Miracle-dz/UniFAR-FTUs huggingface dataset:
A 2×3 Reconfigurable Modes Wide Input Wide Output Range DC-DC Power Converter

There is an increasing demand for efficient and low-profile power converters in almost all industrial applications. Some require stable performance over a wide range of input voltages, whereas others require converters with a stable performance over a wide range of output voltages. In this regard, little work has been done to combine both requirements into one solution. This work suggests a unique solution that addresses both requirements at once. A power converter with <inline-formula> <tex-math notation="LaTeX">$2\times 3$ </tex-math></inline-formula> reconfigurable modes has been presented, able to operate with DC gains of 1, 2 and 4, which can be further extended to 8. The converter shows stable performance for the extended range of both input and output voltage. The flexible reconfigurable feature of the proposal can be applied in a variety of applications. Two reconfigurable modes on the input side can be configured following the variation in the line voltage to maintain a stable performance, and three reconfigurable modes for the output voltage, which raises the output voltage from 24V to 48V and 96V. The proposal is applied in an example application of a phase shifted full bridge converter. The converter is characterized for <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm {in}}=100$ </tex-math></inline-formula>-400V<sub>dc</sub>, <inline-formula> <tex-math notation="LaTeX">$\text{P}_{\mathrm {out}} =1$ </tex-math></inline-formula>kW.
Application aware concurrent multipath transmission in hybrid wireless networks

In the paper we present the Application-Aware Con-current Multipath Transmission (AACMT) concept that enables multi-homed terminal to use application-aware concurrent data forwarding over multiple paths. The AACMT forwarding takes into account the instantaneous application’s traffic requirements and the quality of the available paths. It is a network agnostic solution–in order to be deployed it only requires the AACMT agents at both ends of connection. AACMT utilizes the congestion avoidance and control mechanisms to properly allocate data over available paths. In the paper we described the concept as well as its evaluation for a multi-homed terminal connected simultaneously to LTE and WiFi networks.
Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches

Deep learning (DL) is an exciting field of interest for many researchers and business. Due to a massive leap in DL based research, many domains like Business, science and government sectors make use of DL for various applications. This work puts forward the importance of DL and its application in a few critical electrical segments. Initially, an introduction to Artificial Intelligence (AI) and Machine Learning (ML) is presented. Then the need for DL and the popular architectures, algorithms and frameworks used are presented. A summary of different techniques used in DL is outlined, and finally, a review on the application of deep learning techniques in some popular electrical applications is presented. Five critical electrical applications, namely identification of bearing faults, hot spots on the surface of PV panels, insulator faults, an inspection of power lines and Electric vehicles have been considered for review in this work. The primary aim of this work is to present chronologically, a survey of different areas in which it applies DL along with their architectures, frameworks and techniques to provide a deeper understanding of DL for widespread use in real-time applications.
A Current Supported PV Array Reconfiguration Technique to Mitigate Partial Shading

Partial shading in a solar PV plant is an important concern as it reduces the output power. Dynamic array reconfiguration (DAR) helps in reducing the impact of partial shading. But, it has its own limitations. Further, DAR does not guarantee single peak in array power-voltage characteristics during partial shading. It requires sophisticated and costly MPPT controller to track the global peak in order to extract maximum power from the PV array. In this paper, an improved DAR strategy is proposed using current injection (CI). This CI-based DAR strategy enhances the array power output beyond what is achievable by the available DAR techniques. Using this technique, multiple peaks in the power-voltage characteristics of the PV array are also avoided. The proposed method is evaluated for different levels of shading and its patterns. Experimental results establish the superiority of the CI-based DAR method over the available PV array reconfiguration strategies.
Multi-layer Cell Balancing using Switched Inductor and Switched Capacitor Topology

In electric vehicles large number of series connected cells are required to get high voltage. During normal charging and discharging operation these cells get imbalanced due to manufacturing tolerances and self-leakage current resulting into reduced voltage and deterioration of cells permanently as some of these might get overcharged or deep discharged. Hence, cell balancing is crucial for performance improvement and extended life of the battery. Balancing time and capacitor current are key parameters to judge the quality of a balancing circuit. In this work, a multi-layer cell balancing scheme has been proposed that provides rapid balancing with reduced charging current of the capacitors. The first layer of the proposed technique is derived from a buck-boost converter topology, where inductor is responsible for balancing of a pack consisting of two cells. The second and third layers of the proposed technique are derived from the double tiered switched capacitor circuit, where capacitors are used to do the equalization between battery packs. Modelling and designing of each layer of the proposed topology has been presented. The proposed topology is simple and versatile for industrial applications. The simulation results confirm that the proposed technique improved balancing speed and reduced switching current.
Causal Inference with Correlation Alignment

Estimating the effect of an intervention (i.e. causal inference) is a fundamental problem in fields like medicine, e-commerce, government (among others). An ideal approach requires experimental data, which is often hard to find or collect. Observational data on the other hand is in abundance, but contains systematic bias posing a great challenge for this task. In this paper, we introduce the use of two methods pioneered in domain adaptation: Euclidean and non-Euclidean correlation alignment in the context of causal inference. We evaluate their performance using widely used datasets and our results perform favourably against the current state of the art.
Decision-making based on decision tree for ball bearing monitoring

The vibrations produced by rotating machines affect people and the environment in many ways. They affect comfort, work capacity, health, and safety. For this, condition monitoring is an indispensable tool to track the evolution of vibrations. In the condition monitoring process of rotating machines, maintenance decision-making is subject to constraints relating to the lack of performance of the material resources and the unavailability of experts in the field on the sites, and sometimes the inability of these experts to make a decision. For this reason, a methodology based on two approaches: the vibration analysis and the decision tree to model of the decision-making is proposed. A set of data is collected from vibration signals analysis taken from a set of experiments performed on ball bearings. Then a classification algorithm is applied to build the decision tree. Finally, expert rules are extracted. These rules will be used in the development of a decision-making system, called an expert system. The effectiveness of the proposed methodology is demonstrated in this study.
Experience with fast Fourier least squares in the refinement of the crystal structure of rhombohedral 2-zinc insulin at 1.5 Å resolution

The newly developed fast Fourier least-squares algorithm [Agarwal (1978). Acta Cryst. A34, 791-809] has been used to refine the structure of rhombohedral 2-Zn pig insulin at a resolution of 1.5 A. The CPU time required for each cycle of refinement was about 3 min on an IBM 370/168 computer. After 67 cycles of refinement the conventional R factor was 0.113 for 11 890 terms. The procedure used in the refinement is described and an analysis of the results presented. From the experience learned in this refinement a procedure is suggested which should provide faster convergence and require considerably fewer refinement cycles.
An automatic taxi receipt text recognition application system

Gathering and managing the data of taxi receipts in reimbursement process consumes a lot of manpower. Due to the bad painting and the irregular arrangement of the taxi receipts, the automatic taxi receipt character recognition task has not been solved yet. This paper proposes an automatic taxi receipt text recognition application system based on a recognition system and a communication system. In the recognition system, a Single Shot Multi-Box Detector(SSD) neural network is used to determine the data region, and a CNN (convolution neural network)-GRU (gated recurrent unit) combined neural network is designed for character recognition. For the networks, our data set contains 1800 images of taxi receipts, and the cross validation results show that the accuracy reaches 94.36%. The communication system is deployed on a multimedia server and the system can be used on both computers and phones.
Implementation of Statistical Methods to optimize the Processes of Transport Systems

Currently, statistical modeling is a very widespread way to simplify the approximation of reality through mathematical descriptions of the systems studied in the form of mathematical models. Model is a very often used simplified abstract tool for predicting the behavior of the modeled systems. When modeling complex transport systems, we encounter phenomena and processes whose input parameters are often random in nature. Their states can be predicted with some probability. In such cases, it is possible to use Monte Carlo methods for statistical prediction of states of continuous random phenomena and subsequent simulation and a sufficient number of generated input parameters of the model to obtain statistical results approaching the state of the modeled system in the real environment. The content of the article is a description of the practical application and use of the Monte Carlo method in statistical modeling in the study and in solving a specific problem in transport systems and optimization of transport processes.
Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools

In recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor environments. The new Industry 4.0 model allows smart factories to become very advanced IT industries, generating an ever-increasing amount of valuable data. As a consequence, the necessity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision-making process. This article discusses the latest software technologies needed to collect, manage, and elaborate all data generated through innovative Internet-of-Things (IoT) architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step toward the rich landscape of the literature for readers approaching this field and as a global yet detailed overview of the current state of the art in the Industry 4.0 domain for experts. As a case study, we discuss, in detail, the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs.
D2D Communication Underlaying Microwave and Millimeter-Wave Cellular Networks Using CIPC

D2D communication is one of the promising technologies to improve network performance through direct wireless communication between users, without, or with limited participation of the base station (BS). In this paper, we investigate the performance of D2D communication underlaying microwave ( $\mu $ -wave) and millimeter-wave (mm-wave) cellular networks using stochastic geometry. Rayleigh fading is considered to characterize the $\mu $ -wave channels in both links, while the Rician fading is used to characterize the mm-wave channel in the D2D and cellular desired link. The D2D communication is established when the losses between the D2D transmitter and the D2D receiver are lower compared to the losses that exist between the D2D transmitter and the BS receiver. On the other hand, to minimize the interference caused by the coexistence of cellular and D2D communication and increase network performance, the Channel Inversion-Based Power Control (CIPC) is studied. In this way, the Complementary Cumulative Distribution Function (CCDF) for the successful transmission is obtained. Based on the analytical and numerical results, we show an important increase in the probability of successful transmission using the CIPC and considering the channel gains condition to establish the D2D communication compared to the traditional mode in which power control is not used and no conditions are considered to establish the D2D communication.
Investigating the Effectiveness of a Cable-driven Pinch-Grasp Haptic Interface

Characterisation of virtual objects and their properties is made more efficient through a grasping interface device with force feedback. The requirement for such dexterity has produced complex haptic interface structures that impose limitations on device transparency and fidelity. This study examines the effectiveness of a cable-driven pinch-grasp haptic interface to accurately generate forces to a user’s fingertips during object interaction in a virtual environment. Usability experiments were completed and comprised of size and stiffness discrimination tasks. Both studies compared grasping virtual objects under two modality configurations with the pinch-grasp interface. One configuration encompassed the combination of force and vision feedback, and the other maintained solely vision interaction with the device. Task completion times and correct identification were determined. Results demonstrate that the device improved task completion times in the two experiments when both force and vision feedback modalities were active. The percentage of correct identification was also greater in both experiments with force and vision feedback. These results motivate the use of a bidirectional cable actuation system for the development of multipoint dexterous haptic interface devices.
AC–DC–AC Single-Phase Multilevel Converters Based on Three-Leg Modules Cascaded-Connected Through Transformers

This article investigates a family of single-phase ac–dc–ac multilevel converters that are capable of mitigating sags/swells and voltage harmonics in the grid supply, ensuring regulated load voltage (with fixed amplitude and frequency), and grid current with high power factor and low harmonic content. Thus, the converters are suitable to operate as a unified power quality compensator or uninterrupted power supply. The proposed topologies are composed of three-leg (3L) modules cascade-connected through transformers at the grid side. From the generalized model presented, topologies with two and three 3L modules are discussed. System equations, PWM strategies, and a control system are developed. Compared with the conventional one, the proposed topologies present better results in terms of harmonic distortions, semiconductor losses, and switching frequencies. Simulation and experimental results in different load conditions are provided to validate the feasibility of the system under grid disturbances and load transients.
Vertical Autoscaling of GPU Resources for Machine Learning in the Cloud

Vertical autoscaling scales the amount of resources reserved by a virtual machine in the cloud. Although there have been studies on vertical autoscaling of CPU and memory resources, these have yet to consider GPU resources. In this paper, we propose a vertical autoscaling algorithm to improve the utilization of GPU resources within budget limit by exploiting Lyapunov optimization. Our algorithm deals with the correlation between GPU and CPU resources and requires only resource utilization information to decide to scale GPU resources. The performance measurement results show that our GPU vertical autoscaler can provide optimal performance to containerized CNN-based machine learning applications, such as ResNet-50 and Yolo-v4, with respect to execution time and throughput.
Passive beamforming design for intelligent reflecting surface assisted MIMO systems

Intelligent reflecting surfaces (IRSs) constitute passive devices, which are capable of adjusting the phase shifts of their reflected signals, and hence they are suitable for passive beamforming. In this paper, we conceive their design with the active beamforming action of multiple-input multiple- output (MIMO) systems used at the access points (APs) for improving the beamforming gain, where both the APs and users are equipped with multiple antennas. Firstly, we decouple the optimization problem and design the active beamforming for a given IRS configuration. Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program (QCQP). For solving the transformed problem, we give an approximate solution based on the technique of widely used semidefinite relaxation (SDR). We also propose a low-complexity iterative solution. We further prove that it can converge to a locally optimal value. Finally, considering the practical scenario of discrete phase shifts at the IRS, we give the quantization design for IRS elements on basis of the two solutions. Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks.
Gauss-Newton: A Prediction-Error-Gradient based Algorithm to Track PMSM Parameters Online

Online adaptation of temperature-sensitive motor parameters is of significance for the electric drives in reliability-critical applications. Recursive prediction error method (RPEM) is widely used for this purpose. Gauss-Newton Algorithm (GNA), a prediction-error-gradients based algorithm, is adopted in this paper to find RPEM-gains for the parameter identification. This paper first investigates the simultaneous identifiability of permanent magnet flux linkage ($\Psi_{\mathrm{m}}$), and stator resistance (Rs) of interior permanent magnet synchronous machine (IPMSM) using both nonlinear observability theorem and RPEM. Subsequently, GNA is analyzed for its tracking capability, speed of convergence, need of gain-scheduling and computational demand in comparison to stochastic gradient (SGA), another algorithm of the same class, using steady and dynamic state simulations.
Features Importance to Improve Interpretability of Chronic Kidney Disease Early Diagnosis

Chronic Kidney Disease (CKD) is a worldwide chronic disease that, if it is recognized late, leads a vast majority of patients to suffer premature mortality and quality of life decline due to a progressive loss of kidney function. Data mining classifiers would contribute to an early diagnosis and hence preventing kidney severe damage since subtle patterns in CKD indicators can be discovered. By employing Cross Industry Standard Process of Data Mining (CRISP-DM) methodology along with features importance techniques, this work develops a classifier model that would support healthcare professionals in early diagnosis of CKD patients. By means of a data workflow pipeline, an automated data transformation, modelling and evaluation is applied to the CKD dataset extracted from the University of California Irvine-Machine Learning (UCI-ML) repository. The pipeline developed is used to carry out an exhaustive search of the best data mining classifier and the different parameters of the data preparation’s sub-stages like data missing and feature selection. As a result, AdaBoost is selected as the best classifier with a 100% in terms of accuracy, precision, sensivity, specificity, and f1-score; outperforming the classification results obtained by the related works even with unseen data from a test set. Regarding model’s interpretability, the application of feature selection reduces from 24 to 12 the group of features to be employed in the classifier model developed, achieving more explainable model’s outputs. Furthermore, an analysis of the importance of features selected is carried out to explore the relevance of each selected feature.
Multi-modal, Multi-labeled Sports Highlight Extraction

Video highlight extraction has been as important as many other research on video analysis. To focus on sports highlight extraction, we aim to adopt machine learning techniques to provide an efficient approach to catch the most important moments in sports games of only a short period of time in such a fast-forward age. To elaborate the procedure, we examine frame by frame in a video to summarize what happened in a sports game which may contain the exciting moments, funny moments, game turning point, or even controversial events to name a few.One of the special treatment in this work is that we emphasize the multi-modal approach. Sports video contains a variety of modalities such as images, audios, scores and game time, etc. In this research, we explore different fusion strategies (e.g., the fusion on latent features or early-stage features) to form multi-modal data for modeling in order to utilize rich information to improve the result of highlight extraction. Furthermore, we consider the multi-label model based on the factors that affect the highlights to extract the joint features, and combine with the multi-modal model to further improve the final performance. The experiment results show the outstanding performance of the proposed method on the task of sports highlight extraction.
Dynamic Performance of Coupled SIDO Buck Converter Operating in Current Mode and Voltage Mode Control

This paper studies the dynamic performances like audio-susceptibility, load regulation of coupled inductor single-input dual-output (CI-SIDO) buck converter operating in average current control (ACC) and voltage-mode control (VMC). First, the state-space model of the CI-SIDO buck is derived. Using the state-space model, the open-loop load regulation and audio-susceptibility is derived and analysed. Then, the closed-loop load regulation and audio-susceptibility are derived by closing the feedback loop for voltage-mode control (VMC) and average current control (ACC). Finally, the derived closed-loop transfer functions are verified through simulation results.
Thermal Noise Limits for Optical Time Domain Reflectometry

We derive fundamental thermodynamic limits on the strain resolution of a class of fiber optic distributed acoustic sensing (DAS) techniques based on optical time domain reflectometry. The results indicate a lower limit of around <inline-formula><tex-math notation="LaTeX">$10^{-14}$</tex-math></inline-formula>strain/<inline-formula><tex-math notation="LaTeX">$\sqrt{\mathrm{Hz}}$</tex-math></inline-formula> for a 2 m long (10 ns) optical pulse. These limits indicate that interrogation of unmodified fiber optic cable is unlikely to achieve sensing performance approaching that of engineered acoustic sensors.