Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Integral Sliding Mode Control for Mismatched Buck DC-DC Converters

This paper investigates, a recent result on buck dc-dc converters affected by mismatched load uncertainty. It proposes a state and disturbance observer such that measurement of inductor current is not required. The state and disturbance observer is combined with integral sliding mode control to regulate the output voltage under varying load resistance and to track the reference voltage. The stability analysis of the proposed scheme is presented. A prototype buck converter is developed to assess the feasibility of the observer−controller combination. The robustness of the algorithm is demonstrated by changing load resistance and the reference voltage.
Dynamic Split TCP

This paper is dedicated to the research of the approaches to split transport connections. Currently, the most famous Split TCP approach is the static approach that can be ineffective in certain scenarios. In this paper we propose dynamic Split TCP that makes a decision on the parameters of proxying based on the current network condition and the proxy load. The experimental study of our approach showed its advantage in the resulting speed in comparison with classical Split TCP and analogous dynamic splitting approaches.
A Novel End-to-End Corporate Credit Rating Model Based on Self-Attention Mechanism

In traditional financial analysis discriminant analyses with statistic methods are considered as primary and widely applied corporate credit rating bases in which human impact plays an essential role. Machine learning approaches enormously assist credit rating in reducing human interference and improving rating efficiency. However, existing methods did not efficiently leverage the time series features of financial data that is beneficial for the classification tasks. We propose a novel end-to-end architecture, the SMAGRU, based on self multi-head attention with gated recurrent neural networks, which is capable of evaluating a corporate credit rating by capturing the time series features assigned the weights of market benchmarks. Experiments compared with statistic and machine learning baseline models on different datasets show that our model contains superiority in multi-class credit rating classification. The result of ablation experiments suggests that time series features are crucial in classification performance, and self multi-head attention can significantly enhance these features and thereby improve the accuracy and convergence speed. We show that the SMAGRU generalizes well, even on sparse data during K-fold cross validation experiments.
Three Dimension Angular Position Stabilization using LQR and Kalman Filter

This paper presents a design and evaluation for controlling a coupled system using a robust Linear Quadratic Regulator (LQR) controller acting on the augmented integral state space matrix model of a coupled system. System under investigation consisted of dual fan module that is interlinked and its axis moving freely in the pitch plan. On the other hand, a counter weight was used to balance the two fans thrust to optimize the controller effort in the elevation plan. The counterweight axe was denoted as the elevation axis. If the fans are not on the same horizontal line, the rotation of the system around itself in clockwise or anti-clockwise direction was carried out around the travel axis. The LQR controller design parameters should be able to stabilize itself at any degree on the travel or elevation axes while maintaining hover level along the pitch axis. Such controller acts by defining the penalty of each type of error in controlling this system. The error was multiplied by relevant penalty, then fed-back to the controller that controls the fan speeds accordingly. The representing model had three axes, each have a proportional, derivative, and integral term for the travel and elevation axes but not the pitch axis, the reasons will be discussed later in the paper. Modeling started by design process through defining a non-linear model of the system, linearizing it, then was transferred to state space format, add integral part to the model, then finally design and testing of an LQR controller.
PV-Battery System with Wireless Power Transfer for LV Applications

This paper presents a solar-PV (photo-voltaic) system with wireless power transfer (WPT) and battery storage backup in various state changes throughout the day. A 378W PV module is connected with high-frequency converters at 20 kHz switching frequency and an LCL compensation network for the wireless power transfer across the primary and the pick-up coil. For the power backup, a 48V Li-ion battery is used for the storage. The voltage and current characteristics of the battery and IPT (Inductive Power Transfer) interface are investigated when the PV module is at STC(Standard Test Conditions). The simulation result shows that a complete analysis of wireless power transfer of PV power with battery storage can be achieved to form a robust and reliable system for low voltage(LV) applications. This paper also studies the effect of a variable load on the battery current and voltage respectively.
Seed priming with calcium chloride improves the photosynthesis performance of faba bean plants subjected to cadmium stress

Faba bean (Vicia faba L.) seeds were treated with H2O [nonprimed (NP)] or 2% CaCl2 [primed (P)] before germination for 6 h. After seven days, seedlings were exposed to 0 or 50 µM CdCl2 concentrations for three weeks. Under Cd treatment, P plants showed an improvement of gas-exchange characteristics, chlorophyll (Chl) and carotenoids contents as compared to NP plants subjected to Cd stress. Additionally, the values of Chl fluorescence were relatively similar to those of control, implying that no photodamage occurred. Moreover, under 50 µM Cd, the P plants exhibited lesser accumulation of hydrogen peroxide and superoxide radicals in leaves as compared to NP plants. Likewise, results showed that CaCl2-seed pretreatment alleviated adverse effects of Cd on electrolyte leakage. In conclusion, CaCl2 improved photosynthesis attributes of faba bean plants subjected to Cd stress by mitigating the adverse effects of Cd toxicity through a reduced generation of reactive oxygen species.
Infrastructure-Free Global Localization in Repetitive Environments: An Overview

Repetitive environment is a challenging scenario for mobile robot global localization due to its highly similar structures and lack of distinctive features. Existing solutions in such environments rely heavily on pre-installed infrastructures, which are neither flexible nor cost-effective. Besides, few of the previous research have been focused on the implementation of infrastructure-free localization approaches in repetitive scenarios. Thus, this paper serves as a survey to investigate the problem of infrastructure-free mobile robot global localization with low-cost and efficient sensors in repetitive environments. Three of the most popular infrastructure-free localization methods, namely LiDAR-based localization (LBL), vision-based localization (VBL), and magnetic field-based localization (MFL), are analyzed and evaluated. Extensive global localization experiments are conducted in real-world repetitive scenarios and the results demonstrate that VBL methods perform slightly better than LBL and MFL methods. The overall evaluations indicate that infrastructure-free global localization in repetitive environment is still a challenging problem which deserves more research efforts to develop new solutions.
Is the risk of childhood leukaemia associated with socioeconomic measures in Denmark? A nationwide register‐based case‐control study

The aetiology of childhood leukaemia is poorly understood. Knowledge about differences in risk by socioeconomic status (SES) may enhance etiologic insights. We conducted a nationwide register‐based case‐control study to evaluate socioeconomic differences in the risk of childhood leukaemia in Denmark and to access whether associations varied by different measures of SES, time point of assessment, leukaemia type and age at diagnosis. We identified all cases of leukaemia in children aged 0 to 19 years, born and diagnosed between 1980 and 2013 from the Danish Cancer Registry (N = 1336) and sampled four individually matched controls per case (N = 5330). We used conditional logistic regression models for analysis. Medium and high level of parental education was associated with a higher risk of acute myeloid leukaemia (AML) in the offspring, mainly driven by children diagnosed at ages 0 to 4 years [odds ratio (OR) for high maternal education = 3.07; 95% confidence interval (CI): 1.44‐6.55]. We also observed a modestly increased risk for lymphoid leukaemia (LL) in association with higher level of parental education, but only in children diagnosed at ages 5 to 19 years. Higher parental income was associated with an increased risk of LL but not AML among children aged 5 to 19 years at diagnosis (OR for high maternal income = 2.78; 95% CI: 1.32‐5.89). Results for neighbourhood SES measures indicated null associations. Bias or under‐ascertainment of cases among families with low income or basic education are unlikely to explain the observed socioeconomic differences. Future research addressing explicitly the underlying mechanisms of our results may help to enhance etiologic insights of the disease.
Mix Energy Source Unified Loop based Dual Active Bridge for Electric Vehicle

This paper presents a unified loop based dual active bridge (DAB) with front end rectifier, a boost converter forming AC to DC power stage, and a DAB forming an isolated second DC-DC stage for electric vehicle battery charging application. In unified loop controller, controller capable of power factor correction (PFC) and battery charging control without sensing the DC link voltage in the topology is proposed. In this control strategy, irrespective of the nature of input supply (AC or DC) the output voltage/current maintained constant to the desired value as per battery constant current and constant voltage (CC-CV) characteristics thus the title referred mixed energy source. This type of power stage topology is used due to the inherent nature of the rectifier to provide DC output (constant or pulsating) irrespective of input supply nature. Therefore, a combination of unified loop and mentioned topology allows constant battery voltage/current irrespective of the nature of the input. A Boost converter is used to regulate DC link voltage depending upon load requirement thus varying DC link will allow to regulate the output supply of the DAB. As per the IEC 61000-3-2 standard, the total harmonic distortion in the current drawn from the AC main is restricted to prescribed limits. In this paper mathematical modeling of the topology has been carried along with a detailed study of working of the unified loop. MATLAB/Simulink based model along with the experimental results is discussed to support the idea with both AC and DC supply.
Statistical Process Control in Big Data Environment

Big data analysis tools are an inevitable part of instruments and methods for monitoring and predicting the longitudinal performance of the processes in the production systems of the future, based on the deep automatization and overall digitalization. From this point of view statistical process control (SPC) will continue to be very effective method for meeting these goals. But there must be done some modifications. This paper deals with such possible modifications of SPC. In the first part of the paper the stress is put on various methods that can be integrated into SPC to meet new challenges in collecting, analysing and interpreting data (control charts for high yield processes, multivariable approaches, profile monitoring, data mining tools including machine learning methods, nonparametric control charts). SW for the selected discussed methods is also mentioned. The second part of the paper is devoted to the nonparametric methods of SPC and to the methodology of their practical application.
Voltage disturbance observer for dual three-phase PMSM system

In this paper, a new compensation strategy design of the dead-time effects and other VSI non-linearities is presented for a dual three-phase PMSM. The strategy is based on an estimation of compensation voltages for each motor sub-system by the proposed disturbance observer. This observer is based on the Kalman filter algorithm and the knowledge of the motor model structure and its parameters. The proposed approach is verified by experiments on the test bench which consists of the experimental dual three-phase PMSM and set of three-phase power stages controlled by one Tricore AURIX TC275 microcontroller. The achieved results are compared with the results of the standard compensation strategy.
Particle Manipulation by a Novel Lamb Wave Resonator Array with Grating Reflectors

Acoustic methods based on lamb waves are expected to be a promising tool for the manipulation of bio-particles, by virtue of the miniaturization and low-power consumption of lamb wave resonators (LWRs). We utilize the streaming effects induced by acoustic waves propagating in the fluid to concentrate particles in a circular trajectory. However, multiple vortices which gather particles can be generated by a normal L WR, reducing the capture efficiency. In this device, the L WR was optimized into a novel structure with reflection gratings on one side, preventing the propagation of acoustic waves to the liquid from both sides simultaneously. By means of the ring array comprising eight L WRs with novel structures, only one vortex with the linear speed exceeding 80mm/s under the applied power of 30mW was generated in the center of the array. The acoustic pressure and flow field of the novel array were simulated by an FEA model. The proposed novel L WR structure provides a viable approach for the flexible generation of streaming patterns, which is of great significance for building more complex manipulation platforms for particles and cells in a bio-compatible and contactless manner.
Design of An UAV-Oriented Wireless Power Transfer System with Energy-Efficient Receiver

This paper is to present an unmanned aerial vehicle (UAV)-oriented wireless power transfer (WPT) system which accommodates with an energy-efficient receiver. Actually, the key is to use a buck converter to greatly reduce the current through the receiving coil and the rectifying diodes. As a result, the total power loss on the receiver is effectively reduced. First, to realize the proposed design, the whole system is modeled to analyze its system-level performance. Then, the loss on the receiver is modeled to instruct the parameter design of the buck converter, namely the duty ratio and switching frequency. After that, an experimental prototype is developed to validate the effectiveness of the proposed system. Results show that the proposed WPT system can achieve a dc-to-load efficiency of 82%, which is 9% higher than the case without the buck. Moreover, when the charging current is 6 A, the total loss on the receiver is reduced up to 14.4 W by properly configuring the buck converter.
PhishGAN: Data Augmentation and Identification of Homoglyph Attacks

Homoglyph attacks are a common technique used by hackers to conduct phishing. Domain names or links that are visually similar to actual ones are created via punycode to obfuscate the attack, making the victim more susceptible to phishing. For example, victims may mistake “linkedin.com” for “linkedin.com” and in the process, divulge personal details to the fake website. Current State of The Art (SOTA) typically make use of string comparison algorithms (e.g. Levenshtein Distance), which are computationally heavy. One reason for this is the lack of publicly available datasets, hindering the training of more advanced Machine Learning (ML) models. Furthermore, no one font is able to render all types of punycode correctly, posing a significant challenge to the creation of a dataset that is unbiased toward any particular font. This coupled with the vast number of internet domains pose a challenge in creating a dataset that can capture all possible variations. Here, we show how a conditional Generative Adversarial Network (GAN), PhishGAN, can be used to generate images of hieroglyphs, conditioned on non-homoglyph input text images. Practical changes to current SOTA were required to facilitate the generation of more varied homoglyph text-based images. We also demonstrate a workflow of how PhishGAN together with a Homoglyph Identifier (HI) model can be used to identify the domain the homoglyph was trying to imitate. Furthermore, we demonstrate how PhishGAN's ability to generate datasets on the fly, facilitating the quick adaptation of cybersecurity systems to detect new threats as they emerge.
A Real-Time Fail-Safe Algorithm for Decoding of Myoelectric Signals to Control a Prosthetic Arm

In this article a real-time self-tuning algorithm of detecting fingers movements based on the electromyographic (EMG) sensors is developed. A multi-level input data processing from the EMG sensor with self diagnosis is suggested. It provides the identification of muscle tension, which corresponds of several types of finger movements. This solution enables to use the lowbit controllers to manipulate the prosthetic hand. The results of system modeling and testing on the Arduino nano microcontroller demonstrated that the system identifies finger movements with high accuracy.
Adaptive Online Gated Recurrent Unit for Lithium-Ion Battery SOC Estimation

The Li-ion batteries are commonly used for Electric Vehicles (EVs) and aerospace applications. One of the essential parameters in Li-ion batteries is state of charge (SOC) that shows the available energy in a battery. Various methods were proposed for SOC estimation. Since the battery has a nonlinear equations, it is important to use a method that does not require the system model. In the present study, a new Adaptive Online Gated Recurrent Unit (GRU) method is proposed for the State of Charge (SOC) estimation. It is a kind of deep Recurrent Neural Network(RNN) which solved the vanishing gradient problem in RNNs with GRU units. For Optimization a robust adaptive Online gradient learning method is used. This method is able to tune online the learning rate in the process. Adaptive GRU is a nondependent method from the nonlinear batteries model and simplifies the mathematical computation. The proposed technique is implemented on the real dataset of LifePO4 Li-ion batteries for finding SOC estimation. The exprimental result indicate that the Adaptive GRU method is more accurate than simple RNN.
Fluid Shifts Induced by Physical Therapy in Lower Limb Lymphedema Patients

Complete decongestive therapy (CDT), a physical therapy including manual lymphatic drainage (MLD) and compression bandaging, is aimed at mobilizing fluid and reducing limb volume in lymphedema patients. Details of fluid shifts occurring in response to CDT are currently not well studied. Therefore, we investigated fluid shifts before, during and after CDT. Thirteen patients (3 males and 10 females, aged 57 ± 8.0 years, 167.2 ± 8.3 cm height, 91.0 ± 23.4 kg weight) diagnosed with stage II leg lymphedema participated. Leg volume, limb and whole-body fluid composition (total body water (limbTBW/%TBW), extracellular (limbECF/%ECF) and intracellular (limbICF/%ICF fluid), as well as ECF/ICF and limbECF/limbICF ratios were determined using perometry and bioelectrical impedance spectroscopy. Plasma volume, proteins, osmolality, oncotic pressure and electrolytes were assessed. Leg volume (p < 0.001), limbECF (p = 0.041), limbICF (p = 0.005) and limbECF/limbICF decreased over CDT. Total leg volume and limbTBW were correlated (r = 0.635). %TBW (p = 0.001) and %ECF (p = 0.007) decreased over time. The maximum effects were seen within one week of CDT. LimbICF (p = 0.017), %TBW (p = 0.009) and %ICF (p = 0.003) increased post-MLD, whereas ECF/ICF decreased due to MLD. Plasma volume increased by 1.5% post-MLD, as well as albumin and the albumin-to-globulin ratio (p = 0.005 and p = 0.049, respectively). Our results indicate that physical therapy leads to fluid shifts in lymphedema patients, with the greatest effects occurring within one week of therapy. Fluid shifts due to physical therapy were also reflected in increased plasma volume and plasma protein concentrations. Perometry, in contrast to bioelectrical impedance analysis, does not seem to be sensitive enough to detect small fluid changes caused by manual lymphatic drainage.
Meconium proteases and antiproteases as a potential source of biomarkers for the assessment of the intrauterine environment of the fetus

Abstract Background A protease–antiprotease balance is required to maintain the homeostasis of the intrauterine environment in which the fetus develops. Proteases and antiproteases accumulate in meconium exclusively during intrauterine life and are excreted after birth. Methods Proteomic analysis was used to investigate the protein composition in pooled 50 serial meconium portions from 10 neonates. The UniProt, BRENDA and MEROPS databases were the sources of information used to classify the meconium proteases and antiproteases among 946 proteins identified in meconium. Results A total of 265 enzymatic proteins and 33 protein inhibitors were identified in the meconium. The six main enzyme groups represented in the meconium were oxidoreductases (n = 44), transferases (n = 62), hydrolases (n = 137), lyases (n = 10), isomerases (n = 7) and ligases (n = 5). Six protease families were distinguished: serine (n = 28, 41.2% of all proteases), metallo (n = 23, 33.8%), cysteine (n = 10, 14.7%), aspartic (n = 4, 5.9%), theorine (n = 2, 2.9%) and mixed (n = 1, 1.5%) proteases. Conclusions The well-characterized meconium-based biomarker panel of proteases and their inhibitors may be a source of important information for use in diagnosing fetal disorders and predicting postnatal health and development. The differences in the composition and function between individual meconium proteases and antiproteases confirm their association with numerous metabolic processes characteristic of the intrauterine environment.
Model-Free Predictive Current Control of a Voltage Source Inverter based on Identification Algorithm

The behavior of Model Predictive Control (MPC) is by principle affected by the quality of the model used for the controlled system. A parameter mismatch between the plant and the controller can affect drastically the performance of MPC. This paper presents a new strategy called model-free predictive control (MF-PC) to overcome these problems. In this approach, a recursive least squares algorithm is implemented to identify the parameters of an auto-regressive with exogenous input (ARX) model, using input and output measurements of the controlled system. The proposed method enables an accurate prediction of the controlled variables without requiring detailed knowledge about the physical system. Simulation results obtained for the current control of a two-level, three-phase voltage source inverter, demonstrate that the MF-PC is exceptionally robust against the parameter variations and the model uncertainties, compared to conventional finite-control-set MPC.