Miracle-dz/UniFAR-FTUs huggingface dataset:
A Three-Tier Framework for Understanding Disruption Trajectories for Blockchain in the Electricity Industry

Ever since the invention of Bitcoin by the pseudonymous Satashi Nakamoto, cryptocurrency has provoked debate in banking and finance sectors, and is sometimes considered a potential successor to fiat currency. Blockchain, the new technology underpinning decentralised and immutable databases, has seen much discussion as a potentially game-changing development. Although many industries are exploring its value, the technology has thus far made only minor impacts. A rapidly expanding base of research has emerged on blockchain’s role as a potential disruptor in the electrical energy industry. However, it may be difficult to distinguish hype from more imminently plausible impacts. This paper attempts to serve as a guide for engineering managers wishing to make sense of blockchain’s potential in electricity. This is accomplished by formulating a novel blockchain industry disruption framework, which exists across three tiers. These tiers extend from ideas with the least effect on an industry to total revolutionary concepts that could completely transform an industry. This taxonomy is constructed by examining existing research into disruption hierarchies and blockchain classification methods. Through the lens of this taxonomy, a literature review is performed on blockchain’s role in energy to draw out themes and ideas characterising each tier. The potential likelihood of real-world application of various ideas are discussed, considering how established industries may be affected or disrupted. The authors provide some conjecture here. Finally, courses of action are suggested for those whose sector may be affected by blockchain.
A Novel Soft-Robotic Gripper with Vertically Plane Contact of the Object

This paper presents a novel soft-robotic gripper. The proposed soft-robotic gripper combines two dual-joint pneumatic actuators. The high passive compliance of joint II of the soft actuators realizes vertically plane contact with the object, which improves the grasping reliability. The bending angle and output force of the actuators with different bottom layers were analyzed by finite element analysis. The contact force and contact area of the actuator with the object have been analyzed by finite element analysis. The fabrication of the soft actuators are presented. Then the grasping performance of the soft gripper was studied by systematic experiments at different pressure when grasping rings with different diameters in different positions of the actuator to obtain the optimal position.
Center-Tapped π-Type Single-Phase Cell

A π-type derived four-level single-phase inverter is analyzed in this paper. It is demonstrated that the topology can be obtained both by inserting a neutral connection point in a four-level π-type cell, or removing the switches connected to ground in a five-level T-type cell. Five different pattern approaches to drive converter switches are presented, showing its influence on the high-frequency waveform of the output voltage. Simulation results for scenarios of feeding a single-phase load and a solar array connection to grid are given, making possible to demonstrate its functionality.
Direct Torque Control Scheme for a Nine-Phase Induction Motor With Reduced Current Harmonic

This paper discusses a modified Direct Torque Control (DTC) method in order to reduce phase current harmonic distortion in a nine-phase asymmetrical induction motor drive system. Since the classical DTC is a direct adaptation from the three-phase case, it does not take into account the presence of the non-producing torque current components obtained from the Space Vector Decomposition (SVD) technique. Thus, in classical DTC these non-producing torque components assume high amplitudes, causing distortion in phase currents. In this way, it is shown that modified DTC minimizes the undesired components, reducing significantly the distortion in phase currents while maintaining the fast dynamic response of classical DTC.
Réponses adaptatives des microorganismes eucaryotes du sol aux pollutions métalliques

Les sols pollues par des metaux lourds sont colonises par des communautes de microorganismes qui ont developpe differentes adaptations leur permettant de resister a ces contaminants. Afin d'analyser au niveau moleculaire la diversite de ces adaptations, une approche experimentale innovante basee sur l'etude du meta transcriptome eucaryote des sols a ete utilisee pour comparer les fonctions exprimees au sein d'une communaute de microorganismes eucaryotes colonisant un sol contamine, anciennement contamine et non contamine par des metaux lourds. Les banques d’ADNc eucaryotes construites a partir des ARNm extraits directement de ces sols ont ete criblees par sequencage aleatoire de leurs inserts et par complementation fonctionnelle de mutants de levures sensibles au cadmium.Cette etude a permis d’identifier de nouveaux genes et de nouveaux mecanismes impliques dans la resistance au cadmium ainsi qu’un nombre important de nouvelles proteines hypothetiques. Ceci demontre l’interet applique de cette approche pour la recherche de nouveaux bio catalyseurs et molecules bio actives.En parallele, la diversite microbienne eucaryote a ete revelee et comparee entre les sols par le clonage-sequencage du gene codant la petite sous-unite ribosomique 18S. Cette etude a mis en evidence une diversite inattendue de microorganismes eucaryotes dans ces sols et une analyse phylogenetique a permis de decouvrir un nouveau clade de protistes (Rhizaria,Cercozoa).
Enhancing Quality of Service in SDN based on Multi-path Routing Optimization with DFS

Remarkable growth of network traffic has been noticed recently due to the use of online applications and cloud services. Thus, multiple routing problems need to solve without increasing latency to optimize the traffic. Based on the responses from industry, Software Defined Networking (SDN) has been developed to meet this challenge. SDN allows the programmable features of network and responds dynamically. In this paper, multipath routing problem in SDN is addressed and resolved under OpenFlow protocol where Ryu controllers are used to implement SDN using the Mininet WiFi emulator. Besides, in addition to a graphical user interface, a module has been developed using python programming along with OpenFlow based Ryu controller. In the simulation results, depth first search (DFS) algorithm outperforms over breadth first search (BFS) in terms of round trip time (RTT), delay and throughput. Hence, this article enhanced the quality of service (QoS) in SDN.
Model-Based Control of Nonlinear Wire Tension in Dynamic Needle Winding Processes

A defined wire tension force at the nozzle exit is crucial for reproducible and precise wire deposition in a needle winding process. The estimation of wire tension with an Extended-Kalman-Filter (EKF), presented in a prior publication, was evolutionarily refined in this work. To advance emulation of nonlinear bending forces for tension simulation, neural networks were introduced. A comparison between estimation by an EKF with excluded updates and by a random walk EKF proved valid model quality. Loose wires were detected with an additional functional module. To prevent loose windings, wire tensions at the nozzle exit were controlled with a model predictive control approach. This controller was validated by dynamically adjusting the reference wire tensions during winding.
Prediction of Heart Disease Using Multi-Layer Perceptron Neural Network and Support Vector Machine

In recent years, heart disease is one of the major causes of death. So it is necessary to design a system that correctly diagnoses heart disease. In this study, we have proposed two classifiers. One is a Multi-Layer Perceptron neural network (MLP) and another is Support Vector Machine (SVM). Our work is to classify two-class of heart disease and five class of heart disease. Here we have used the Cleveland heart disease online database which consists of 303 instances with 5 classes and 13 attributes. For two-class classification problem, SVM has 92.45% accuracy while the accuracy of MLP is 90.57%. For five-class classification problem, MLP has an accuracy 68.86% while SVM is 59.01%.
CASINO Core Microarchitecture: Generating Out-of-Order Schedules Using Cascaded In-Order Scheduling Windows

The performance gap between in-order (InO) and out-of-order (OoO) cores comes from the ability to dynamically create highly optimized instruction issue schedules. In this work, we observe that a significant amount of performance benefit of OoO scheduling can also be attained by supplementing a traditional InO core with a small and speculative instruction scheduling window, namely SpecInO. SpecInO monitors a small set of instructions ahead of a conventional InO scheduling window, aiming at issuing ready instructions behind long-latency stalls. Simulation results show that SpecInO captures and issues 62% of dynamic instructions out of program order. To this end, we propose a CASINO core microarchitecture that dynamically and speculatively generates OoO schedules with near-InO complexity, using CAScaded IN-Order scheduling windows. A Speculative IQ (S-IQ) issues an instruction if it is ready, or otherwise passes it to the next IQ. At the last IQ, instructions are scheduled in program order along serial dependence chains. The net effect is OoO scheduling via collaboration between cascaded InO IQs. To support speculative execution with minimal cost overhead, we propose a novel register renaming technique that allocates free physical registers only to instructions issued from the S-IQ. The proposed core performs dynamic memory disambiguation via an on-commit value check by extending the store buffer already existing in an InO core. We further optimize energy efficiency by filtering out redundant associative searches performed by speculated loads. In our analysis, CASINO core improves performance by 51% over an InO core (within 10 percentage points of an OoO core), which results in 25% and 42% improvements in energy efficiency over InO and OoO cores, respectively.
Self-Evolutionary Pose Distillation

Conventional pose distillation method utilizes the teacher’s output and the label to co-supervise the student model. The architecture of the student model is fixed in the training, making it impossible to obtain a compact and powerful model. In this paper, we introduce the theory of evolution to the pose distillation and propose the self-evolutionary pose distillation (SEPD) method which not only improves the performance of the student model but also reduces the size of the student model. Specifically, the SEPD considers the original model as the teacher model and obtains the student model by using the model reduction strategy to shrink the teacher model. The student model is supervised by the teacher’s output and the label jointly. After the optimization, the student is treated as the teacher model and the student model is obtained by the model reduction strategy. The student model is optimized again. The compact and strong model is obtained by repeating the procedure above. Experiments on the challenging benchmark validate the effectiveness of our SEPD method.
Partial Harmonic Current Compensation Applied to Multiple Photovoltaic Inverters in a Radial Distribution Line

The increase of the current harmonic content due to nonlinear loads is one of the main concerns in power systems. Therefore, the possibility to use the current margin of photovoltaic (PV) systems to provide harmonic current compensation have been addressed in several works. In this context, an important point is to ensure that the PV inverter current does not extrapolate its rated current peak. For this purpose, dynamic current saturation techniques are adopted, incorporating the partial harmonic current compensation capability in PV inverters. Thereby, this work proposes to implement the partial harmonic current compensation applied to multiple PV inverters using a dynamic saturation technique based on an open-loop algorithm. Furthermore, a radial distribution line is used to analyze the effects of harmonic compensation sharing among PV inverters under different solar irradiance conditions. The current margin of PV systems are combined to compensate nonlinear load currents in the line. Simulation results show the improvements in the grid power quality, ensuring a total demand distortion (TDD) lower than 5%, as recommended by the IEEE Std 519-2014.
Deep Converged Network for Attention

In natural language processing, feature extraction plays a significant role in different models and most of previous models based on Transformer or BERT have obtained the substantial performance, however, most of these feature extractors with attention mechanism don’t fully play their effects. In this study, the Feature Fusion Residual Network (FFRN) is proposed for enterprises sentiment analysis, which can calculate the sentimental tendency toward texts by learning semantics of sentences. To make well use of the ability of Transformer, the paper introduces the feature fusion extractor and deep converged architecture, which use attentional encoder and residual network to learn and resolve the structure of sentences deeply that finally can improve the accuracy of prediction in enterprises sentiment analysis. Experiments on different basic datasets show that proposed model obtains the new state-of-the-art compared to Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) with attention. Keywords:
Impact and Modeling of Möbius Connection in the Rotary Traveling Wave Oscillator Performance

The new interest in high frequency applications such as 5G and mmWaves have increased the design requirement for the integrated circuits, specifically for the signal generation and base-band processing stages. The Rotary Traveling Wave Oscillator (RTWO) has been proposed not only as clock distribution network when is used in arrays (ROAs), but also for high data rate applications and communication systems. The RTWO has two ring-shaped differential transmission lines as resonator and this resonator ring cannot be built with a constant characteristic impedance due to the characteristics of the CMOS manufacturing processes. The most complex discontinuity of the RTWO resonator is the Möbius connection; this discontinuity is generally manufactured using angles of 90° or 45°, different metal levels and stacks of intermetallic connections (vias). In this work we analyze and model the 45° Möbius connection for different geometrical configurations and technologies. The results show that the Möbius connection commonly implemented using the last two top metal levels is not the optimal and by using a lower metal layer is possible to select the optimal cross-connection in this type of oscillators.
An overview of EMF exposure assessment metrics

With the arrival of new, more powerful radiofrequency (RF) energy sources, the potential risks associated with human exposure to electromagnetic fields (EMF) also increased. This brought the necessity of developing appropriate security regulations, as well as standardized measurement and exposure evaluation techniques. This paper gives a brief overview of current metrics used for the assessment of exposure to RF-EMFs.
On the Design of Logarithmic Multiplier Using Radix-4 Booth Encoding

This paper proposes an energy-efficient approximate multiplier which combines radix-4 Booth encoding and logarithmic product approximation. Additionally, a datapath pruning technique is proposed and studied to reduce the hardware complexity of the multiplier. Various experiments were conducted to evaluate the multiplier’s error performance and efficiency in terms of energy and area utilization. The reported results are based on simulations using TSMC-180nm. Also, the applicability of the proposed multiplier is examined in image sharpening and convolutional neural networks. The applicability assessment shows that the proposed multiplier can replace an exact multiplier and deliver up to a 75% reduction in energy consumption and up to a 50% reduction in area utilization. Comparative analysis with the state-of-the-art multipliers indicates the potential of the proposed approach as a novel design strategy for approximate multipliers. When compared to the state-of-the-art approximate non-logarithmic multipliers, the proposed multiplier offers smaller energy consumption with the same level of applicability in image processing and classification applications. On the other hand, some state-of-the-art approximate logarithmic multipliers exhibit lower energy consumption than the proposed multiplier but deliver significant performance degradation for the selected application cases.
MPPT for a photovoltaic system under partial shaded conditions

In this paper, a method to find the maximum power point for a photovoltaic system under partial shaded conditions is presented. Furthermore, a control law using Passivity Based Control is proposed for a DC-DC converter. In addition, simulation results show the good performance of the extremum seeking control in searching the global maximum based in the inherent robustness of the Passivity Based Control.
Transfer Learning Based Kernel Fuzzy Clustering

Transfer Learning is utilized to the traditional clustering methods to enhance the clustering effect in the target domain with insufficient data. These methods can obtain good performance in handling the linear data. But for the ‘non-linear’ data, they cannot perform effectively. To solve this issue, this paper proposes a novel transfer learning based kernel fuzzy clustering algorithm. In this method, the kernel clustering is first performed to the source data to achieve the important knowledge, i.e. cluster prototypes. Then these cluster prototypes are transferred to guide the data clustering in the target domain. Experiments conducted on the ‘non-linear’ data prove the superiority and efficiency of the proposed method.
A Semi-Intrusive Load Monitoring Approach for Demand Response Applications

Appliance-Load Monitoring (ALM) is essential for several demand-response (DR) programs. The techniques used for this purpose are broadly classified into two types: 1) Intrusive Load Monitoring (ILM), and 2) Non-Intrusive Load Monitoring (NILM). This paper proposes a semi-intrusive load monitoring (SILM) technique to exploit the bests of both. The method does not use any disaggregation algorithm; instead, it’s an instrumentation approach to monitor the power consumption of appliances. First, it groups all appliances of a residential house in some clusters and then monitors those, in near real-time, using only one sensing-unit per cluster. A lab-scale testbed is developed to verify the claims of the proposed method. Test results demonstrate the monitoring and controlling capability and prove the cost-effectiveness of the technique. Raspberry Pi is used as the programming platform along with Python programming language.
Extremity Extraction Based on Curvature with Skeleton Filtering and the Ability of Recognizing of Hidden Features Human Pose Representation

Silhouette border's extremities can be considered as describing features for human postures. Using of extremities for posture representation, and hence action recognition, is a simple and time efficient method that can also reduce the detection error rate. But unfortunately, popular extremity extraction methods e.g. star skeleton methods, encountered with enormous faults. This paper proposes a new method for extremity extraction with minimum error rate. First of all, curvature is introduced as a feature that can be used for extraction of candidate extremities. Since, the curvature is a boundary feature, it is insensitive to posture situations. Two filtering steps are then applied for minimizing wrong features. Since some of features may be blocked by occlusion a prediction step based on previous frames is employed. Pose representation is then carried out using a multi layer perceptron neural network. Promising achieved result verified the effectiveness of the proposed method. Experimental results demonstrate the privileged performance of the proposed algorithm to extract the extremities and so the human posture against the best reported works
Control Strategy and Power Management for Multifunctional Inverters with BESS and Reactive Power Compensation

Due to the non-controllable characteristics of the photovoltaic (PV) generation, like the solar radiance intermittence, technical challenges arise for the electrical power system, when considered a high penetration of PV generation in the utility grid. One possible solution for these problems is to integrate a Battery Energy Storage System (BESS) with the generation systems. Herewith, it is possible to consider the distributed generator similarly as controllable sources, allowing many operating modes for the PV systems, including reactive power compensation. In this way, this paper proposes a low-level power management strategy for the compensation of active and reactive power to local loads. It aims to guarantee an adequate power factor to the installation reducing cost associated with the energy bill. Besides that, real scenarios are considered, in which the limits of the state of charge (SOC) cannot be exceeded. In this case, the system can promote the BESS recharge only by the PV or by the PV plus the utility grid. To evaluate the proposed structure, PSIM software simulations are presented. It is considered the physical model of a PV array, which uses real data obtained experimentally.
Data-driven Modelling of Engineering Systems with Small Data, a Comparative Study of Artificial Intelligence Techniques

This paper equitably compares five different Artificial Intelligence (AI) models and a linear model to tackle two real-world engineering data-driven modelling problems with small number of experimental data. Analysis of results show that, in both cases, the models are highly nonlinear and Multi-Layer Perceptrons (MLPs) outperform other AI models including neuro-fuzzy networks (or in short fuzzy models), Radial Basis Function Networks (RBFNs) and Fully Connected Cascade (FCC) networks. The latter has been claimed to be superior in the literature for some non-engineering benchmarks.
Low computational cost technique for SPMSM sensorless drive using active flux concept

In this paper, permanent magnet synchronous motor flux control is performed, involving the active flux concept. This flux control is associated with a scalar drive (V/f), being a native sensorless technique, without a special approach. Moreover, the applied strategy has low computational cost and can operate at very reduced speeds, tracking references and rejecting disturbances. The focused application is direct-drive washing machines. Experimental results are presented, including a very low-speed test. The main contribution of this paper is related to the control design.
Research on Rao-Blackwellized Particle Filter SLAM Based On Grey Wolf Optimizer

In order to improve the accuracy of localization and mapping, this paper proposes a Rao-Blackwellized particle filter SLAM based on grey wolf optimizer. The estimation performance of the Rao-Blackwellized particle filter is improved by the local exploration and global exploitation capabilities of the grey wolf optimizer. Low-weight particles move towards the “prey” and the estimates of the pose is further optimized in the process. The algorithm overcomes the problem of particle degradation to a certain extent and reduces the number of particles required for precise localization and mapping. The improved algorithm is compared with the gmapping on different data sets and the results prove that the proposed algorithm is effective in different environments.