Miracle-dz/UniFAR-FTUs huggingface dataset:
A Short Review on Explainable Artificial Intelligence in Renewable Energy and Resources

The aim of the study is to review XAI studies in terms of their solutions, applications and challenges in renewable energy and resources. The results have shown that XAI really helps to explain how the decisions are made by AI models, to increase confidence and trust to the models, to make decision mode reliable, show the transparency of decision-making mechanism. Even if there have been a number of solutions such as SHAP, LIME, ELI5, DeepLIFT, Rule Based Approach of XAI methods, a number of problems in metrics, evaluations, performance and explanations are still specific, and require domain experts to develop new models or to apply available techniques. It is hoped that this article might help researchers to develop XAI solutions in their energy applications and improve their AI approaches for further studies.
Machine learning for monitoring and predictive maintenance of cutting tool wear for clean-cut machining machines

This paper focuses on the study and development of learning algorithms oriented to wear classification and predictive maintenance (PdM) of the cutting tool (CT) of a clamping machine for producing structural steel bars. While several works dedicated to CTs for turning and milling operations, or in general for metal removal operations, also known as subtractive manufacturing processes, can be found in the literature, the phenomena related to cutting with a cutting knife have not been widely treated in the literature. This article intends to focus on the analysis of the latter problem. The objective is to estimate the wear of the CT, a critical component of the steel bar cutting machine. The SVM classifiers were therefore used to classify the wear. For the predictive maintenance purpose, two algorithms were implemented for the prediction of the remaining service life, based on the Degradation Model and the Similarity Model respectively; in the first method, a prediction and state update function were used, while in the second method, a Long Short-Term Memory (LSTM) Neural Network (NN) was used.
A Computationally Efficient Method for Energy Allocation in Spot Markets With Application to Transactive Energy Systems

Spot markets provide an interesting opportunity for profit maximization by energy trading based on immediate decisions on participant bids. However, their short market-clearing time can affect computational efficiency, search space, and reliability of price-energy allocation to bidding participants. Accordingly, developing a prompt and effective decision-making process plays a vital role in smooth energy delivery in these markets. This paper proposes an approach to alleviate the computational cost of the spot market aggregator in order to decide price-energy bids. The proposed bidding model is developed for the transactive energy systems, where the spot market aggregator utilizes the proposed method to maximize profit by choosing participants’ demand-side bids. The proposed method can efficiently manage participants’ combined energy and price information and avoid a highly complicated search space. It takes advantage of the multi-variable Taylor series approximation to create users’ individual cost functions. The approximated cost functions lead to user-specific bids that expedite the spot market transaction while maintaining aggregator profit. The resultant system is able to exercise profit maximization with high performance within milliseconds. The efficiency of this scheme is also demonstrated through a comparative study by using the particle swarm optimization method.
Phase Noise Suppression for Backscatter Communication with Tunable Delay Matching Block

The Range correlation effect is able to suppress phase noise in a backscatter system with a small separation between the transmit and receive antennas, due to the small-time difference between the antenna leakage path and the transmit and receive local oscillators (LOs), however as the time difference increases the effectiveness is degraded. We demonstrate a LO correlation system suitable for bi-static backscatter systems with a communication channel to share the LO. A delay block capable of generating an arbitrary delay based on Sinc interpolation of fixed delays is proposed and demonstrated.
Searching for luminous absorbed sources in the WISE AGN catalogue

The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the AHEAD project (grant agreement no. 654215). GM acknowledges financial support from the AHEAD project that is funded by the European Union as Research and Innovation Action under Grant no. 654215. FJC and IO-P acknowledge financial support through grant AYA2015-64346-C2-1-P (MINECO/FEDER). SM acknowledges financial support by the Spanish Ministry of Economy and Competitiveness through Grant no. AYA2016-76730-P. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the US Department of Energy Office of Science.
DaxVM: Stressing the Limits of Memory as a File Interface

Persistent memory (PMem) is a low-latency storage technology connected to the processor memory bus. The Direct Access (DAX) interface promises fast access to PMem, mapping it directly to processes’ virtual address spaces. However, virtual memory operations (e.g., paging) limit its performance and scalability. Through an analysis of Linux/x86 memory mapping, we find that current systems fall short of what hardware can provide due to numerous software inefficiencies stemming from OS assumptions that memory mapping is for DRAM. In this paper we propose DaxVM, a design that extends the OS virtual memory and file system layers leveraging persistent memory attributes to provide a fast and scalable DAX-mmap interface. DaxVM eliminates paging costs through pre-populated file page tables, supports faster and scalable virtual address space management for ephemeral mappings, performs unmappings asynchronously, bypasses kernel-space dirty-page tracking support, and adopts asynchronous block pre-zeroing. We implement DaxVM in Linux and the ext4 file system targeting xS6-64 architecture. DaxVM mmap achieves 4.9x higher throughput than default mmap for the Apache webserver and up to 1.5x better performance than read system calls. It provides similar benefits for text search. It also provides fast boot times and up to 2.95x better throughput than default mmap for PMem-optimized key-value stores running on a fragmented ext4 image. Despite designed for direct access to byte-addressable storage, various aspects of DaxVM are relevant for efficient access to other high performant storage mediums.
Segmentation and Error Detection of PV Modules

An important technique for the quality assurance of photovoltaic (PV) panels during the manufacturing process is the analysis of electroluminescence (EL) images. Various defects can be detected such as scratches, cracks, and short circuit cells. In our paper we introduce a new EL image dataset of monocrystalline panels and compare different loss functions for the detection of defects with state-of-the-art YOLOv5 deep neural network. For the segmentation of cells and to be able to make a geometrical description of panels we use an image-gradient based optimization approach. These information lead to a detailed structural and qualitative characterization of PV modules.
Evaluation of Distributed Ledger Technology Implementation in Electrical Energy Service through a Case Study

Distributed ledger technologies (DLTs) have become a game changer in electrical services platformization and digitalization. Therefore, the need for DLTs in electrical energy services must be understood. We present a case study of a European Union (EU) project in the Norwegian city of Trondheim, where a DLT-driven energy marketplace was piloted. We contribute to the literature and field by presenting the factors, challenges, and issues affecting DLT implementation in electrical energy services, which can be helpful for further work in electrical energy services and platform ecosystems. For policy makers and practitioners, this paper presents DLT providers' reflections about their experience in an electrical energy services project in the smart city context. These insights could be useful to ease future adoption of DLTs and to provide a ground for future empirical investigations.
Unsupervised time series pattern recognition for purpose of electronic surveillance

Signal classification is one of the main tasks of electronic surveillance. This paper focuses on extracting patterns from time series and testing robustness of pre-trained Neural Network (NN). A dataset of 10 different time series was created and used to train a neural network based on the Time-Series Representation Learning via Temporal and Contextual Contrasting (TS-TCC) model [1]. The logits layer of the NN model was removed from this pre-trained model to obtain the feature vectors. A dataset containing 87 real signals acquired from passive surveillance sensors was passed to the NN to obtain embeddings that represent the features of the signals extracted from the NN. The dataset was then corrupted with missing pulses and spurious pulses and tested on pre-trained NN. This unsupervised learning method was able to recognize 76% of the signals even with 50% of the missing input data. The research showed that an important step to improve NN performance is to choose suitable data scaling method. The best results were achieved using the StandardScaler from scikit-learn preprocessing library [2].
Skeleton-based Recognition of Pedestrian Crossing Intention using Attention Graph Neural Networks

Besides the ability to automatically detect and localize on the road, self-driving cars need to observe and understand pedestrian attention to ensure safe operations. In this study, a compact skeleton-based method to predict pedestrian crossing intention is presented. The skeleton data is first extracted using a state-of-the-art pose estimation method. Then, the proposed approach combines graph neural networks, self-attention mechanisms, and temporal convolutions to create distinctive representations of pedestrian moving skeleton sequences. The crossing intention of people is classified based on the extracted features. The experiments demonstrate competitive results with previous methods on the public JAAD dataset.
Sharp estimates of Green function of hyperbolic Brownian Motion

The main objective of the work is to provide sharp two-sided estimates of $\lambda$-Green function of hyperbolic Brownian motion of a half-space. We strongly rely on recent results obtained by K. Bogus and J. Malecki [3], regarding precise estimates of the Bessel heat kernel of half-lines.
On Self-Learning Mechanism for the Output Regulation of Second-Order Affine Nonlinear Systems

This article studies global robust output regulation of second-order nonlinear systems with input disturbances that encompass the fully-actuated Euler–Lagrange systems. We assume the availability of relative output (w.r.t. a family of reference signals) and output derivative measurements. Based on a specific separation principle and self-learning mechanism, we develop an internal model-based controller that does not require a priori knowledge of reference and disturbance signals and it only assumes that the kernels of these signals are a family of exosystems with unknown parameters (e.g., amplitudes, frequencies, or time periods). The proposed control framework has a self-learning mechanism that extricates itself from requiring absolute position measurement nor precise knowledge of the feedforward kernel signals. By requiring the high-level task/trajectory planner to use the same class of kernels in constraining the trajectories, the proposed low-level controller is able to learn the desired trajectories, to suppress the disturbance signals, and to adapt itself to the uncertain plant parameters. The framework enables a plug-and-play control mechanism in both levels of control.
A new dataset and systematic evaluation of deep learning models for student activity recognition from classroom videos

Student activity, a non-verbal modality, is an important cue to imply the students’ learning state and engagement level during class. Quantitative observation of the student activities will contribute to innovating teaching methods. However, traditional methods that rely on manual observation are time-consuming. Nowadays, surveillance cameras have been widely deployed in the classroom. This results in a massive amount of classroom videos. However, the exploitation of these videos is limited due to the lack of annotated information. This paper has two main contributions. First, a fully annotated dataset dedicated to student activity analysis has been proposed. To this end, five activities in the classroom context including sitting, standing, sleeping, raising-hand and using-phone have been defined and collected in different class sessions. Second, three deep learning models have been fully assessed for the activities recognition.
Direct-Drive Wind Generator Concept with Non-Rare-Earth PM Flux Intensifying Stator and Reluctance Outer Rotor

This paper proposes a novel concept for an electric generator in which both ac windings and permanent magnets (PMs) are placed in the stator. Concentrated windings with a special pattern and phase coils placed in separate slots are employed. The PMs are positioned in a spoke-type field concentrating arrangement, which provides high flux intensification and enables the use of lower remanence and energy non-rare earth magnets. The rotor is exterior to the stator and has a simple and robust reluctance-type configuration without any active electromagnetic excitation components. The principle of operation is introduced based on the concept of virtual work with closed-form analytical airgap flux density distributions. Initial and parametric design studies were performed using electromagnetic FEA for a 3MW direct-drive wind turbine generator employing PMs of different magnetic remanence and specific energy. Results include indices for the goodness of excitation and the goodness of the electric machine designs; loss; and efficiency estimations, indicating that performance comparable to PM synchronous designs employing expensive and critical supply rare-earth PMs may be achieved with non-rare earth PMs using the proposed configuration.
A Tool for Model Checking Eventual Model Checking in a Stratified Way

We present a tool for model checking eventual properties in a stratified w ay t hat c an e ase t he n otorious s tate space explosion problem. As shown by the name, the tool is dedicated to eventual properties formalized as $\diamond\varphi$ where $\varphi$ is a state proposition. It is possible to formalize crucial requirements/properties that should be satisfied by a lot of software systems. For example, termination is such a requirement/property. Therefore, it is meaningful to concentrate on the properties. We also conduct some case studies showing that the state space explosion can be eased to a certain scope with the technique/tool.
A framework for safe and intuitive human-robot interaction for assistant robotics

The brand new paradigm of Industry 5.0 envisages an increased leading role of the human operator in the production lines of the next future. Human-centric oriented solutions are going to be developed based on proactive human-robot collaborations, able to better exploit the skills and capabilities of both humans and cobots, mainly thanks to artificial intelligence. Several functionalities must be assured to reach such a goal, guaranteeing safety and flexibility, from human action prediction to object recognition and affordance. This paper offers an overview of the existing solutions for the various, separate issues, proposing a general framework for mobile manipulators assisting human workers, in a context of mass customization.
RAPID COMMUNICATION: A novel technique for mechanical characterization of thin elastomeric membrane

This paper reports a new method to characterize the mechanical properties, such as the elasticity, of a thin elastomeric film. A sensitive microscope visualization instrument is developed for measuring the deformation of a circular membrane of thickness less than 200 µm, under a constant load. The elastic deformation of the thin membrane was measured laterally. A theoretical model is constructed to quantitatively correlate the elasticity to the deformation profile of the membrane. The good agreement between the experimental and theoretical results facilitates the determination of the elastic modulus of a thin elastomeric membrane.
Task Space Vector Field Guiding for Motion Planning *

The article deals with the problem of planning in the task space in the presence of vector fields, while verifying and validating the constraints in the configuration space. The proposed approach, called the Task Space Vector Field Rapidly-exploring Random Tree (TSVF-RRT) algorithm, extends the Task-Space Rapidly-exploring Random Trees (TS-RRT) algorithm by incorporating vector fields into the task space, while avoiding non-trivial constraints on the configuration space. The planner restricts the search to a lower dimensional space, minimizing the upstream functional. To evaluate the proposed approach, graphical simulations are presented, carried out using a planar manipulator of 10 DOF. Possible advantages that encourage further research in this line are discussed.
FMCW Radar-Based Material Characterization Using Convolutional Neural Network and K-Means Clustering

This paper proposes a machine learning model and a calibrated frequency-modulated continuous-wave (FMCW) radar sensor to characterize dielectric slabs. First, a calibration concept derived from vector network analyzer (VNA) measurements is used to calibrate the FMCW radar’s raw IF signal and to measure the reflection coefficient of a material at a much lower cost than the VNA. Second, the measured reflection coefficient is fitted to a complex-valued convolutional neural network (CNN) to determine the dielectric constant, loss tangent and thickness of the material. K-means clustering is proposed to reduce the complexity of the CNN by significantly reducing the number of classes. The results show that the proposed model enables the extraction of the material parameters with high accuracy.
A Compact Harmonic Radar System at 61/122 GHz ISM Band for Physiological Joint Angle Estimation

This paper presents a wearable harmonic radar system for biomechanical joint angle estimation to be used in medical rehab applications. Active frequency doubling reflector tags are placed on a patient’s limbs and get detected by a dual receive channel harmonic radar front end. Target-tracking is realized by use of a motion-model based estimator with minimal quadratic error association. Range and angle estimation is performed by bilateration of individual channel target tracks. The resulting positional data is to be used as input for a model-based physiological joint angle estimation, which is not part of this paper. The system is tested on a healthy subject performing gait with multiple strides at a comfortable pace. The positional tracking is compared to equivalent data captured by a marker-based optical motion capturing system, which is the gold standard for gait analysis.
3-D Nonlinear Guidance Law for Target Circumnavigation

In this letter, we address the problem of circumnavigating a stationary target using a single vehicle. Unlike most existing results wherein the target is encircled in a two-dimensional plane, we focus on devising a guidance strategy that enables a vehicle to encircle a target in a three-dimensional space using the relative information between the vehicle and the target. In particular, we assume that the vehicle has lateral acceleration capabilities only and that the radial acceleration is unavailable, thereby making the proposed design favorable for a class of aerial vehicles (e.g., aircraft and fixed-wing UAVs, which cannot hover and have to maneuver constantly). Therefore, the vehicle’s steering controls are its lateral acceleration components in the pitch and yaw channels. In addition, we also consider nonlinear, coupled three-dimensional engagement kinematics between the vehicle and the target to preserve the inherent coupling between various channels and to achieve satisfactory control precision even if the channels are strongly coupled. Furthermore, we minimize a relevant weighted cost function to obtain the lateral acceleration components in the pitch and the yaw channels. We finally demonstrate the efficacy of our design via simulations.
Photovoltaic Maximum Power Point Tracker for a Multiple-Input SEPIC Under Partial Shading Condition

The power produced from solar panels depends on irradiation, temperatures, number of cells, and aging impact. Moreover, extracting maximum power from solar cells is independent of load. Therefore, a maximum power point tracker (MPPT) is essential to maximize the output power from a photovoltaic (PV) module. The incremental conductance, and the equilibration algorithms have been applied to extract the MPP under partial shading condition. Partial shading on solar panels causes the power-voltage curve to have different local maximal points Among them, the largest MPP, the global maximum point, should be extracted. In this paper, a multiple-input single-ended primary inductance converter (SEPIC) is chosen to combine PV modules with an alternative source because it provides a continuous input current, and full MPP tracking range.
Frequency-Beam-Scanning mm-Wave Antennas for Direction Finding RADAR

We propose a new type of compact planar Leaky-Wave Antennas (LWA) for mm-wave direction finding RADAR systems. The antennas are based on the Substrate Integrated Waveguide (SIW) leaky-wave technology which combines low profile and simple feeding with frequency scanning capability. Theoretical results show the flexibility of the design to adapt to different frequency ranges in the 60 GHz band. This is possible by just changing the dielectric material used for the SIW substrate. Designs with different angular Field-of-View (FoV) and frequency ranges are presented along with an example of signal-processing for Direction-of-Arrival (DoA) estimation.
108 and 124 GHz Fundamental VCOs with 21% and 7% DC-to-RF Efficiency in 22nm CMOS FDSOI

This paper presents the design of two high efficiency fundamental voltage controlled oscillators (VCO) for sub-THz applications. The design optimizes the transistor for voltage gain, swing and PAE at the operating frequency to achieve 21% and 7% DC-to-RF efficiencies. The varactor and back-gate voltages are utilized for tuning control. The back-gate controls improves the relative tuning range of the VCOs by $\sim$ 50%. Layout techniques are employed in the transistor and inductor to improve the VCO frequency. The two VCOs are implemented in 22nm CMOS FDSOI, oscillate at 108.7 GHz and 124 GHz having an output power of 3.41 and -1.45 dBm with a tuning range of 3.4% and 3.7%, respectively. The chip operates at a supply voltage of 0.7V and an $I_{BIAS}$ of 1.4mA with a core power consumption of 10.2mW and 9.9mW, respectively. The active area is 0.095x0. 087 $mm^{2}$.
Virtual Synchronous Generator: An application to Microgrid Stability

The objective of this paper is to illustrate an alternative control algorithm for power converters which have the task of introducing the energy generated by systems based on the use of renewable sources such as photovoltaic or wind power, into small-sized networks, microgrids. Virtual Synchronous Generator can operate both in parallel to the main network as well as in isolated and autonomous conditions (islanding-mode). The contribute of this work is to drive a consolidated approach, made in the time domain, in a more flexible and less time-consuming approach based on Park transformation