Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Detection of Gearbox Damage, Development of ANN Forecast Maintenance Tool

Wind turbine maintenance management worldwide has received more attention, with efforts towards reducing the costs of maintenance and downtime. The kinetic energy from the wind is converted into electric power with certain efficiency, the loss of energy is dissipated as heat through radiation, conduction and convection through the oil cooling system, on the drivetrain power, and especially on the gearbox. Data monitoring and analysis based on a continuous measurement from the wind turbine Supervisory Control and Data Acquisition (SCADA) is used for the development of an Artificial Neuronal Network (ANN).
A Novel Asymmetrical 21-Level Inverter for Solar PV Energy System With Reduced Switch Count

This article presents a novel asymmetrical 21-level multilevel inverter topology for solar PV application. The proposed topology achieves 21-level output voltage without H-bridge using asymmetric DC sources. This reduces the devices, cost and size. The PV standalone system needs a constant DC voltage magnitude from the solar panels, maximum power point tracking (MPPT) technique used for getting a stable output by using perturb and observe (P&O) algorithm. The PV voltage is boosted over the DC link voltage using a three-level DC-DC boost converter interfaced in between the solar panels and the inverter. The inverter is tested experimentally with various combinational loads and under dynamic load variations with sudden load disturbances. Total standing voltage with a cost function for the proposed MLI is calculated and compared with multiple topologies published recently and found to be cost-effective. A detailed comparison is made in terms of switches count, and sources count, gate driver boards, the number of diodes and capacitor count and component count level factor with the same and other levels of multilevel inverter and found to be the proposed topology is helpful in terms of its less TSV value, devices count, efficient and cost-effective. In both simulation and experimental results, total harmonic distortion (THD) is observed to be the same and is lower than 5% which is under IEEE standards. A hardware prototype is implemented in the laboratory and verified experimentally under dynamic load variations, whereas the simulations are done in MATLAB/Simulink.
Performance Analysis of Sales Big Data Processing using Hadoop and Hive in Cloud Environment

Nowadays, big data gains much attention from academics and IT industries. This is due to the extraordinary current growth of data that must be accompanied by a variety of qualified data storage and processing techniques to overcome the5 V’s challenge of big data. This research is aimed to conduct a performance analysis of big data processing. The sales data will be processed in a parallel scheme on the cloud server and then managed using Hadoop and hive. The research shows that the more VMs used, the lower processing time needed, but this is inversely proportional to the CPU time required. Whereas, from the side of block size testing the research result shows that the decrease in the time of query execution is very visible by the change in the use of block size from 2MB to 4MB and 8MB, but the change in the blocksize size from 4MB to 8MB does not significantly affect the speed of query execution.
AR40ER: A Semantic Platform for Open Educational Augmented Reality Resources

Tablet computers are gaining in presence in modern-day classrooms, enabling the use of a variety of apps for purposes such as note-taking or assessment. Augmented Reality (AR) experiences in the classroom, made possible by current hardware, permit new ways of interaction and visualization, as well as increase student motivation and engagement. They also overcome the need for potentially expensive hardware required for experiments in certain scientific domains. The movement of Open Educational Resources (OER) has enabled the sharing of heterogeneous learning resources. Their retrieval can be improved by enriching their metadata using Semantic Web technologies. In this paper, we present AR40ER, a semantic platform for heterogeneous AR experiences provided as OER. We showcase four AR scenarios from different school subjects. These scenarios can be integrated through a lose coupling in third-party apps. Apart from describing how this integration works, we demonstrate how a note-taking app can benefit from these scenarios.
Consensus of Discrete-Time Multi-Agent Systems with Markovian Packet Dropouts*

This paper investigates the consensus problem for discrete linear multi-agent systems (MASs) in the case of Markovian packet dropouts. The agent subsystems are connected by lossy channels, which can be described by Markov process. The relationship of agent dynamics, communication network, and packet losses rate is studied, and based on which the MASs can achieve consensus by a designed distributed controller. Specifically, based on the stability of a transformed system state function under the identical packet looses, we first derive the sufficient condition of consensus for discrete-time MASs, and meanwhile design the control gain. Then, we also discuss the sufficient condition of consensus for the MASs under nonidentical packet losses. At last, the availability of the proposed results is illustrated through simulation examples.
A 3D-CNN Classifier for Gender Discrimination from Diffusion Tensor Imaging of Human Brain

Research on medical image recognition by machine learning has been studied for a long time. Medical image recognition has been mainly for detection of diseases or symptoms. In recent years, there is a hypothesis which human health conditions and personal attributes affect brain structure. Most studies focus on T1-weighted images, however T2-weighted and diffusion-weighted images are also used in medical practice. Diffusion images contains signals and information which are possibly used to discriminate human health or other state rather and are different from T1-weighted or T2-weighted images. Some studies using 3D-CNN (3-dimensional convolutional neural network) have been applied for brain structural magnetic resonance imaging such as T1-weighted images, but diffusion tensor images cannot be directly input to 3D-CNN because their data is represented as the tensor of the movement of water molecules. Therefore, in this research, we propose 3D-CNN which input the diffusion images of each direction to different channel. We construct multi-channel 3D-CNN and verify the effect of brain structure image mode on gender discrimination using T1, T2, and DTI.
Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization

Gas source localization tackles the problem of finding leakages of hazardous substances such as poisonous gases or radiation in the event of a disaster. In order to avoid threats for human operators, autonomous robots dispatched for localizing potential gas sources are preferable. This work investigates a Reinforcement Learning framework that allows a robotic agent to learn how to localize gas sources. We propose a solution that assists Reinforcement Learning with existing domain knowledge based on a model of the gas dispersion process. In particular, we incorporate a priori domain knowledge by designing appropriate rewards and observation inputs for the Reinforcement Learning algorithm. We show that a robot trained with our proposed method outperforms state-of-the-art gas source localization strategies, as well as robots that are trained without additional domain knowledge. Furthermore, the framework developed in this work can also be generalized to a large variety of information gathering tasks.
Potential induced degradation (PID): Review

Potential induced degradation (PID) is a problem that in recent years has been the focus of research and studies on the performance of the photovoltaic module (PV) under field conditions, in view of the consequences caused by this degradation. Even with extensive material, the understanding of the PID phenomenon is still incomplete, but it must be taken into account that technological and environmental diversity are factors that imply in reversal techniques. This article aims to conduct a critical review in order to provide an overview and broad view of the literature available to promote understanding of the current state of PID research. The role is to present the definitions of the PID mechanism supported by scholars and researchers as well as the influence of temperature, humidity and voltage on the progression of the PID and the detection and reversal methodologies and preventive measures in PV c-Si modules.
Model Construction based on Variable Selection of Indicators and Alternative Data in Market Forecasting

In the conventional exchange rate forecasting methods, it was rare to consider both market internal factors and market external factors. As a result, some important factors that cause price movements cannot be fully grasped, and problems will occur. Therefore, in this paper, we consider an analytical method for the purpose of developing a financial market forecasting method that considers not only market internal factors but also market external factors.
Verbal and Non-Verbal Features in Deception Detection Systems

Deception detection system is widely applied in different areas that are related to security, hiring new employees for business, criminal investigation, law enforcement, terrorism detection and others. There are different cues or signals that are used in implementing algorithms for deception detection. These cues are either verbal or non-verbal. The verbal cues mean the signal that extracted from speech, while non-verbal features related to facial expressions, eye gaze, head moments, eye blinking and other body gestures. The non-verbal features are so important and widely applied in recent studies. These features also achieve acceptable accuracy. While verbal features are less applied than non-verbal and achieve less detection accuracy.
Research on fat-tail phenomenon via artificial stock market modeling

As a typical stylized fact in stock market, fat-tail phenomenon has been widely concerned and applied to risk management practice, while the mechanism of its production has been a mystery for a long time. Based on the discussion of the modeling paradigms of the emerging agent-based computational finance, classical finance and behavioral finance, this paper deems that the later two models cannot study fat-tail phenomenon. The agent-based computational finance model has great potential, but its ability to solve financial problems is impeded by the confused modeling solutions. In this paper, an agent-based modeling approach for computational finance is presented. According to the new approach, a simple and analysable model is established. The evidence that market microstructure can produce fat-tail phenomenon has been found, and its mechanism has been analyzed. Results and conclusions of this study will influence ideas of applying fat-tail phenomenon to risk management.
Analysis and Design of a 35-GHz Hybrid π-Network High-Gain Phase Shifter With 360° Continuous Phase Shifting

This paper presents the analysis and design of a 35-GHz high-gain phase shifter with 360° continuous phase shifting. To enhance the phase shift range, a hybrid <inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula>-network realized by combining the electrical tuning through capacitors and magnetic tuning through transformers is developed. The phase shift modules are inserted between the vertically stacking transistors to achieve the embedded phase shifting with the minimum loss. Furthermore, the Gm stages offer additional signal gain to suppress attenuation in the passive phase shifter. The capacitive neutralization technique is utilized to further increase the gain and enhance stability. This prototype, fabricated in a 28-nm CMOS process, demonstrates a 360° continuous phase shift with a maximum gain of 25.6 dB and a minimum noise figure of 4.1 dB. It consumes 26-mW power with a supply voltage of 0.9 V and 1.25 mm <inline-formula> <tex-math notation="LaTeX">$\times0.75$ </tex-math></inline-formula> mm chip area.
Transmission Power Rate Control for EHD With Temporal and Complete Deaths

This paper proposes a continuous-time communication model of an energy harvesting device (EHD) in the scenario that such EHD suffers from the temporal death caused by the energy depletion and completion death caused by the destruction of its hardware or software, respectively. The harvested energy is modeled as continuous fluid process, it arrives continuously and varies in time. The data is assumed to be infinite backlog, and the EHD transmits it via a wireless channel fluctuates randomly due to fading. We reformulate the system model into an equivalent piece-deterministic Markov process (PDMP) based on the imbedded discrete-time decision epoch sequence of the system model, and then an infinite horizon discrete-time Markov decision process (MDP) is built. We show the existence of the stationary deterministic optimal transmission power rate (TPR) policy, and an algorithm for computing the TPR policy and the maximum total expected throughput is developed. Finally, numerical examples are provided to confirm the analytical findings. The effects of some system parameters to the optimal TPR policy and the maximum expected throughput are investigated numerically.
Eliminating the Residual Ultraviolet Excitation Light and Increasing Quantum Dot Emission Intensity in LED Display Devices

Ultraviolet light-emitting diodes (UVLEDs) used as excitation sources for quantum dots (QDs) are promising candidates for the display field. To eliminate the residual excitation light, the conventional method is simply increasing the optical density of QDs for packaging, which would significantly reduce the luminous efficiency of QD-light-emitting diode (LED) devices owing to reabsorption loss. In this study, the combination strategy of a boron nitride (BN) reflective coating and an ultraviolet-reflection filter (UV-R filter) for QD-LED devices is proposed. By managing the recycle of UV light to excite more QDs and reducing the reabsorption effect of QDs carefully, their green emission intensity of LED using such a strategy is 82.0% higher than that of a conventional device with a similar QD light energy proportion of ~99.0% (almost no residual UV excitation light). This strategy is also effective for red QD-LED devices while eliminating residual UV excitation light and enhancing the red emission intensity. Therefore, this study provides an effective way to develop QD-LED devices with high QD emission intensity and low UV light leakage for display.
Opening of the Italian Ancillary Service Market to Distributed Energy Resources: Preliminary Results of UVAM project

The present paper deeply analyzes the main features and the preliminary results of the UVAM project: a pilot project launched in 2017 by the Italian Regulatory Authority that aims to investigate the technical and economic feasibility of the provision of ancillary services by Distributed Energy Resources (DERs), controllable loads, energy storage systems and e-mobility. Indeed, the rising of renewables and DERs penetration in the power system recently called for the enlargement of the pool of actors that can actively participate in the grid balancing. The UVAM project represents the first opening of the Italian Ancillary Service Market to distributed resources and, for the first time, a capacity remuneration is also added to remunerate the provision of ancillary services. The results for the period November 2018 - March 2020 are analyzed both from technical and economic perspective, highlighting the main difficulties faced in the experimentation, while trying to interpret the behavior adopted by Aggregators in this first phase of the project.
New hermetic sample holder for radioactive materials fitting to Siemens D5000 and Bruker D8 X-ray diffractometers: application to the Rietveld analysis of plutonium dioxide

A new hermetic sample holder to be used with radioactive or air- and moisture-sensitive samples has been developed; it has been designed to fit most of the commercial Siemens/Bruker diffractometers (e.g. D5000 and D8 series). Thanks to the design of the sample holder and to a sample preparation process allowing two-containment-barrier protection, X-ray data can be collected using a standard uncontaminated diffractometer mounted in a Bragg–Brentano geometry. The design offers very accurate and reliable sample positioning. In order to demonstrate the high quality of the data obtained, the Rietveld analysis of plutonium dioxide is presented. Good agreement between the refinement and published data demonstrates the quality of the sample preparation and the accuracy of the sample holder. High-quality X-ray diffraction powder patterns can be recorded for use in Rietveld refinements, even on highly absorbing radioactive materials.
Multimodal Classification of Emotions in Latin Music

In this study we classified the songs of the Latin Music Mood Database (LMMD) according to their emotion using two approaches: single-step classification, which consists of classifying the songs by emotion, valence, arousal and quadrant; and multistep classification, which consists of using the predictions of the best valence and arousal classifiers to classify quadrants and the best valence, arousal and quadrant predictions as features to classify emotions. Our hypothesis is that breaking the emotion classification in smaller problems would reduce complexity and improve results. Our best single-step emotion and valence classifiers used multimodal sets of features extracted from lyrics and audio. Our best arousal classifier used features extracted from lyrics and SMOTE to mitigate the dataset imbalance. The proposed multistep emotion classifier, which uses the predictions of a multistep quadrant classifier, improved the single-step classifier performance, reaching 0.605 of mean f-measure. These results show that using valence, arousal, and consequently, quadrant information can improve the prediction of specific emotions.
Object Recognition to Refine Drone Positioning

This article is about using object recognition to more accurately position drones. A description is given of using an autopilot with a Kalman filter to reduce the error in positioning the drone. The use of a quaternion for positioning a drone is described. The implementation of the marker recognition module based on the calculation of moments using the OpenCV library and the Python programming language is described, which allows to find the offset relative to the marker, as well as allowing to calculate the positioning error over time. This allows you to synchronize the data received from the camera and autopilot to reduce the error in positioning the drone.
High Q-Factor PCB Embedded Flip-Chip Inductors With Multilayer CZTB Magnetic Sheet for Power Supply in Package (PwrSiP)

This article investigates a novel approach to the integration of multilayer, thin film magnetics embedded in printed circuit board (PCB) for Power Supply in a Package (PwrSip) applications. A solenoid inductor structure is formed by sandwiching a multilayer CZTB magnetic sheet between two distinct PCBs with copper tracks on each which form the upper and lower windings of the device. A solder reflow process utilizing solder balls mounted on the exposed NiAu pads of one of the PCBs is used to create the electrical connection between the two boards, hence, forming closed current loops for the windings of the inductor. This novel inductor structure enables the use of about 4 oz (<inline-formula> <tex-math notation="LaTeX">$140~\mu \text{m}$ </tex-math></inline-formula>) of copper to enclose the multilayer CZTB magnetic sheet, and hence demonstrates very low <inline-formula> <tex-math notation="LaTeX">$R_{\text {dc}}$ </tex-math></inline-formula>. The multilayer CZTB magnetic sheet is characterized by using a Ryowa permeameter. The magnetic sheet exhibited a permeability of 367. The manufactured flip-chip inductor was electrically characterized by using a vector network analyzer (VNA). The device has a footprint of 7.25 mm<sup>2</sup> and has an inductance of 52 nH. The fabricated PCB embedded inductor achieved a peak quality factor of 23 at 40 MHz.
Poster: Vggish Embeddings Based Audio Classifiers to Improve Parkinson's Disease Diagnosis

The absence of highly predictive and readily applicable biomarkers for Parkinson's disease (PD) significantly hinders the diagnosis and subsequent monitoring of the condition. Since up to 90% of PD patients exhibit speech aberrations, however, the use of patient voice as a rapid diagnostic measure has shown significant promise. Past research towards creating voice-based automated diagnostic tools has relied on expert handcrafted audio feature sets that capture patient articulation, phonation, and prosody properties. Not only is there a limited consensus on the ideal contents of a PD audio diagnostic feature set, but also manually selected features may not fully exploit the predictive power of the underlying data. In this study, we demonstrate the benefit of employing VGGish embeddings, a more generalizable and higher throughput feature extraction strategy, for voice-based PD diagnosis. Our top VGGish-based model achieved 87% accuracy for detecting PD and significantly outperformed models trained on multiple handcrafted feature sets, a mel-frequency cepstral coefficient set, as well as an ImageNet pretrained convolutional neural network extraction strategy. VGGish models were also highly competitive with clinically determined UPDRS III–18 speech deterioration ratings for PD diagnosis. These results demonstrate the potential of VGGish embeddings for creating fast and accurate voice-based PD classification models.
Materials characterisation and cytotoxic assessment of strontium-substituted bioactive glasses for bone regeneration

The regeneration of bone lost due to disease and trauma as well as the bonding of bone to biomedical implants present significant challenges to the field of biomaterials. Increasing evidence for the benefit of strontium as a potent anti-osteoporosis therapy suggests that biomaterials which can release strontium to the surrounding environment may find a range of uses in orthopaedic, spinal and dental surgery to regenerate hard tissue. Here we report on the structure and physical properties of a series of ten glasses based on 45S5 Bioglass®, where 0 to 100% of the calcium was substituted with strontium on a molar basis. Characteristic temperatures were determined using differential thermal analysis. We also analysed the structure of the glasses with Raman spectroscopy and assessed their cytotoxicity using a human osteosarcoma cell line. All characteristic temperatures (except crystallisation onset, Tx) decreased with strontium addition to the glass. Glass molecular structure did not change across the series and the silicate network was predominantly composed of linear chains (Q2) and a small amount of (Q3) with isolated orthophosphate units (Q0). The phosphate was present as [PO4]3− orthophosphate units (Q0) in all glasses. Immersion of the Sr-glasses in simulated body fluid (SBF) showed formation of biomimetic apatite in 1 week or less. Cell proliferation assays demonstrated that all the glass compositions supported normal cell attachment and proliferation. In summary, we show that strontium can be added to bioactive glasses in place of and in combination with calcium with minimal alteration to glass physical properties. These strontium-substituted bioactive glasses will be useful for a range of applications in orthopaedic regenerative medicine.