Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study of Single-Switch and Double-Switch Converter Topologies, Working on Dead-Zone Mode, for Fuel Cell Applications

The growing concerns regarding the increase of greenhouse gases emissions causes engineers and researchers to look for more sustainable solutions to produce electricity. Fuel Cells (FCs) are viewed as a promising solution, since they can provide clean and renewable energy. Regardless the application of FCs, a power converter is required to control their operation. Hence, the overall efficiency of the system is strongly dependent on the efficiency of the power converter. Despite the multitude of power conversion solutions available in the literature, it is not clear which solutions provide the best compromise between efficiency, cost effectiveness, and reliability. On that basis, this paper aims to evaluate the performance of candidate DC-DC converter topologies for FC applications in DC microgrids. Main merits and drawbacks related to each topology will be assessed. Performance analysis will consider parameters like conversion efficiency, ripple, and number of components. A48V DC microgrid and a 3 kW FC, with a voltage range from 68 V-36V are modelled in Simulink.
Wearable Technology and Machine Learning to Monitor Upper-Limb Use in Brain Injury Survivors

The study explored the use of machine learning-based algorithms to estimate clinical scores (meant to capture the quality of upper-limb movements) via the analysis of wearable sensor data collected during the performance of functional tasks. Data was recorded using accelerometer units from thirty-seven individuals with upper-limb hemiparesis due to acquired brain injury while they performed eight activities of daily living. Clinicians assessed the study participants' quality of movement using the Functional Ability Scale. A hierarchical Random Forest-based algorithm was developed to derive the Functional Ability Scale scores from the accelerometer data. The results showed a high correlation (R2=0.91) between the estimates derived from the accelerometer data and the scores provided by the clinicians. The method herein presented has the potential to provide a means to track the motor recovery process in patients experiencing upper-limb motor impairments following a stroke or a traumatic brain injury hence enabling precision rehabilitation interventions.
Analysis of association rules between serum mass spectrometry data and physical examination data

Physical examination data and serum mass spectrometry data are obtained through the blood, serum mass spectrometry data are so inextricably linked between the patient's serum mass spectrometry data and the physical examination data, using the Association Analysis Algorithm in data mining to correlate the attributes of serum mass spectrometry data and physical examination data, the analysis results obtained have an important reference value for the process of decision designation and are of great significance for the development of medical blood testing .
Implementation of Green Coffee Bean Quality Classification Using Slim-CNN in Edge Computing

As one of the most important economic industries, how to improve the quality and output of the coffee industry is important. Defective coffee beans affect the flavor of coffee after roasting and grinding. In order to reduce the cost of labor and time, it is effective to use a convolutional neural network (CNN) model to identify defective green coffee beans. However, the complexity and huge parameters of the CNN model make the edge computing devices spend too much time on identification. Therefore, we introduced a lightweight deep learning network Slim-CNN to classify green coffee beans. Experiment results show that Slim-CNN achieves 92% accuracy with 6 times fewer parameters than MobileNet and 270 times fewer parameters than VGG16. The Slim-CNN model can be used on different edge computing devices to reduce labor costs in the coffee industry and improve the quality of coffee.
WiFi Realtime Localization for Smartphones by MIMO-OFDM Tracking Algorithm

This paper investigates the localization problem based on superimposed signals in multipath environments. First, we estimate the multipath parameters embedded in MIMO-OFDM signals via the space-alternating generalized expectation-maximization (SAGE) algorithm. Then to mitigate the disadvantages of initial value dependency and clutter problem of SAGE, we resort to the joint probabilistic data association (JPDA) method. Considering the computational burden, we adopt a modified JPDA technique, which is computationally tractable in applications with high clutter density. Hence it comes naturally an integrated localization algorithm, simulations validate its efficiency. Finally, we implement an accurate WiFi realtime localization system on smartphones that can be deployed on commodity WiFi infrastructure, which demonstrates the superiority of the proposed method.
Literature Review on Wireless Charging Technologies: Future Trend for Electric Vehicle?

Transportation is considered as the largest contributor to greenhouse gas emissions. Recently, many European countries and the World Health organization (WHO) have passed laws to reduce road vehicles emissions, which are responsible for 60.7% of European road transport air pollution. The electrification of vehicles required various charging infrastructure options. One of the state-of-the-art technologies is dynamic wireless charging systems (to deliver energy to the EV in motion). Thus, this paper summarizes distinct static and dynamic wireless charging technologies for electric vehicles. Analyzing different wireless power transfers and their limitations shows that the static wireless charging stations in house garages or open parking lots and dynamic charging infrastructures in smart roads or highway-charging lanes will be promising solutions in the near future to make electric vehicle charging easier or without making stops for recharging.
A Train Cooperative Operation Optimization Method based on Improved Reinforcement Learning Algorithm*

This paper mainly focuses on the high-speed train cooperative operation problem. To solve this problem, this paper presents a speed curve optimization method based on improved reinforcement learning algorithm. First, according to the train dynamics system, we build the speed curve optimization object. In order to realize the cooperative operation of trains, we use the artificial potential field method to establish the reward function for train spacing. At the same time, to ensure passenger comfort, train jerk rate also needs to be added into the reward function. And then, agent of improved reinforcement learning is established. The improved reinforcement learning algorithm is different from the general reinforcement learning algorithm in that the observation dimension of policy network is manually reduced compared with that of the Q value network to improve the learning speed of the algorithm. At the same time, in order to reduce the agent's attempts to perform useless actions in some states, a reference controller is added to the system to further accelerate the learning process. In addition, training parameters need to be set, such as training termination conditions, maximum number of steps, desired global reward value, and so on. After the training. The Agent can generate a desirable speed curve of train based on constraints of vehicle output and jerk rate under cooperative operation.
Recent Advancements in Finger Vein Biometrics: A Review

For biometric purposes, many parts of the body are used for recognition, and specifically for vein recognition the palm, finger, hand dorsal, and wrist are used. There exist a couple of other biometric recognitions from various body parts such as the fingerprints, retina, face, footprints, ear, palm prints, retina, vein, etc., but among them, vein recognition is much more reliable and accurate. Specifically, finger vein recognition techniques have gained importance in the last decade with various methods and techniques using classical and deep learning approaches. However, there is limited use of finger vein recognition techniques using deep learning and especially for authentication purposes. For finger vein recognition, various feature extraction techniques and classification algorithms have been used. In this paper, different techniques used for feature extraction and classification from both traditional and deep learning perspectives of finger vein recognition along with the accuracy achieved and other performance metrics have been shown.
Economically Optimal and Stability Preserving Hybrid Droop Control for Autonomous Microgrids

Cost-based droop schemes for microgrids (MG) have been developed to achieve cost reduction; meanwhile, microgrid stability depends on the droop control design and its parameters. This paper proposes a hybrid cost-based droop control that achieves both optimal economical operation and stability preserving for autonomous microgrids. The impact of cost-based droop schemes on stability is investigated utilizing a modified cost-based small-signal linearized model. Low-frequency eigenvalues tend to migrate towards instability when the load increases if a cost-based droop scheme is adopted. The proposed hybrid droop control manages to achieve the optimal generation by incorporating the incremental cost in the active power droop while ensuring stable performance by utilizing active and reactive power derivative controllers. The reactive power derivative controller is utilized to suppress the migration of the low-frequency eigenvalues towards instability. The active power derivative controller is utilized to damp the oscillations of the active power-sharing among the distributed generators (DG). The effectiveness of the proposed droop control to ensure an optimal and stable operation is validated on Matlab/Simulink.
Jedno rešenje alata za vizualizaciju mrežnog raspoređivača TTEternet tehnologije u automobilskoj industriji

U sistemima zasnovanim na komunikaciji čija je kompleksnost na visokom nivou izuzetno je komplikovano pro-naći uzrok problema u samoj komunikaciji. Postojanje određenih alata umnogome olakšava pronalazak i otklanjanje problema, te alati sami po sebi zauzimaju bitno mesto u procesu analize i pronalaska problema. Rad opisuje jedno rešenje alata za vizualizaciju mrežnog raspore divača TTEternet tehnologije koji se koristi u okviru komponente operativnog sistema zasnovanog na AUTOSAR standardu. Alat omogućava grafički prikaz vremena slanja svih eternet paketa u sistemu kao i ulazne i izlazne portove samog Eternet sviča kroz koje se šalju i primaju eternet paketi. Na osnovu istraživanja o samoj TTEternet tehnologiji rodila se ideja o vizualizacija rasporeda slanja paketa kao pomoćni alat koji bi olakašao pronalaženje problema na samom početku razvoja programske podrške za automobil i na taj način štedelo vreme od početnog do gotovog proizvoda. Testiranjem alata pokazalo se da je veliki broj slučajeva sa problemima koji se susreću, a vezani su za komunikaciju, pokriven alatom i na uspešana način prepoznati.
Gaussian Noise Removal Method Based on Empirical Wavelet Transform and Hypothesis Testing

Most industrial signals in reality have nonlinear and non-stationary characteristics, so the effect of signal denoising by general signal analysis methods is often not ideal. Taking the vibration signal of rolling bearing as an example, statistically, it is non Gaussian distribution, and the mixed noise is mostly Gaussian noise, and the noise bandwidth is wide, so the traditional filtering method is not suitable. Based on the above analysis, a Gaussian noise elimination method based on empirical wavelet transform and hypothesis test is proposed in this paper . Taking the vibration signal of rolling bearing as an example, Gaussian noise is mixed into it. The signal is decomposed into several components through empirical wavelet transform, and which components meet the Gaussian distribution are identified as noise through the hypothesis test of Gaussian distribution. The simulation results show that the denoising method proposed in this paper is more stable than the traditional method.
Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments

Even in the absence of unmeasured confounding factors or model misspecification, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (a) there exists a time-dependent risk factor for survival that also predicts subsequent treatment, and (b) past treatment history predicts subsequent risk factor level. In contrast, methods based on marginal structural models (MSMs) can provide consistent estimates of causal effects when unmeasured confounding and model misspecification are absent. MSMs are a new class of causal models whose parameters are estimated using a new class of estimators—inverse-probability-of-treatment weighted estimators. We use a marginal structural Cox proportional hazards model to estimate the joint effect of zidovudine (AZT) and prophylaxis therapy for Pneumocystis carinii pneumonia on the survival of HIV-positive men in the Multicenter AIDS Cohort Study, an observational study of homosexual men. We obtained an estimated causal mortality rate (hazard) ratio of . 67 (conservative 95% confidence interval .46-.98) for AZT and of 1.14 (.79, 1.64) for prophylaxis therapy. These estimates will be consistent for the true causal rate ratios when the functional forms chosen for our models are correct and data have been obtained on all time-independent and time-dependent covariates that predict both subsequent treatment and mortality.
Test and Evaluation Harnesses for Learning Systems

There is an increasing demand for operational uses of machine learning (ML), however, a lack of best practices for test and evaluation (T &E) of learning systems is a hindrance to supply. This manuscript proposes a new framework for best practices, described as T &E harnesses, that corresponds principally to the task of engineering a learning system-in contrast to the status quo task of solving a learning problem. The primary difference is a question of scope. This manuscript places T &E for ML into the broader scope of systems engineering processes. Importantly, two challenge problems, acquisition and operations, are used to motivate the use of T &E harnesses for learning systems. This manuscript draws from recent findings in experimental design for ML, combinatorial interaction testing of ML solutions, and the general systems modeling of ML. The concept of T &E harnesses is closely tied to existing models of systems engineering processes. We draw the conclusion that existing best practices for T &E form a subset of what is needed to rigorously test for system-level satisfaction of stakeholder needs.
SBL Based on Fixed Noise Power Estimation Applying to Conformal Array Beamforming

Underwater motion platforms have limited capacity of carrying and complex movements, and often carry small conformal arrays, so there is a high demand for robust high-resolution beamforming. sparse Bayesian learning (SBL) for beamforming is an algorithm which has robust high-resolution performance. This algorithm needs to estimate the noise power of the received signal, and mainly adopts the asymptotically effective estimation method of maximum likelihood, but this method needs to perform iterative calculation together with the SBL result, and the calculation is complicated. In this paper, SBL based on fixed noise power estimation applying to conformal array beamforming is proposed for the frequency broadband beamforming algorithm of the conformal array mounted on the small motion platform. The simulation and experimental results show that the main lobe and side lobes of this method have high-resolution, and this method does not need to estimate the noise power through iteration, and the calculation amount is also lower than the conventional SBL for beamforming.
Performance Analysis of PM Assisted Synchronous Reluctance Motor with Optimized Novel Design for Electric Vehicular Application

PM motors exhibit superior performance characteristics required for electric vehicles with a high manufacturing cost due to the high requirement for PMs in rotor assembly. Although research is already done to find out a suitable candidate for non-PM motors, but still, they are not good choices for all the performance parameters. In this paper, authors have produced the new alternative against this as it is impossible to achieve high performance of the motor without the use of PMs for electric vehicles application. This paper presents a new design approach of PM assisted synchronous reluctance motor (PMASynRM) with the minimum usage of PMs in rotor flux barriers. Finite element method (FEM) is used to carry out work for analysis and evaluation purposes and required performance parameters are obtained and compared with benchmark models. Effects of changes in current and speed values on torque are carried out to study torque variations. A few design limitations are also analyzed and present.
Augmented Reality Implementation for Human Anatomy Learning in Medical Education: A Review

The COVID-19 pandemic has changed the style of teaching and learning, one of them in medical education. Face-to-face meetings during the COVID-19 pandemic can no longer be done freely. With existing limitations, students are required to learn to understand learning without face-to-face with educators, especially medical students. So, it is necessary touse learning media that can increase active learning and learning independently. Learning media such as augmented reality is one that needs to be studied to help students learn. This research aims to understand the state-of-the-art of augmented reality implementation for human anatomy learning in the medical education field from 2018 to 2022. This research reviews recent studies about augmented implementation for human anatomy learning in medical education. The results of this research explain that there are things that can still be a challenge for further research. Future research can explore research for general people who use augmented reality to educate about human anatomy and the impact of its use. Another suggestion is to develop by minimizing the cost as low as possible.
Discrimination of phoneme length differences in word and sentence contexts

The ability of listeners to discriminate phoneme duration differences within word and sentence contexts was measured. This investigation was part of a series of studies examining the audibility and perceptual importance of speech modifications produced by stuttering intervention techniques. Just noticeable differences (jnd’s) of phoneme lengths were measured via the parameter estimation by sequential testing (PEST) task, an adaptive tracking procedure. The target phonemes were digitally manipulated to vary from normal (130 m) to prolonged (210 m) duration in 2‐m increments. In the first condition the phonemes were embedded in words. In the second condition the phonemes were embedded within words, which were further embedded in sentences. A four‐interval forced‐choice (4IAX) task was employed on each trial, and the PEST procedure determined the duration at which each listener correctly detected a difference between the normal duration and the test duration 71% of the time. The results revealed that listene.
Source Coding for Synthesizing Correlated Randomness

We consider a scenario wherein two parties Alice and Bob are provided <inline-formula> <tex-math notation="LaTeX">$X_{1}^{n}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$X_{2}^{n}$ </tex-math></inline-formula> – samples that are IID from a PMF <inline-formula> <tex-math notation="LaTeX">$P_{X_{1} X_{2}}$ </tex-math></inline-formula>. Alice and Bob can communicate to Charlie over (noiseless) communication links of rates <inline-formula> <tex-math notation="LaTeX">$R_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$R_{2}$ </tex-math></inline-formula>, respectively. Their goal is to enable Charlie generate samples <inline-formula> <tex-math notation="LaTeX">$Y^{n}$ </tex-math></inline-formula> such that the triple <inline-formula> <tex-math notation="LaTeX">$(X_{1}^{n},X_{2}^{n},Y^{n})$ </tex-math></inline-formula> has a PMF that is close, in total variation, to <inline-formula> <tex-math notation="LaTeX">$\prod P_{X_{1} X_{2} Y}$ </tex-math></inline-formula>, enabling the three parties achieve strong coordination. In addition, the three parties may posses pairwise shared common randomness at rates <inline-formula> <tex-math notation="LaTeX">$C_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$C_{2}$ </tex-math></inline-formula>. We address the problem of characterizing the set of rate quadruples <inline-formula> <tex-math notation="LaTeX">$(R_{1},R_{2},C_{1},C_{2})$ </tex-math></inline-formula> for which the above goal can be accomplished. We propose a new coding scheme based on random algebraic codes–coset codes in particular–of asymptotically large block-length. We analyze its performance and derive a single-letter information-theoretic inner bound. This bound subsumes the largest known inner bound and improves upon it strictly for identified examples. Our findings build on a variant of soft-covering which generalizes its applicability to the algebraic code ensembles. In addition, we provide an outer bound to the rate region for this three party distributed setup.
Approximating the number of successes in independent trials: Binomial versus Poisson

Let I1, I2, . , In be independent Bernoulli random variables with P(Ii = 1) = 1 − P(Ii = 0) = pi , 1 ≤ i ≤ n, and W = ∑ni=1 Ii , λ = EW = ∑n i=1 pi . It is well known that if pi ’s are the same, then W follows a binomial distribution and if pi ’s are small, then the distribution of W , denoted by LW , can be well approximated by the Poisson(λ). Define r = λ , the greatest integer ≤ λ, and set δ = λ − λ , and κ be the least integer more than or equal to max{λ2/(r − 1 − (1 + δ)2), n}. In this paper, we prove that, if r > 1 + (1 + δ)2, then