Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensorless Vector Control of Induction Machine Using High Frequency Synchronous Injection

This paper presents the sensorless vector control of an induction machine in which high frequency signal injection is used to estimate the rotor flux vector’s actual angle. The signal used for these calculations is injected in the estimated synchronous reference frame, from where comes the method’s synchronous attribute. The sensorless vector control is constructed with the most commonly used cascade PI controller structure, both on the flux and the angular speed control paths of the system. This paper focuses the properties of the selected common coordinate system’s angle estimation considering the controls structure. The mathematical background of the method will be detailed, along with the PLL’s tuning, which is used for the estimation.
ParSimTech Research and Training Center

A citizen’s quality of life is always tightly related to the improvement of general technological progress in many subject areas – from mechanical and electrical engineering, information technology, mining and technological fields of industry up to biotechnological processes of cellular and molecular dynamics, medicine, and biomechanics of living organisms. It also depends on an achieved level in theory and mathematical description of the internal dynamic processes of different physical nature, simulation of these processes and further practical implementation of the theory into the real technical objects. An important role here plays computing resources which allow to minimize computational time and to increase the complexity of the simulation models. Parallel computing systems open for scientists a new level of simulation, but they also set high requirements on specific knowledge and experience of the users. As a result, granting access to computing resources is not sufficient, specialised training of the personnel is also needed. The same problem appears during the integration of a computing centre into the university educational program – new courses, workshops and learning materials are required. In this paper, we share our experience and a vision, how modern computing resources could be integrated into university life, what challenges and issues can be expected here. Also, we present a new scientific project between Ukrainian and German academia partners which has a goal to enhance accessibility, usability and improve the system organization of parallel computing resources. The project represents a multidisciplinary research area of Parallel Simulation Technology (ParSimTech) and focuses on the creation of the Ukrainian research and training ParSimTech centre.
Diagnosis of Phytophthora ramorum - evaluation of testing methods.

Phytophthora ramorum est un pathogene recemment decrit qui a conduit a l'introduction d'une reglementation phytosanitaire d'urgence dans la Communaute Europeenne en novembre 2002. Le Departement de l'Environnement, de l'Alimentation et des Affaires Rurales, Inspectorat de la Sante des vegetaux et des semences (PHSI) a initie une prospection en Angleterre et au Pays de Galle a l'automne 2001 pour determiner si l'organisme etait present. En avril 2002, P. ramorum a ete trouve pour la premiere fois au Royaume-Uni et pour la premiere fois en Europe depuis les signalements initiaux aux Pays-Bas et en Allemagne. Suite a cette decouverte, P. ramorum a ete signale principalement sur rhododendrons et viburnums mais aussi sur une large gamme d'autres plantes ornementales a la fois au Royaume-Uni et dans d'autres pays europeens. La gestion efficace de la maladie et la mise en oeuvre de la reglementation phytosanitaire repose sur un diagnostic rapide et precis de la maladie base sur la reconnaissance des symptomes sur le terrain et l'identification de la cause par analyse. D'autres maladies et desordres physiologiques empechent une identification fiable du probleme au moment de l'inspection avec typiquement seulement 20-30% des echantillons soumis se revelant positifs pour une espece de Phytophthora. Cet article decrit l'evaluation d'une PCR en temps reel et une analyse serologique rapide (lateral flow) developpes par le CSL pour detecter P. ramorum et Phytophthora spp. respectivement.
Exploring currency exchange dynamics from a complex network perspective

We analyze the fluctuations of 166 global currencies relative to the Euro over the period 2006–2018. Our analysis of the increases and decreases in average global currency values towards the Euro reveals the approximately 8 year long European recession, with a periodic rise-and-fall characteristic between 2007–2018, with low points of the Euro currency in 2010, 2012, and 2015. Furthermore, we enhance our analysis by creating currency fluctuation networks, for each year, based on the idea that two currencies, with similar evolution towards the Euro, are linked in a weighted undirected graph. Analyzing the obtained graphs (one for each yearly transition, i.e., 20062007 to 2017-2018), we find that the network modularity and average path length are highly correlated $(r = 92.18$% and $r = 88. 25$%, respectively) with the actual average currency fluctuation over the respective period. Our results suggest that network modeling of currency fluctuations reveals underlying properties of the economic landscape, and could be used to enhance our understanding of the complex economic system.
Network Service Mapping and Scheduling under Uncertain Processing Time

This paper proposes an optimization model for the network service mapping and scheduling problem with uncertain processing time. We model processing time uncertainty through Γ-robustness approach, which provides different degrees of robustness against processing time uncertainty. We formulate the problem with the objective to minimize the worst-case makespan over the given uncertainty set. A heuristic is presented to tackle the problem. The numerical results show that the proposed model outperforms the conventional model with deterministic parameters in terms of worst-case makespan.
Optimal grid integration of renewable energy sources with energy storage using dq0 based inverter controller

In the present era, the contribution of renewable energy to energy supply is growing massively and it is replacing the conventional fuels in electricity generation. The intrusion of these Renewable Energy Sources (RES) to the grid has led to the increase in complexity of the system due to the varying nature of the RES. The low rotational inertia and the stability issues are the demanding problems faced in RES connected grid system. Enormous analysis has to be carried out to enhance integration and to provide an efficient and stable energy supply. In order to make this analysis undemanding, several transformations have been developed to transform system components to less-complex models. One such transformation that proves to be efficient is dq0 transformation. This paper presents the mathematical formulation of dq0, which forms the basis for modeling dq0 based system components. For efficient analysis of grid integration, an inverter controller is designed using dq0 quantities for a Solar Photovoltaic (PV) integrated grid. This dq0 transformed controller proves to be more efficient and simpler than the complex conventional controllers. This paper also aims to highlight the importance of energy storage system in maintaining the stability of the grid. The simulation results of PV connected grid system with various Energy Storage Devices manifests the crucial role played by the Energy Storage System in the optimal working of RES integrated grids.
Hungarian method based Resource Allocation Scheme for Vehicular Cloud Networks

The computing resources of a cloud are dispersed geographically in a different servers, vehicles obtain the services through the network. It is important to develop the efficient and cost effective resource allocation schemes in Vehicular Cloud Network (VCN). Innovative schemes are required to allocate the resources so that efficient utilization of resources is obtained in VCN environment. Managing the vehicle demands, minimization of the processing time, reduction of the resource cost are key parameters to be addressed in VCN. In this paper, we propose the Hungarian method based resource allocation scheme in VCN. The objective of the proposed scheme is to effectively provide the computing resources to the task. The proposed mehod scheme optimally allocates the computing resources with significant reduction in the cost. The proposed method improves the resource utilization efficiently with optimization of resource cost.
Prediction of objective function value for heat transfer coefficient function reconstruction by FWA

A parallelised numerical method based on Fireworks Algorithm (FWA) has been developed to solve the Inverse Heat Transfer Problem. The objective function could be minimised by the FWA approach, and it is defined by the deviation of the virtually acquired and the calculated temperatures. To improve the finding the best and efficient solutions the next iteration of FWA would be predicted inside the searching area by the proposed technique.
Modeling and Analysis of Zig-Zag Boost Converter for Battery Charging Applications

In a DC micro-grid (MG), the energy storage system (ESS) plays a vital role in doing the generation-load power management, DC bus voltage control and/or the power smoothening to provide a quality power to the load. In this paper, a new Bi-directional Boost converter topology denoted as Zig-Zag Boost converter (ZZB) is proposed as a charge controller for energy storage system for DC MG. The superiority of the ZZB over the conventional boost converter is that it can provide a comparatively higher gain with a better efficiency for the same duty cycle. Although this topology uses an extra switch and an additional inductor, the voltage and current stress of the switching and magnetic components is reduced, as well as the size of the inductors, resulting in lower overall cost and higher efficiency. Small deviations in the inductors and switches characteristics are perfectly acceptable, as the mismatch current is self-regulated to zero. This paper provides the complete model and an efficiency assessment of ZZB over other common topologies in the literature, while the results are validated via simulations in MATLAB/SIMULINK.
Multi-Level Preemption in TSN: Feasibility and Requirements Analysis

To overcome the limitation of strictly non-preemptive frame transmission in Ethernet networks, the IEEE 802.1Qbu standard was introduced. This standard specifies a one-level frame preemption paradigm wherein, depending on their priority levels, frames are grouped into two categories: namely, the “express frames” and the “preemptable frames”. These two categories are given with the interpretation that (1) only express frames can preempt preemptable frames; and (2) two frames belonging to the same category cannot preempt each other. While this approach partially solves the problem, some preemptable frames can still suffer long blocking periods, irrespective of their individual priority levels. Indeed, there are frames that do not fall into the express frames category, but nevertheless have firm timing requirements that can only be met if they can benefit from preempting lower priority frames. To ameliorate the condition of such frames, we propose a multi-level preemption paradigm. Specifically, we expose the limitations of the one-level preemption approach experimentally; and we present the feasibility and implementation requirements of the multi-level preemption scheme in details.
Analysis and Design of Circular Coil Transformer in a Wireless Power Transfer System for Electric Vehicle Charging Application

This paper analyses the factors influencing wireless power transfer system efficiency for electric vehicle application. Due to complexity in the wireless power transfer transformers, a Finite Element Analysis (FEA) is the key to facilitate the calculation of the mutual inductance and the coupling coefficient. A circular coil transformer model is modelled and simulated in Ansys Maxwell software under different conditions. Simulation results are performed and analyzed. An introduction to WPT transformer model is also presented.
On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop

Machine learning in association with remote sensing has assisted agricultural specialists in monitoring, classification and yield estimation of crops. Tobacco is a major taxable crop of Pakistan, however the existing traditional methods for its monitoring and yield estimation are not only expensive and time consuming but also have limitations in terms of accuracy of collected data by a large number of diverse human surveyors. Due to the existence of such loopholes in the employed mechanism for tobacco crop monitoring and yield estimation, its illicit growth and distribution is on the rise. In this paper we have established a sophisticated machine learning mechanism for tobacco crop estimation using temporally stacked sentinel-2 satellite’s data of Pakistan. Instead of the conventional approach of using single remotely sensed imagery for the target crop classification, we propose a machine learning based classification algorithm while keeping in view the phonological cycle of the target tobacco crop. Using the proposed mechanism, the temporal variations within the tobacco crop and its association with the variations of other vegetation is considered to improve the classification performance of the employed machine learning algorithm. Furthermore, the impact of stacking the vegetation indices derived from near infrared and vegetation red edge bands of sentinel-2 with the original sentinel-2 datasets, including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Index 45 (NDI45), on the classification performance of the machine learning mechanism is investigated. Ground Truth data for training of our Artificial Neural Networks classifier, was obtained using indigenously developed survey application “GEOSurvey”. Experiments were conducted using our proposed mechanism while considering various input data setups – including single date imagery, temporally stacked datasets based on phonological cycle of tobacco crop and different combinations of NDVI and NDI45 stacking. Our proposed experimental setup consisting of temporally stacked imagery along with NDVI stacking results in the best classification performance of 95.81% with reference to the single date imagery stacked with NDVI and NDI45, with performance gain of 07.32%.
Towards a Transfer Concept from Camera-Based Driver Assistance to the Assistance of Visually Impaired Pedestrians

In order to support visually impaired people in traffic situations, a concept for transferring existing camera-based object detection algorithms from Advanced Driver Assistance Systems (ADAS) to Assistive Systems for the Visually Impaired (ASVI) is in development. This paper summarizes the previous work towards a transfer concept, introduces research categories and according statements, and presents the evaluation of the three so far addressed use cases, namely road background segmentation, crosswalk, and lane detection. Based on a literature review, it is stated that ADAS and ASVI are similarly composed and that there is an overlap in use cases addressed in ADAS and needed in ASVI. The conduction and evaluation of a qualitative interview study makes it possible to determine the exact overlap: Lane, crosswalk, traffic sign, traffic light (state), (driving) vehicle, obstacle, and bicycle detection. Additionally, road background segmentation is considered. For every of these use cases, it has to be established if an adaptation is possible and leads to similar hit rates as the underlying algorithm. This is confirmed for the so far addressed use cases. The hit rates on the tested data are 99.87 % for road background segmentation, 98.64 % for crosswalk, and 97.89 % for lane detection. In the future, the remaining use cases will be examined and the findings will be summarized in a transfer concept.
Data Analytics Architectural Framework for Smarter Radio Resource Management in 5G Radio Access Networks

Along with supporting new business opportunities, data analytics -- understood as the pursuit of extracting meaning from raw data -- will be key to getting 5G radio access networks (RANs) efficiently rolled out and operated. The exploitation of data analytics services within the RAN itself, that is, to empower the operation of the RRM algorithms executed within the RAN nodes, is an incipient area of interest that broadens the more well-established applicability of using data analytics for on-top-of-the-RAN operations. In this respect, this article is an attempt to put a spotlight on this topic by motivating a plausible data analytics architectural framework for data-driven, smarter RRM algorithms.
A OneM2M Intrusion Detection and Prevention System based on Edge Machine Learning

As Internet of Things (IoT) is widely spread and is becoming heterogeneous, a growing number of connected devices are the focus of security threats. Hence, a standardized security strategy seems required. OneM2M [1] is a global standard initiative designed to satisfy the need for a common horizontal platform for the multi-industry M2M/IoT applications. In this paper, we propose an Intrusion Detection and Prevention System (IDPS), for the Service Layer introduced by the oneM2M standard. To our knowledge, it is the first generic IDPS for the oneM2M Service Layer based on Edge Machine Leaning (ML). We will detail, in this work, the strategy of the oneM2M-IDPS. Moreover, we investigate the performance of ML algorithms on the oneM2M generated dataset to choose the best ones for our IDPS. Since we are in the context of tiny devices (IoT), we pay attention in our experiments to the features dimension reduction in ML and thus, to the size of trained models.
Patterns of phylogeography and vicariance of Chamaerops humilis L. (Palmae)

Chamaerops humilis L. is 1 of 2 native palms occurring in Europe and the only native palm in the West Mediterranean region. Our aims were: (1) to describe its phylogeographic structure; (2) to infer a biogeographic scenario to explain its present distribution; and (3) to assess changes in its distribution from the last interglacial period. Twenty-two populations were sampled. An amplified fragment length polymorphism analysis produced 226 fragments, which allowed recognition of 4 groups of populations: (1) E Iberian Peninsula plus Al Hoceima (NE Morocco), the Balearics, and Sardinia; (2) France, the Italian Peninsula, and Sicily; (3) SW Iberian Peninsula and NW Morocco; and (4) S Morocco (var. argentea Andre; the first 3 groups are currently included in var. humilis). The phylogenetic information and molecular clock-related estimates, combined with geological and fossil history from the Eocene to present, suggest that C. humilis occurred in Central Europe in the Tertiary, reaching (1) Spain and (2) Italy, with expansion from here across North Africa to (3) N Morocco and S Spain, and (4) S Morocco. Climatic changes may also help explain the fragmented current distribution of this species. The groups of populations are sufficiently genetically distinct to recommend conservation of at least some populations in each region.
Cost Based Optimal Dynamic Economic Dispatch with Wind Integration

In the Energy management System (EMS), economic dispatch is an important issue. Economic dispatch optimizes the per unit cost of generation. The economic operation of thermal plants with wind integration is proposed in this paper. Dynamic economic dispatch (DED) problem has been solved using quadratic constrained programming by CPLEX solver in General Algebraic Modelling System (GAMS) software. The effect of wind integration has been studied on the economic operation of thermal units considering first test system. First Test system consists of three thermal units and second test system consists of six thermal units. A day ahead wind forecasting and demand forecasting have been considered for analysis. Finally first test system with wind integration for a day ahead has been taken to illustrate the effectiveness of proposed method to solve DED with wind integration.
Dissecting the CUDA scheduling hierarchy: a Performance and Predictability Perspective

Over the last few years, the ever-increasing use of Graphic Processing Units (GPUs) in safety-related domains has opened up many research problems in the real-time community. The closed and proprietary nature of the scheduling mechanisms deployed in NVIDIA GPUs, for instance, represents a major obstacle in deriving a proper schedulability analysis for latency-sensitive applications. Existing literature addresses these issues by either (i) providing simplified models for heterogeneous CPUGPU systems and their associated scheduling policies, or (ii) providing insights about these arbitration mechanisms obtained through reverse engineering. In this paper, we take one step further by correcting and consolidating previously published assumptions about the hierarchical scheduling policies of NVIDIA GPUs and their proprietary CUDA application programming interface. We also discuss how such mechanisms evolved with recently released GPU micro-architectures, and how such changes influence the scheduling models to be exploited by real-time system engineers.
Integrating Smart Production Planning Into Smart Production

Industry 4.0 R&D is currently driving the emergence of Smart Production Environments (SPE) where the manufacturing of new products takes place very flexibly and dynamically in several steps and locations, possibly distributed all over the world. Companies involved in such smart productions act in highly competitive global markets and always have to find new ways to, e.g., cut costs by changing to the best providers, or enabling, e.g., tax savings, to stay competitive. This can only be achieved by using the fastest, most effective, efficient, and flexible distributed collaborative production processes. In this context, a Collaborative Adaptive Production Process Planning (CAPPP) can be supported by semantic product data management approaches enabling production-knowledge representation, utilization, curation, and archival as well as knowledge sharing, access, and reuse in many flexible and efficient ways. To support such scenarios, semantic representation of production knowledge integrated into a machine-readable process formalization is a key enabling factor for sharing such explicit knowledge resources in cloud-based production knowledge repositories. Our approach of Knowledge-Based Production Planning (KPP) introduces such a method and already provides a corresponding prototypical Proof-of-Concept implementation. In this way, it can not only be used in production planning but also, e.g., in Collaborative Manufacturing Change Management (CMCM) as well as, e.g., in Collaborative Assembly-, Logistics- and Layout Planning (CALLP) both building on the results of CAPPP. Therefore, a collaborative planning and optimization from mass to lot-size one production in a machine readable and processable representation will be possible. On the other hand, KPP knowledge can be shared to support other types of innovation, collaboration, and cocreation within a cloud-based semantic production knowledge repository.
Probabilistic Schedulability Analysis for Real-time Tasks with Precedence Constraints on Partitioned Multi-core

The design of embedded systems is facing the explosion of new functionalities requiring increased computation capacities and, thus, the introduction of multi-core processors. Moreover, some functionalities may impose precedence constraints between the programs implementing them. In this paper, we consider partitioned scheduling of tasks with precedence constraints defined by multiple Directed Acyclic Graphs (DAGs). The variability of execution and communication times is taken into account by describing them with probability distributions. Our probabilistic response time analysis is validated on random generated task sets and on a PX4 drone autopilot.