithm. The paper demonstrates that the correlation induced by the variation between the head and torque could be easily detected. The paper also suggests using the likelihood ratio test, in order to discretize parameters likely to hold direct information on the fault.
Circuit Architecture and Design of A Megahertz Wireless Power Transfer System for Drones

This paper presents a new circuit architecture and design of a megahertz wireless power transfer (WPT) system for drones. A belt-shaped 3D receiving (Rx) coil is especially proposed that has a minimized planar area but improve magnetic coupling. The saved space contributes to minimize the impact to the original aerodynamics of the drones. The circuit architecture employs the Class E2 topology for high efficiency when operating at 6.78 MHz. In order to enhance the robustness of the MHz WPT system, the impedance matching networks (IMNs) are added after the power amplifier (PA) and the Rx coil, respectively. The load-pull simulation over the Smith Chart is conducted to design the IMNs that ensure the robustness (i.e., high-efficient operation) when both loading condition (i.e., a battery under charging) and coil relative position dynamically change.
Opti2: A reconstruction approach for periodic signals using compressive sensing

The reconstruction of frequency-sparse signals from a low number of samples using a compressed sensing concept is a widely studied problem and solutions based on greedy and relaxation methods are available. However, their performance is degraded in presence of spectral leakage, which affects the sparsity of the signal representation and consequently, its estimation accuracy. In this paper a two-stage optimization approach, called Opti2, is proposed for the reconstruction of periodic signals that can be expressed in terms of fundamental frequency and harmonics. The estimation provided by basis pursuit denoising (BPDN) sparse optimization approach is computed in the first stage and used as initial guess for the second stage, where a constrained non-linear optimization problem is solved in an iterative fashion, aiming to improve the estimation of the signal parameters. The evaluation of the proposed method with simulated and experimental data demonstrates that it outperforms existing approaches in term of accuracy, showing its robustness to noise, compression rate and dictionary refinement factor.
TFFC-RNN:A New RNN Based Approach for Bearing and Misalignment Compound Fault

This paper proposes a fault diagnosis method based on the speed signal of the servo motor for the bearing and misalignment compound fault. First, it explores the change of motor speed caused by compound fault excitation and analyzes the theoretical feasibility of the speed method to achieve compound fault diagnosis. Experiments show that the detection of weak fault signals such as bearings in compound faults is susceptible to interference from mismatched installation faults, which will make the traditional diagnosis algorithms invalid. The pre-processed speed signal and the signal obtained through fast fourier transform(fft) are passed through the recurrent neural network respectively, and the input time-domain features and frequency-domain features are fused together as the basis for fault classification. This Time-Frequency Feature Compound-RNN model (TFFC-RNN) can perfectly classify bearing faults and normal signals under the interference of misalignment faults. Finally, the research and experimental verification of which recurrent neural network to use as the best composition of the model is carried out.
Assessment of FACTS Devices Nonsinusoidality in Smart Grid

FACTS-devices are the key elements of a Smart Grid. At the same time the nonlinear nature of FACTS devices causes current and voltage waveforms distortion in the electrical network. The article is devoted to the development of methodology for harmonic and interharmonic components assessment generated by FACTS devices. A mathematical model has been developed for harmonics and interharmonics order assessment in electrical network using the method of signal flow graphs. The developed methodology is investigated on the example of a three-phase six-pulse frequency converter with a salient DC link. The result of the research is a dependence diagram of the harmonics and interharmonics order generated by the frequency converter. The considered approach is universal and can be applied to various FACTS devices taking into account the correction of the transfer function.
Additively Manufactured Capacitive Proximity and Tactile Sensors for Soft Robotic Systems

Soft robotics are considered one of the most promising approaches towards fully collaborative robotic devices. Soft robotic systems are intrinsically safe due to their compliant nature. Using additive fabrication for the development of soft capacitive sensors, enables high flexibility and individualization capabilities in terms of design and material properties. In this work, we outline the sensorization of stretchable capacitive sensors for proximity and tactile detection. This sensor will represent an essential component of a soft robotic finger in future work. While the functional principle has already been shown previously, here we explore the design space further and perform more in depth test of the influences of the stretch on both proximity and tactile measurements. The capacitive pad's sensitivity is evaluated by applying normal force on each sensor, where the applied force is ranged between 2 N and 16 N, with resultant capacitive change of the four tactile sensors between 3 pF and 7 pF. The pad's deformation is tested by stretching the pad up to 5 cm. This results in increasing the capacitance of the tactile sensing elements between 0.6 pF and 1.5 pF, and decreasing of the capacitance in the proximity sensing elements between 1.4 pF and 1.5 pF.
Grant-Free Power Allocation for Ultra-Dense IoT: A Mean Field Perspective

Grant-free access, in which each Internet-of-Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant-free access with multi-packet reception capabilities, with an emphasis on ultra-low-end IoT applications with small data sizes and energy usage constraints. We propose a power allocation scheme that integrates the IoT device’s traffic and energy budget by using stochastic geometry framework and mean-field approximation to model and analyze mutual interference among active IoT devices. We also derive a Markovian model to capture and track the IoT device’s queue length, and derive the successful transmission probability at steady state. Simulation results illustrate the optimal power allocation strategy and show the effectiveness of the proposed approach.
Data-Driven Analysis of Distributed Generator-Based Power Systems Using Koopman Mode Decomposition

Accurate analysis and simultaneous control of observables, such as frequency and voltage, are necessary for power system stabilization. However, the trigonometric function-based Fourier transform (FT) is insufficient for analyzing power systems with multiple harmonics and nonlinear distortions as it approximates the dynamics with a linear model that assumes periodicity and stability, thereby prompting a need to develop a new method with higher performance and speed. In this study, we used a new non-model-based data-driven empirical mode decomposition (EMD) method, known as Koopman mode decomposition (KMD), to analyze nonlinear and unsteady power systems consisting of synchronous generators and distributed generators. The strengths and weaknesses of KMD were discussed by comparing the individual results obtained from KMD and FT.
Skin Cancer Classification based on Cosine Cyclical Learning Rate with Deep Learning

Since early-stage skin cancer identification can improve melanoma prognosis and significantly reduce treatment costs, AI-based diagnosis systems might greatly benefit patients suffering from suspicious skin lesions. The study proposes a cosine cyclical learning rate with a skin cancer classification model to improve melanoma prediction. The contributions of models involve three critical CNNs, which are standard deep feature extraction modules for the skin cancer classification in this study (Vgg19, ResNet101 and InceptionV3). Each CNN model applies three different learning rates: fixed learning rate(LR), Cosine Annealing LR, and Cosine Annealing with WarmRestarts. HAM10000 is a large collection of publicly available dermoscopic images dataset used for our experiments. The performance of the proposed approach was appraised through comparative experiments. The outcome has indicated that the proposed method has high efficiency in diagnosing skin lesions with a cosine cyclical learning rate.
Delay Analysis of a Multi-Hop Strict Priority Scheduler

Strict priority schedulers are often used in IP networks to guarantee delays for traffic that has strict delay requirements. There is literature for single node bounds for some priorities, however, for end-to-end delay bounds throughtraffic is often ignored, which can be crucial in obtaining correct end-to-end bounds. In this paper we give an upper bound on the end-to-end delay and delay variation distributions for all the priorities in a strict priority scheduler. We assume the highest two priorities are aggregates of constant-bit rate traffic, the third priority is modeled as an aggregate of on-off sources (e.g., VoIP), followed by a video stream. The delay distribution takes through-traffic and cross-traffic into account. Using simulations we demonstrate that our approach gives an upper bound to the delay and delay variation distributions.
Eye and Head Rotation Control Via Feedback Linearization

In this paper, recently studied rigid body rotation control problems are summarized as a constrained dynamics on SO(3) from the point of view of a nonlinear multi input multi output system. Our specific interests are in the rotation of human head and eye, where the control objective is to direct the pointing direction towards a stationary or mobile point target in space. Eye and head rotation control problems were studied in the mid-nineteenth century by Listing, Donders, Helmholtz and others. In these studies, it was observed by Donders that, for rotations away from the primary direction, the rotation vectors are restricted to lie on a surface, called the Donders' surface. Additionally, for eye movements under head-fixed condition, it was observed by Listing that the Donders' surface is actually a plane, called the Listing's plane. The input signals to the eye and head are provided by a triplet of external torques generated by muscles. Three output signals from the eye and head are chosen as follows. Two of the signals are coordinates of the frontal pointing direction. The third signal measures deviation of the state vector from the Donders' surface (respectively Listing's plane). Thus we have a 3 × 3 square system and the claim is that the proposed square system is locally feedback linearizable on a suitable neighborhood N of the state space.
Numerical Methods of Estimating Bounds on the Non-linear Saturation of Barotropic Instability

Two numerical methods are presented for calculating rigorous upper bounds on the finite-amplitude growth of barotropic instabilities to zonal jets on a rotating sphere. One of the methods is based on Shepherd (1988)'s analytic method, which uses the conservation law of domain-averaged pseudomomentum density. A variational minimization problem is solved numerically with a quasi-Newton method after discretization. The other is the authors' original method to solve a minimization problem under the constraints of the conservation laws of all Casimir invariants and total absolute angular momentum. The convex simplex method, which has been used in operations research, is applied to solve a quadratic programming problem. The two methods are applied to estimate the upper bounds for several profiles of the initial unstable jet and the bounds are compared with the results of non-linear time integrations from the unstable jet with a high-resolution model (Ishioka and Yoden, 1994). The two bounds are found to be almost completely identical to each other. Evidence from high-resolution numerical experiments is that the bounds overestimate the actual wave-enstrophy achieved in numerical experiments by a factor of 1.2 to 2.3.
A Modulation Method of Series-Resonant Dual-Active Half-Bridge Converter for ZVS and Minimum RMS current

For a given voltage conversion ratio and reference output power, minimizing conduction and switching losses are essential to achieve high efficiency. This paper proposes a modulation method to minimize RMS current and achieve complete ZVS operation for all operation ranges of the series-resonant dual-active half-bridge converter. The proposed modulation scheme utilizes four control degrees of freedom (primary-duty, secondary-duty, phase shift, and switching frequency) to achieve high efficiency. The proposed modulation method is based on time-domain analysis, which provides accurate analysis results even when harmonic currents are induced by the deviation of switching frequency from the resonant frequency. Analysis of the proposed modulation method is given for step-up/down mode and bidirectional operation. And it shows that the proposed modulation method is advantageous in the operation of a high step-up or high step-down conversion ratio. The effectiveness of the proposed modulation method is verified through an experimental prototype of a series-resonant dual-active-half-bridge converter operating at over 150 kHz switching frequency.