Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Wind Energy Integration on a Voltage Sag Source Locator Based on Power-Flow Information

This paper assesses a method of locating the source of voltage sags based on the power flow behavior at the point of common coupling (PCC) between systems. With the information of voltages and currents flowing in the interconnection transformers, the method is a suitable solution to locate the source of faults using a set of Boolean logic. The method is tested with the integration of a wind farm that remains connected through the appropriate control of its power converters during contingencies on the IEEE 34 node test feeder with distributed and spot loads, single-phase lines and distributed generation in parallel using a Real Time Digital Simulator (RTDS). Comparative tests are performed between the system with and without the power electronics interfaced generation, observing its dynamic response to the required Low voltage ride-through (LVRT) requirements and verifying the robustness and efficiency of the localization method.
Minibatch Approximate Greatest Descent on CIFAR-10 Dataset

The proposed research incorporates control theory into numerical optimization to develop two-phase long and short term global minimum seeking strategy for weight learning in minibatch deep learning neural network (DLNN). First-order derivative methods to update weights using single-phase optimization method applied the first-order derivative methods to update weights using single-phase optimization method, which in turn disregards the initial point located at far away from the optimum solution. Therefore, the number of iteration and error rate is high due to the slow roll-off from the initial point to the solution. The implementation of SDAGD in Convolutional Neural Network (CNN) is tested on a larger image dataset – CIFAR-10. The preliminary result shows that SDAGD achieved a misclassification rate of 13.8% on CIFAR-10 dataset.
Performance Evaluation of a FinFET -based Dual-Output Second Generation Current Conveyor

This paper presents an optimal design of a high performance DO-CCII based on FinFETs. Proposed DO-CCII works on supply voltage of $\pm 0.9\mathrm{V}$, The performance of the CCII has been thoroughly investigated in terms of DC, AC and transient characteristics of branch currents. CCII shows excellent high frequency response of currents. The 3dB BW for currents is 11 GHz. CCII provides excellent performance over its CMOS counterpart. Performance investigation has been done using HSPICE simulations.
Hybrid modeling of energy storage system and electrical loads in a pilot-microgrid

management of energy systems in an efficient and sustainable way is one of the most researched subjects by the scientific community and companies in the energy sector. In this scenario, the microgrid concept is a promising approach. A microgrid is composed of renewable sources (eg, wind and solar photovoltaic) non-renewable sources, electric loads, electrical protections and energy storage systems. This paper proposes the modeling of subsystems that compose the pilot-microgrid developed in laboratory of renewable sources of the Federal University of Espírito Santo based on hybrid systems with focus on the energy storage system and the electric loads. This work describes the modeling steps and shows the simulation results using the hybrid models.
Recursive Sine Cosine Base Function Neural Network for Proximity Capacitive Gesture Recognition

This paper presented a recursive sine cosine base function neural network (RSCNN) user gesture base on proximity capacitive sensor. The human interactive gesture signal analyses have been a research topic smart home fields that algorithms build in local device to recognize real time. The neural network have been used in many fields that including identification, control and classification. RSCNN features is used sine and cosine base function to map input signal which have wide range function to receive uncertainty input signal range. Moreover, the recursive weight record previous signal to add learn procession. Therefore, the RSCNN methods to identify user gesture has satisfactory response.
Performance Comparison between Block Maximum SNR Algorithm and MMSE Channel Inversion Algorithm in Multiuser-MIMO Systems

Multiuser (MU)-MIMO has been considered as a fundamental technology for simultaneous communications between a base station and multiple users. This paper compares MMSE channel inversion algorithm and BMSN algorithm which utilize commonly the channel regularization with the pseudo-noise in the MU-MIMO transmission. The performance evaluation is carried out through computer simulation.
Cooperative Output Regulation of Heterogeneous Linear Multi-Agent Networks via ${H_{\infty }}$ Performance Allocation

The cooperative output regulation (COR) problem of heterogeneous linear multi-agent networks [multi-agent systems (MASs)] is studied in the paper. Agents are allowed to be inherently heterogeneous linear time-invariant systems and the communication graph is not restricted to be acyclic. Both state- and output-feedback control protocols that make use of neighbors’ output information are considered. In light of robust control theory and robust output regulation theory, small-gain solvability conditions with closed-loop pole specifications are first derived. Then, an ${H_{\infty }}$ performance allocation approach is proposed for protocol design, where algorithms for optimizing graph weights and designing protocol gains are constructed, respectively. Both continuous-time (CT) and discrete-time MASs are investigated in a unified framework. In addition, solvability of the COR problem is further discussed from the graph, agent, and exosystem aspects, respectively. It is proved that the proposed approach pre-explicitly ensures the solvability of the COR problem for either of the three cases: the graph is acyclic; for CT MASs, agents are minimum-phase and right-invertible; and for CT MASs, the poles of the exosystem have zero real part and agents are right-invertible.
Thin Cylindrical Cloak Using Multi-Layer Ceramic Capacitors

Recently, electromagnetic cloaking using the transformation electromagnetics has attracted much attention from many scientists. The first experimental demonstration of electromagnetic cloaking has been reported with a cylindrical structure which made up by SRRs (Split ring resonators). In addition, multi-layer ceramic capacitors (MLCCs) have been employed for miniaturizing the unit cell structure of metamaterial. In this paper, we propose a unit cell structure composed of MLCC to design the thin cylindrical cloak. By adjusting the length of internal electrodes and the gap between two neighboring electrodes, the relative permeability of the unit cell can be controlled. The proposed unit cell structure is a very small size and it can be expected for thin cylindrical cloak for the large cloaked object.
AC-AC Hybrid Switched-Capacitor Series Resonant Converter

An AC-AC Hybrid Switched Capacitor Series Resonant Converter is proposed in this paper, which is the result of the integration of a switched capacitor ladder cell with the H-bridge series resonant converter working at high frequency. Front-end (AC-DC) and back-end (DC-AC) active converters operating at the line frequency are also part of the proposed architecture. The ladder structure reduces the switches voltage stresses to one half of the input voltage, and the series resonant converter provides soft switching for all switches of the converter. The proposed power converter architecture is intended to be used as the building block of a high voltage solid state transformer with high frequency magnetic link isolation. Qualitative and design oriented quantitative analysis, along with design methodology are presented. Simulations results for a design example with a rated power of 2 kW, 2 kV input peak voltage and 311 V output peak voltage are shown, that validate the theoretical analysis presented in the manuscript.
Regulation of Nonminimum-Phase Nonlinear Systems Using Slow Integrators and High-Gain Feedback

This paper investigates the regulation problem for nonminimum-phase nonlinear systems via slow integrators and high-gain feedback. The developed methodology involves four steps: in the first step, we design a full-information feedback controller for an auxiliary system, which parallels the stabilization of the original system, as pioneered in [18]. In the second step, we use high-gain feedback to stabilize the original system, which is able to recover the performance of the auxiliary system by choosing the gain high enough. In the third step, with a condition on the dc steady-state input–output map, a slow integrator is added that enforces the objective of regulation. In the last step, we extend the methodology into the output feedback case by utilizing the extended high-gain observer to estimate the derivatives of the output as well as one unknown term including system uncertainties. The use of the observer recovers the performance under full-information feedback. The design procedure is first presented for the linear model for the clarity of the proposed methodology. The effectiveness of such methodology is demonstrated on the nontrivial translational oscillator rotating actuator example. At last, the design for minimum-phase nonlinear systems is also provided. In this case, the performance recovery of the closed-loop auxiliary system by output feedback is verified.
Robust Stability of Positive Monotone Feedback Interconnections

Positive monotonicity and robustness of feedback interconnections of nonlinear systems are studied. First, it is shown that a well-known instantaneous gain condition on the open-loop systems, which implies feedback well-posedness, also guarantees feedback positivity. Second, parallel to the well-known results based on integral quadratic constraints, uncertainty in positive monotone feedback interconnections is characterized using a generalized notion of integral linear constraints (ILCs), which leads to robust stability conditions that are scalable with the dimensions of the systems. A list of ILCs for commonly encountered uncertainties is provided. The efficacy of the results is illustrated by a number of examples.
Closed-Loop System to Guarantee Battery Lifetime for Mobile Video Applications

The pervasive utilization of mobile technology for an increasing number of utilities and applications enhances the importance of the battery-energy optimization. Among those applications, the ones related to digital video are very popular. When using this type of applications, users may want to ensure a certain battery lifetime in order to complete the viewing of a given video content. This paper presents the design of a closed-loop control system that, from the user-desired battery lifetime, regulates the battery discharge rate in order to meet the user expectations regardless of the real-time dynamic power variations of the video decoding activity. The system is validated first by simulation and then by real tests, since it has been implemented in the operating system (OS) of a commercial development board. A simple proportional controller is able to limit the system error to an equivalent difference of only 0.4 s between the target and the achieved lifetimes, with very little computational overhead. Besides, its OS-based implementation makes it transparent to the user-level applications. Some tests show how the system is able to achieve the same lifetimes as the ones achieved with a couple of Linux-based governors, but with the determinism that the user target lifetime sets from the beginning of each test. Moreover, further tests show that the deterministic lifetimes can be extended up to 12% and 4% beyond those two governors, respectively.
CNN-LSTM: Cascaded Framework For Brain Tumour Classification

Glioma is common type of brain tumour in adults originating from glia cell. Despite advances in medical image analysis and gliomas research, accuarte diagnosis remains a challenge. Gliomas can be in general classifed into High Grade (HG) and Low Grade (LG). The exact classification of glioma helps in evaluating the disease progression and selection of the treatment strategy. Whilst medical image classification using a Convolutional Neural Networks (CNNs) has achieved remarkable success, but it is still difficult task for CNNs to accurately classify 3D medical images. One of the major limitation is the fact that CNNs are difficult to optimize in 3D volumetric classification. In current work, we addressed this challenge by introducing a cascade of CNN with Long Short Term Memory (LSTM) Network for classification of 3D brain tumor MR images into HG and LG glioma. Features from pre-trained VGG-16 were extracted and fed into LSTM network for learning high-level feature representations to classify the 3D brain tumour volumes into HG and LG glioma. The results showed that the features extracted from VGG-16 gave better classification accuracy as compared to the features extracted from AlexNet and ResNet.
Analysis of Overvoltage in Distribution Networks under different Ground Conditions using ATP

present paper evaluates the influence of the grounding systems conditions associated to the presence of arresters in the overvoltages coming from atmospheric surges in electrical distribution grids. The analysis considers the transient behavior of the electrical system, based on computational simulations carried out with the support of the ATP (Alternative Transient Program) software. This paper deals with a critical issue in the area of electrical power systems, related to the consequences of deficiencies in the grounding system to the operation of electrical grid. Detailed models of the main components from power system that interact in this process are presented, demonstrating through transient computational simulations, that the improvement of the ground conditions allows a better protection of the equipment in terms of insulation coordination.
Novel Quintuple-Mode Wideband Filter Based on Substrate Integrated Waveguide Using an Elliptic Metallic Post

In this paper a novel wideband filter using quintuple-mode substrate integrated waveguide (SIW) resonator is investigated. An elliptic metallic post loaded rectangular SIW cavity is used to excite five resonant modes. The simulations were carried out using commercial electromagnetic simulator: Ansoft HFSS. The electric field distributions of modes excited in the quintuple-mode SIW resonator are simulated first by Eigen-mode solution type. To improve the filtering performance a defected ground structure (DGS) is also used. A wide passband filter under the resonance of these five modes is achieved with 60 % fractional bandwidth at 15 GHz frequency center and two transmissions zeros at the upper frequency side.
Zinc Oxide Nanorod Field-effect Transistor with Difference Frequency Detection

Nano metal-oxide-semiconductor field-effect transistor (MOSFET) has been widely used in various sensors to achieve high sensitivity due to low-noise property of nano FET. In this paper, we present a novel zinc oxide (ZnO) nanorod FET operated in alternating current mode to implement ultra-sensitive detections. The FET is constructed by cross-connected ZnO nanorods directly grown from opposite ends of drain and source microelectrodes. ZnO nano FET acting as a high-frequency mixer transduces exterior-induced FET conductance change into an alternating current change at a certain difference frequency, which is detected by a lock-in amplifier. The nanorod FET demonstrates ultra-high sensitivity, excellent interference immunity and extremely stable performances.
Software Engineering Process for Developing a Person Re-identification Framework

In video surveillance, different security spots are considered to be placed in unconstrained locations. Cameras located on corridors, gateways and roads, have raised the need for new video surveillance systems to support activities like monitoring and look and find lost individuals. Several tools from image and video processing, as well as machine learning research areas have been conceptualized, and tested on limited data. However, implementing academic knowledge into full applications following state of the art software engineering techniques is complex. Similarly, small changes theorized in academia may represent big changes in software development. In this paper, a combination of the COMET (Collaborative Object Modeling and Architectural Design Method) and OCEP (Open Community Engagement Model) methods is proposed to develop a person re-identification framework named Perception. The three operational modes implemented in the framework (evaluation, registration and operation) provide flexibility for researchers and developers, as evidenced by the implementation of two application-specific prototypes.
Tactile Robots as a Central Embodiment of the Tactile Internet

In this paper, we discuss and speculate about the concept of the Tactile Robot connected with human operators via smart wearables as an essential multimodal embodiment of the coming Tactile Internet. The Tactile Robot, succeeding the recently introduced kinesthetic soft robot, is the upcoming next step in the evolution of rapidly developing robotic platforms that are capable of sensitive physical interaction with their environment. From the combination of rich tactile feedback with state-of-the-art robotics, technology, and algorithms emerge the potential of a meaningful and immersive connection to human operators via the vastly progressing smart wearables and virtual reality/augmented reality devices, effectively creating real-world avatars. Moreover, the Tactile Internet is believed to make it possible to create avatar collectives spanning different application domains and, therefore, cover heterogeneous robotic platforms. We hypothesize that this development will enable us to seamlessly interact with heterogeneous systems such as industrial assembly lines, service robots, automated medical units, or even deep sea and space exploration units. This new paradigm of an immersive coexistence between humans and robots builds on numerous technological advances in robotics, multimodal teleoperation, wearable technology, distributed computing, or network technology, for example. However, such a vision obviously poses major challenges in multiple areas that are still to be overcome. In this paper, we discuss the potentials and enabling technologies together with foreseeable application domains in the framework of the Tactile Internet. Furthermore, we address major challenges and hypothesize about potential solutions.
Cyberbullying Detection: An Overview

This paper is an overview of cyberbullying which occurs mostly on social networking sites and issues and challenges in detecting cyberbullying. The topic presented in this paper starts with an introduction on cyberbullying: definition, categories and roles. Then, in the discussion of cyberbullying detection, available data sources, features and classification techniques used are reviewed. Natural Language Processing (NLP) and machine learning are the famous approaches used to identify bullying keywords within the corpus. Finally, issues and challenges in cyberbullying detection are highlighted and discussed.
Syntactic Language Processing among Women - An EEG/ERP Study of Visual Pictorial Stimuli

Language is one of the intelligent categories of cognitive function comprised grammar, syntax, formation, composition of phrases and sentences. Syntactic language processing with Wh-related questions are more complicated sentence pattern where difficulties might appear in specific groups to comprehend. However, we have investigated the neural correlates of syntactic Malay language processing among a group of women where the subjects would respond by pressing buttons in order to match the several binary visual pictorial stimuli with Wh-questions. There were simply two types of Wh-questions in this study, who subjects questions and which object questions. A high dense EEG sensor net was used to record the Event Related Potentials (ERP). N400 ERP component was extracted and analysed. Higher amplitude of N400 component was found significantly in the right hemisphere of the temporal - parietal region during which object question response indicating lateralized integrated semantic information and memory processing in the brain. To extend and compare this outcome, further work in the specific language impairment group and in the pregnancy group is our future plan.
Order-of-Arrival of Tagged Objects

The order-of-arrival (OOA) of a stream of objects moving along a direction is important for decision making in logistic and industrial applications. As an example, the dispatch of luggage in airports requires to determine their OOA at checkpoints; as another example, the automatic steering of items in supply chains requires to sort and identify them while moving on a conveyor belt. This paper establishes a general framework for determining the OOA of objects that are moving along a monitored direction via radio-frequency identification (RFID) systems. Three techniques for OOA tracking are proposed and developed for objects equipped with RFID tags possessing ranging capabilities. The benefits of the proposed techniques are validated experimentally based on measurements gathered with an RFID system operating in an indoor environment.
Transition Behavior in Large Deflection of Simply Supported Plate with Piezoelectric Patches under Pretension

The transition behavior of a simply supported circular plate embedded with symmetric piezoelectric patches under pretension in large deflection due to lateral load is studied. von Karman’s large deflection theory is employed but extended to a layered case that accounts for piezoelectric force terms. For a primary insight, however, a simplified linear case is considered to deal with a modified Bessel or standard Bessel equation for the lateral slope, depending on the relative magnitude of the piezoelectric load. The associated analytical solutions are developed following the boundary conditions of simply support ends and the interface continuity between the piezoelectric layered boss and the annular area. A further integration is carried out to obtain the expressions for lateral deflection and thus which at the center of the patched plate. The behavior of how the center deflection varies with the increase of pretension is thoroughly illustrated to show the transition behavior, i. e. , how the bossed plate transfers from a plate mode to a membrane mode as the pretension proceeds. The developed approach is implemented with typical silicon-based materials commonly used for a sensing or actuating device. An extensive parametric study is conducted to explore the effects of varying the dimension of the patched region and applied voltage across the piezoelectric patches. The results indicate that, piezoelectric effect appears to be present only in a low pretension regime. In this case, the higher the applied voltage, the easier the plate under pretension may transmit to the membrane mode. Moreover, varying the radial size of the patched region may have a substantial effect on the transition behavior, as well, in a low pretension condition.
An Analytical Method to Derive a DC-DC Converter for an Arbitrary Voltage Conversion Ratio

An analytical method to derive a DC-DC converter topology for an arbitrary voltage conversion ratio is presented in this paper. The proposed method can be employed to derive DC-DC converters based on single and multiple inductors. The derivation of DC-DC converters having single inductor is explained in detail with a brief introduction to the multiple inductor derivation. The basic switching cells that can be incorporated into the single inductor based converters to obtain higher gain is introduced. Furthermore, their use to realize existing converter topologies is illustrated to validate the applicability of the proposed method to derive converters having an arbitrary gain.