Miracle-dz/UniFAR-FTUs huggingface dataset:
A dynamical multi-input/multi-output network formulation for stability analysis in AC microgrids

Nowadays, we have witnessed a significant increase of renewable sources across the existing power grids as a result of the undeniable need to mitigate the effects of environmental pollution and to assist the economic development of the countries. However, due to the lack of rotational inertia and their intermittent nature, renewables have increased the severity of the occurring disturbances and instability phenomena, which in turn, place a demand for more accurate modeling of the grid and the power system components. In this paper, we propose a novel network formulation for AC microgrids that allows the consideration of less conservative conditions for the network whilst facilitating the adoption of higher order inverter-based generation dynamics and the stability analysis and control. In particular, we formulate the network as a dynamical multi-input/multi-output system, which we show is both stable and passive, even when the network's dynamic and lossy nature is considered. Then we present the opportunities and the advantages offered by the proposed framework and provide useful guidelines for a more accurate modeling of power system components. Finally, we verify the proposed network formulation through a numerical application on the Four Machine Two Area Kundur test system and a dynamic simulation on typical Medium Voltage (MV) distribution lines.
A Twice Subtraction Method for Obtaining LOI in Pulsed Eddy Current Signals of Ferromagnetic Samples

Lift-off point of intersection (LOI), as a desirable signal feature independently of the lift-off effect, is expected to be applied in pulsed eddy current (PEC) testing of ferromagnetic samples. The time-domain LOI in PEC signals is easily recorded and easier to be extracted for evaluating ferromagnetic samples, compared with the frequency domain LOI in spectral PEC signals. However, the time-domain LOI cannot be obtained in the PEC signals of ferromagnetic samples. In this article, a twice subtraction method was proposed to obtain an LOI feature in PEC signals of ferromagnetic samples for evaluating the sample thickness, which is verified through simulations and experiments. In the twice subtraction method, the first subtraction is performed between PEC signals from the sample and air to obtain a difference in the PEC signal. The secondary subtraction is implemented between the difference in the PEC signal and its standard deviation. As a result, an LOI can be extracted from the twice subtraction signals when only lift-off distances are introduced. The results demonstrate that the twice subtraction method is a feasible program to obtain an LOI in twice subtraction signals of ferromagnetic samples, and the obtained LOI has the potential to be used to measure the thickness of ferromagnetic samples without lift-off effect.
Impact of Imperfect Channel Estimation in HF OFDM-MIMO Communications

Multiple Input Multiple Output (MIMO) technology offers the possibility of increased throughput in wireless communications, and as such, it is a potential candidate for High Frequency (HF) systems where only small bandwidths are available. However, the increased wavelength and size of antenna elements complicate the implementation of HF multi-antenna transceivers. In this paper, we consider multi-carrier (OFDM) MIMO HF communication systems where the multiple non co-located antennas at each communication end are interconnected through Radio Frequency (RF) links. We consider Minimum-Mean-Squared-Error- (MMSE) pilot-assisted channel estimation at the receiver side in OFDM transmission, and, leveraging results from random matrix theory, we present novel upper and lower bounds on the achievable throughput in the presence of imperfect channel estimation. Our representative numerical results for a 9 × 9 HF MIMO system quantify the achievable rate for realistic HF channel parameters, validating the interest in multi-antenna systems.
Morphological, Structural, and Dielectric Properties of Thermally Aged AC 500 kV XLPE Submarine Cable Insulation Material and Its Deterioration Condition Assessment

Cross-linked polyethylene (XLPE) has been widely used as insulation material for cables. In 2019, the highest voltage level AC 500 kV XLPE submarine cable has been operated in Zhoushan, China. The irreversible degradation poses a safety hazard to the operation of submarine cable. Therefore, it is necessary to investigate the physical, chemical and electrical properties of XLPE submarine cable insulation under aging condition and explore the method for its deterioration degree assessment. In this paper, the AC 500 kV XLPE submarine cable insulation material was thermally aged at 130 °C. The deterioration characteristics of materials were analyzed based on morphology, chemical structure, mechanical property, thermal property and dielectric properties. Multiple characteristic parameters were extracted and new deterioration condition assessment model was established. Results show that the physicochemical characteristic parameters including retention rate of elongation at break, carbonyl index (CI) based on FTIR, full-width at half-maximum (FWHM) of the diffraction peak based on XRD, melting enthalpy based on DSC analysis, and the dielectric characteristic parameters including modified Cole-Cole model parameter $\chi _{\mathrm {s\alpha }}$ and AC breakdown strength are sensitive to deterioration condition of XLPE material. According to the change of elongation retention at break, the deterioration condition of XLPE sample was divided into enhanced stage, wear-out stage and disposal stage. The new multi-factor deterioration condition assessment model including six characteristic parameters (retention rate of elongation at break, CI, FWHM, melting enthalpy, modified Cole-Cole model parameter $\chi _{\mathrm {s\alpha }}$ , AC breakdown strength) was successfully established by grey correlation analysis. The new model can reflect the property of mechanical, chemical, thermal and dielectric property and has prominent effect in differentiating deterioration degree of the AC 500 kV XLPE submarine cable insulation material.
Brain-in-Car: A Brain Activity-based Emotion Recognition Embedded System for Automotive

Emotional distress during driving can greatly affect the safety and comfort of the driver. Being able to detect and react to the emotions of the driver would greatly improve in-car safety. It could also be utilized in a variety of different applications to improve the driving experience. In this paper, we introduce a brain signal-operated emotion recognition system that is specifically tailored for the Automotive Open System Architecture (AUTOSAR) framework. The proposed system acquires brain electroencephalography (EEG) signals of the driver, identifies the underlying emotion using machine learning techniques, and feeds that emotion into the car system where different car components can react to that input. Our results demonstrate the ability of the system to recognize two emotions, namely sadness versus happiness, from the recorded EEG with a mean accuracy of 89.7% across three examined subjects using subject-dependent data. Moreover, when training the system using data recorded from multiple subjects, a mean accuracy of 91.7% is achieved. Taken together, these results indicate the ability of the proposed approach to discriminate between sadness and happiness whose extreme expression could have a significant impact on driving behavior.
An Architecture for Situation-Aware Smart Logistics

Disruptions and exceptions are an important source of risks in logistics, as far as the planning of transportation services is concerned. Failing to rapidly react on and handle such events may lead to serious depreciation of the transported cargo and reputation damage. The Internet of Things seems to be the technology capable of providing the tools required to detect exceptions nearly real-time. However, currently, there is little research on how to enhance the detected exceptions with related information from internal or external sources. Furthermore, most exception detection capabilities rely on experience and not much research exist on how to improve the accuracy of using third-party knowledge. In this paper, we propose a reference architecture for situation-aware logistics. The architecture specification follows the key principles derived from an extensive requirements analysis, the state of the art literature, and the ideas promoted by the Industrial Data Space initiative. The proposed architecture has been instantiated and tested by means of a prototype designed for the case of temperature-controlled transportation services.
Lip-Reading with Limited-Data Network

The development of Automatic Lip-Reading (ALR) systems is currently dominated by Deep Learning (DL) approaches. However, DL systems generally face two main issues related to the amount of data and the complexity of the model. To find a balance between the amount of available training data and the number of parameters of the model, in this work we introduce an end-to-end ALR system that combines CNNs and LSTMs and can be trained without large-scale databases. To this end, we propose to split the training by modules, by automatically generating weak labels per frames, termed visual units. These weak visual units are representative enough to guide the CNN to extract meaningful features that when combined with the context provided by the temporal module, are sufficiently informative to train an ALR system in a very short time and with no need for manual labeling. The system is evaluated in the well-known OuluVS2 database to perform sentence-level classification. We obtain an accuracy of 91.38% which is comparable to state-of the-art results but, differently from most previous approaches, we do not require the use of external training data.
Effective Fingerprint Extraction and Positioning Method Based on Crowdsourcing

At present, WiFi fingerprinting indoor positioning technology is a research hotspot, and the construction of a radio map based on crowdsourcing can significantly reduce the amount of labor required. However, when users collect fingerprint information using crowdsourcing data in practical applications, the received signal strength (RSS) information collected by user phones updates slowly and will remain the same for a certain distance and time. Therefore, crowdsourcing data is inaccurate, and a radio map thereby established will also lead to inaccurate indoor positioning. In order to address this issue, this paper proposes a method for extracting effective RSS information from crowdsourcing data in order to establish the radio map and achieve positioning. By analyzing the crowdsourcing data characteristics, effective RSS information is identified and extracted. Based on inertial sensors data, the position of effective RSS information is determined. The effective crowdsourcing data associated with the position is weighted and fused in order to establish the radio map. Aimed at the issue of the RSS collected by phones updating slowly, a combined positioning method for position calculation is proposed in order to achieve effective positioning. Experiments were conducted in a real environment to validate the algorithm, and the positioning accuracy could reach 1.5 m by using crowdsourcing data to construct the radio map and executing an indoor positioning algorithm, which is close to the accuracy achieved when using manual data collected by professionals at a certain distance and saves a great deal of labor.
Impact of Interface Trap Charges on the Electrical Characteristics of Back-Gated 2D Negative Capacitance (NC) FET

In this letter, a fully analytical compact drain current model of back-gated two-dimensional (2D) negative capacitance (NC) FET including interface trap charges has been developed by solving Poisson's, drift-diffusion and 1-D Landau-Khalatnikov equations, and it is validated against the experimental data. In addition, the impact of interface trap charges on the electrical characteristics of the back-gated 2D NC-FET is investigated systematically based on the model. It is found that the subthreshold swing (SS) and on-off current ratio (Ion/Ioff) are seriously degraded because of the presence of interface traps at 2D channel/oxide interface and 2D NC-FET with a big tf is more immune to the degradation induced by the interface traps. In addition, the underlying physics are elucidated by the capacitance matching of 2D NC-FET.
A Speech Quality Classifier based on Signal Information that Considers Wired and Wireless Degradations

There are many factors that can affect the users' quality of experience (QoE) in Voice over IP (VoIP) services, especially in wireless networks. In speech communication systems there are different impairment factors because physical phenomena that occur in the wired and wireless transmission channel. In this context, a non-intrusive speech quality classifier based on Discriminative Restricted Boltzmann Machines (DRBM) is proposed, which considers speech signals degradations caused by wired and wireless network degradations. To accomplish this goal, a test scenario is implemented, in which the speech signal is coded by the AMR-WB and transmitted using both a lossy wired channel and the Rayleigh fading model. Also, different modulation schemes and channel degradations, such as packet loss rate, signal-to-noise ratio and Doppler shifts are implemented. As a result, a speech database is built that is used to train different machine learning algorithms. Experimental results demonstrated that the DRBM reached the best results. Performance assessment results show that the proposed classifier based on DRBM overcomes the current standardized algorithm described in ITU-T Rec. P.563.
Perspectives for Appliance and Accuracy Improvement of Coordinate Measurements with Laser Technique

This paper describes some practical problems of coordinates measuring concerning both linear and angular measurements. The trade-off between the measuring accuracy and maximum range was pointed out as a central issue in Geometrical Dimensioning and Tolerancing (GD&T) inspection. The opportunity for online measurements for manufacturing industries has also been described. An implementation of the advanced commercial measuring system in relation to GD&T has been demonstrated in this work.
Matrix Converter Based on SVD Modulation Using a Microcontroller as Unique Controlling Device

In this paper, the implementation of a complete set of control algorithms for a three-phase Matrix Converter (MC) is addressed. The control algorithms are implemented on a single Microcontroller Unit (MCU) of the enhanced C2000 Delfino family of Texas Instruments (TI). By a suitable programming sequence, the MCU is capable of fully controlling the MC, which avoids the use of special hardware for assuring a reliable commutation process. The Singular Value Decomposition modulation (SVD) and the four-step current commutation technique, fundamental steps in this proposal, were implemented in the TMS320F28379D MCU device. The optimal algorithmic strategies and a proficient design allow a compact implementation in a single device. For validating this study, extensive numerical simulations were carried out, and an experimental prototype was efficiently implemented. Both kinds of results were discussed and analyzed to demonstrate the effectiveness of the proposed implementation.
Textual Description based Video Annotation Methods

In the Present days, a large number of multimedia significant resources create the browsing, retrieval task. In this survey different types of methods are using, to foster the browsing of large video storage and provides sufficient access to contents. The Video annotation initially cuts the detected video sequence, where the succeeding frames take out to show each shot or frames with a huge difference. The extraction of key frames that shows in every image is compressing the few images by using standard technologies like HOG, fuzzy clustering and Genetic algorithm regarding the textual depiction of the scene. Video Annotation improves the sufficient searching and retrieving of the video components. They are inspired and are sometimes directly taken from image techniques. Thus, numerous varieties of images are easily applied to video annotation.
Speech-Based Stress Classification based on Modulation Spectral Features and Convolutional Neural Networks

Interest in stress recognition has notably increased over the past few years. In this work, we focus on recognizing stress from speech. We propose the use of modulation spectral features as input to a convolutional neural network (CNN) for classifying stress. As benchmark, the OpenSMILE features used in the INTERSPEECH 2010 Paralinguistic Challenge is adopted and evaluated with a support vector machine (SVM) and a deep neural network (DNN) based backends. Experiments are performed with the well-known Speech Under Simulated and Actual Stress (SUSAS) database. Performances are investigated considering 2-class, 4-class and 9-class classification problems. Results show that the proposed approach outperforms the benchmark on a challenging 9-class classification task with accuracy as high as 70% representing gains of roughly 18% over the benchmark.
Convolutional Neural Network for Short-term Wind Power Forecasting

Wind power generation is becoming increasingly relevant to the power supply system as it is clean and renewable. This paper proposes a novel methodology for short-term wind power forecasting, based on a convolutional neural network (CNN). In this work, we evaluate the CNN ability of predicting the wind power generation by comparing it to two benchmarking methods – ARIMA and gradient boosting machine (GBM). We prove that CNN is well suited for this purpose, outperforming the other tested techniques, specially when the prediction horizon is greater than 1-hour. Besides, this paper shows that additional features like meteorological forecasts provide fruitful information, powering the CNN performance.
A Two-Stage Relay Selection and Power Allocation Algorithm for NOMA-Based Multicast Cognitive Radio Networks

In this paper, the problem of joint relay selection and power allocation (J-RS-PA) for NOMA-based multicast cognitive radio networks is considered. In particular, the aim is to simultaneously maximize the SINR/SNR of the primary and secondary transmitter-receiver (TR) pairs, subject to quality-of-service (QoS) constraints. Communication between the primary and secondary TR pairs is performed over two-phases, namely, the broadcasting phase, and the cooperation phase. In the broadcasting phase, the primary and secondary transmitters broadcast their data symbols to the intermediate relays; while in the cooperation phase, the selected relay forwards the decoded symbols to their intended receivers. However, the J-RS-PA problem happens to be non-convex (i.e. computationally-prohibitive). Alternatively, an optimal low-complexity two-stage relay selection and power allocation (TS-RS-PA) algorithm is devised, which is based on the solution of intelligent linear programming problem formulations. Simulation results are presented to validate the proposed TS-RS-PA algorithm, which has been shown to yield the optimal SINRlSNR values for the primary and secondary TR pairs in comparison to the J-RS-PA scheme, but with lower computational complexity, while satisfying QoS constraints.
Consistency of Multi-Agent Systems with Lur'e Nonlinearity in a Directed Switching Topology

This paper study a directed topology. Compared with the conclusions of the existing undirected topology, the requirements of the communication topology are greatly reduced and more general. The designed control protocol only utilizes the information of the neighbor agents. In order to convert the consensus problem under the switching topology into a stability problem of a low-dimensional switching system, the special property of the directed graph Laplacian matrix is used to decompose it, then use the linear transformation in a particular form. Then the Lyapunov function method combined with the linear matrix inequality method is used to analyze the stability of the system. The feedback matrix of the dynamic consensus controller is given, and the average dwell time condition is obtained. The results show that the proposed method can achieve the consensus of the Lur'e type nonlinear multi-agent system under arbitrary switching topology conditions.
Design-Time Safety Assessment of Robotic Systems Using Fault Injection Simulation in a Model-Driven Approach

The rapid advancement of autonomy in robotic systems together with the increasing interaction with humans in shared workspaces (e.g. collaborative robots), raises pressing concerns about system safety. In recent years, the need of model-driven approaches for safety analysis during the design stage has gained a lot of attention. In this context, simulation-based fault injection combined with a virtual robot is a promising practice to complement traditional safety analysis. Fault injection is used to identify the potential safety hazard scenarios and to evaluate the controller's robustness to certain faults. Besides, it enables a quantitative assessment w.r. t. other techniques that only give qualitative hints, such as FMEA. Thus, it facilitates the refinement of safety requirements and the conception of concrete mitigation actions. This paper presents a tool-supported approach that leverages models and simulation-assisted fault injection to assess safety and reliability of robotic systems in the early phases of design. The feasibility of this method is demonstrated by applying it to the design of a real-time cartesian impedance control system in torque mode as a use case scenario.
GEO-Trust: Geo-aware security protocol for enabling cross-border trustable operations and data exchange in a global digital economy

Security and trustability in Internet is a baseline problem that continues arising as far as digitalization and data economy are taking place. It is well-known the value of data in the current digital age and social media economy. Consequences of this has produced a digitally-enabled data capture for different business domains and research. Nowadays, there is a lack of trustability for sharing data. In addition, legal regulations about data protection such as Swiss Data Protection Act (DPA), which is being updated and influenced by GDPR’s European Union principles are defining a more restrictive regulation about data usage, data geo-location and data sharing which are crucial to enable a data economy. Therefore, key challenges need to be addressed for guarantying a safe and regulations compliance flow of data (cross-borders). GEO-TRUST is developing techniques and algorithms that promote regulation of data exchange, trustability, consent management, reputation and security as contribution for the emerging Data Economy Ecosystem. In details, GEO-TRUST project proposes an innovative protocol called Proof of Offset (POO) to enable a higher control and limit data access by geo-location, accountability, data exposition minimization, data semantic annotation that guarantee cross-domain data re-use and higher awareness about data protection. This work presents the key concepts and initial results behind this technology being developed under the support of Hasler Foundation in Switzerland.