Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Combinatorial Weighting Method for Power Quality Evaluation Based on Maximization Deviation

With the rapid development of the energy Internet, power quality in modern power system has been confronted with the new opportunities and challenges. The rationality evaluation of power quality is the basis of customizing electricity price and comprehensive energy management. Traditional power quality evaluation usually ignore users' satisfaction, however, it should be considerate of both technical indicators and users' satisfaction indicators. According to the characteristics of power quality evaluation that the comprehensive evaluation problem is treated as multiobjective decision-making problem, this paper uses improved analytic hierarchy process (IAHP) and improved entropy weight method (IEWM) to determine the subjective weights and objective weights of each indicator respectively and calculate the comprehensive weight of each power quality indicator on this basis with the principle of maximization deviation, which can conquer the incomprehensiveness, bias and other disadvantages of traditional power quality evaluation method. The feasibility of the proposed power quality evaluation method and its effectiveness are verified by actual observation data of an observation station of a certain city in southern China.
Direct Digital Control of Single-Phase Grid-Connected Inverters With LCL Filter Based on Inductance Estimation Model

Soft magnetic powder cores with their high saturation flux density and low core loss are excellent alternatives for filter inductors in inverter-based applications. However, their nonlinear current-dependent inductance characteristics pose a challenge for control of grid-connected inverter with LCL filter. In this paper, the variable inductance conundrum is discussed and a modified direct digital control method based on a variable-structure inductance estimation model that takes into consideration wide nonlinear variation in both inverter- and grid-side inductances is proposed. The proposed method is shown to have better grid-voltage harmonic rejection and improved stability margins. However, investigation of stability which is conventionally based on nominal values of filter inductors cannot predict instabilities over the entire range of inductance variation. Hence, a parametric approach to conventional stability methods with a parameter space defined by variation in actual and estimated inductance is explored in this paper. The effect of line impedance on stability is also investigated with impedance-based stability criterion by considering line inductance as an additional dimension in the parameter space. A pattern in stability margins is observed due to inductance variation, with inductance at its minimum being most vulnerable to instability. Experimental results measured from a 5 kW single-phase grid-connected inverter with various LCL filters have verified the feasibility of the proposed control method. The experimental results also match the analytical results with reasonable accuracy.
t-Schatten-$p$ Norm for Low-Rank Tensor Recovery

In this paper, we propose a new definition of tensor Schatten-<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> norm (t-Schatten-<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> norm) based on t-SVD, and prove that this norm has similar properties to matrix Schatten-<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> norm. More importantly, the t-Schatten-<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> norm can better approximate the <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> norm of the tensor multi-rank with <inline-formula><tex-math notation="LaTeX">$0 < p < 1$</tex-math></inline-formula>. Therefore, it can be used for the Low-Rank Tensor Recovery problems as a tighter regularizer. We further prove the tensor multi-Schatten-<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> norm surrogate theorem and give an efficient algorithm accordingly. By decomposing the target tensor into many small-scale tensors, the non-convex optimization problem <inline-formula><tex-math notation="LaTeX">$(0 < p < 1)$</tex-math></inline-formula> is transformed into many convex sub-problems equivalently, which can greatly improve the computational efficiency when dealing with large-scale tensors. Finally, we provide the theories on the conditions for exact recovery in the noiseless case and give the corresponding error bounds for the noise case. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our t-Schattern-<italic>p</italic> norm in the Tensor Robust Principle Component Analysis and the Tensor Completion problems.
Renewable Energy Source P-V conected to the grid through Shunt Active Power Filter based in P-Q theory

This paper presents the design and simulation of an active power filter (APF) supported by a PV plant. The system can operate in three different modes: APF, APF+PV and APF+PV+Px, which means the system functions as APF extracting the necessary energy to compensate harmonics and unbalances entirely from the grid, the second mode means that this energy is extracted from the PV, and the last mode allows to use all the power available in the PV system to feed the load, support the APF and to export PV energy to the grid. The system includes a PV generator, a DC/DC converter controlled with MPPT, and a voltage source converter (VSC) acting as APF. P-Q Theory algorithm is used to control the APF. The system is simulated on MATLAB/Simscape Power Systems toolbox on three scenarios.
Advanced Beamforming Techniques for Passive Imaging of Stable and Inertial Cavitation

Passive Acoustic Mapping (PAM) allows real-time imaging of the cavitation activity for ultrasound therapy. However, a reproducible treatment requires accurate monitoring of the two types of cavitation: stable cavitation (oscillating bubbles producing tissue heating or soft mechanical effects) and inertial cavitation (collapsing bubbles producing destructive mechanical damages). To improve their localization, we have adapted a nonlinear beamformer based on the p-th root compression of signals (pDAS) to PAM, since the pDAS enhances resolution and contrast of B-mode images. The proposed method is compared in simulation to PAM and PAM with Phase Coherence Factor (PCF). The results illustrate the superiority of both adaptive beamformers (PCF and pDAS) compared to PAM in terms of resolution and contrast. Moreover the amplitude-based beamformer pDAS compared to PCF achieves a better contrast when imaging inertial cavitation from broadband received signals.
An Optimal Control Based Approach for Improving Renewable Energy Utilization in a Microgrid

Due to the increasing energy demand and environmental concern, efficient usage of the renewable energy sources becomes the focus in power systems. The microgrid (MG) is becoming important as it can integrate various types of renewable energy effectively in a small area. The coordination control of various distributed units in a microgrid is one of the most challenging research areas. The frequency in a microgrid is frequently disturbed by the intermittency of renewable energy generations and time-varying load, thus it is essential to develop a cost-effective method to maintain the frequency stability. This paper proposes an optimal control based on power line control with the emphasis on improving renewable energy generation utilization while reducing the deviation of frequency. To illustrate the effectiveness of the proposed algorithm, numerical simulations using MATLAB are conducted on as-bus microgrid.
Modelling the Iono-Acoustic Wave Field for Proton Beam Range Verification

In proton therapy, cancer patients are irradiated with high energy protons. For a successful treatment it is important that the location with the highest energy deposition, the so-called Bragg-peak, is located inside the tumor and not in the healthy surrounding tissue. Here, we investigate if the iono-acoustic wave field generated by the protons can be used to monitor the Bragg-peak location during treatment. To this end we present a new numerical method to model the pressure field generated by a clinical proton pencil beam. To compute the field, we convolve a 3-D Greens function, representing the impulse response of the medium, with a volume density of injection rate source. This source describes the expansion of the medium due to a local temperature increase caused by the energy deposited by the protons. To image the proton dose distribution, we first measure the pressure field synthetically by a matrix transducer positioned below the Bragg peak in a plane parallel to the beam. Next, we use these measurements to solve the linear inverse problem iteratively. To regularize the inversion, we take the temporal behavior of the dose deposition as prior knowledge. For the presented example, where the pencil beam has a proton range of 63 mm we are able to reconstruct the location of the Bragg peak within 4 mm accuracy.
Screen Printed Vias for a Flexible Energy Harvesting and Storage Module

This case study evaluates a highly flexible screen printed through-hole-via using silver microparticle inks for applications in energy harvesting and storage modules. The printed vias fabrication and reliability are evaluated by means of a double sided screen-printing method and repetitive (cyclic) bending tests. Vias, in 125 μm thick PET, were laser cut (50, 100, 150, and 200 μm nominal diameter) then filled, and simultaneously connected to adjacent vias, by screen printing. To investigate the use of the printed via in a monolithic energy module, the vias were used for the fabrication of a flexible printed supercapacitor (aqueous electrolyte and carbon electrode). The results indicate that the lower viscosity silver ink (DuPont 5064H) does not fill the via as effectively as the higher viscosity ink (Asahi LS411AW), and only the sidewall of the vias are coated as the via size increases (≥ 150 μm diameter). Conversely, the Asahi silver paste fills the via more thoroughly and exhibited a 100 % yield (1010 vias; 100 μm nominal via diameter) with the 2-step direct screen-printing method. The bending test showed no signs of via specific breakdown after 30 000 cycles. The results indicate that this via filling process is likely compatible with roll-to-roll screen printing to enable multi-layered printed electronics devices.
Improved Robust Tensor Principal Component Analysis via Low-Rank Core Matrix

Robust principal component analysis (RPCA) has been widely used for many data analysis problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to extract the low rank and sparse components of multidimensional data, which is a generation of RPCA. The current RTPCA methods are directly based on tensor singular value decomposition (t-SVD), which is a new tensor decomposition method similar to singular value decomposition (SVD) in matrices. These methods focus on utilizing different sparse constraints for real applications and make less analysis for tensor nuclear norm (TNN) defined in t-SVD. However, we find low-rank structure still exists in the core tensor and existing methods can not fully extract the low-rank structure of tensor data. To further exploit the low-rank structures in multiway data, we extract low-rank component for the core matrix whose entries are from the diagonal elements of the core tensor. Based on this idea, we have defined a new TNN that extends TNN with core matrix and propose a creative algorithm to deal with RTPCA problems. The results of numerical experiments show that the proposed method outperforms state-of-the-art methods in terms of both accuracy and computational complexity.
Passivity-Based Control of DC-DC Boost Power Converter for MPPT used in DC Microgrid Systems

DC microgrid systems can operate in both; islanded mode or grid-connected mode through a common DC bus, where the photovoltaic (PV) array supplying power to the DC bus by means of the dc-dc boost power converter. In grid-connected mode, the DC-bus voltage is kept constant by grid-connected control-loop using dc-ac power converter. To this end, the input voltage of the dc-dc boost power converter must be controlled so as to impose the operating point of the PV array provided by the maximum power point tracking (MPPT) algorithm. Thus, this paper presents a robust nonlinear control strategy using passivity-based control (PBC) to track a reference voltage provided by the MPPT algorithm of the dc-dc boost power converter used in DC microgrid systems. Based on the energy shaping and the damping injection technique, the PBC strategy provides a property of energy dissipation as well as robust voltage control. This control strategy ensures large signal stability as well as fast recovery performance of the system during irradiance changes. MATLAB simulation and OPAL-RT real time simulator results are presented to verify the control performance of the proposed control strategy.
Broadband, temperature tolerant and passively biased resonantly enhanced Mach-Zehnder modulators

We describe a resonantly enhanced Mach-Zehnder modulator (MZM) that can be operated over a wide temperature range of 55°C without being actively biased, while providing a significant resonant enhancement of 6.8 at the nominal wavelength / temperature compared to a linear MZM driven with a distributed driver. More importantly, it enables a ~20X improvement in power consumption compared to a 50 Ω matched linear travelling wave modulator with comparable phase shifter technology, drive voltage and output optical modulation amplitude. Passive biasing of the Mach-Zehnder interferometer is further implemented by replacing a splitter element in the MZM with a novel device combining splitting and fiber coupling functionalities in a single, multi-modal structure, that converts permanent fiber placement into a phase correction. Both concepts are combined in a single modulator device, removing the need for any type of active control in a wide temperature operation range.
Towards User QoE-Centric Elastic Cellular Networks: A Game Theoretic Framework for Optimizing Throughput and Energy Efficiency

User-centric network architectures are a key proponent to enable the uniform Quality of Experience (QoE) requirement for future dense heterogeneous network (HetNet) deployments. However, catering to spatio-temporally varying user service demands arising from the plethora of diverse mobile applications remains a challenge in such network architectures. In this paper, we propose a QoE-centric elastic framework for a dense multi-tier cellular network deployment. The framework leverages the control and data plane separation architecture (CDSA) for enabling selective data base station (DBS) activation within user equipment (UE)-centric virtual cells (also referred to as service zones). The allocation of these virtually elastic service zones around selected UEs is conducted via a central control base station (CBS) and modeled through two game techniques, namely evolutionary and auction games. Both the games are based on a utility minimization problem which is a function of weighted mean UE throughput and usage based UE service demands. To illustrate the trade-offs between the game models, network level performance is compared in terms of aggregate throughput, energy efficiency, algorithm convergence speed and mean UE scheduling probabilities.
Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications

A hand impairment can have a profound impact on the quality of life. This has motivated the development of dexterous prosthetic and orthotic devices. However, their control with neuromuscular interfacing remains challenging. Moreover, existing myocontrol interfaces typically require an extensive calibration. We propose a minimally supervised, online myocontrol system for proportional and simultaneous finger force estimation based on ridge regression using only individual finger tasks for training. We compare the performance of this system when using two feature sets extracted from high-density electromyography (EMG) recordings: EMG linear envelope (ENV) and non-linear EMG to muscle activation mapping (ACT). Eight intact-limb participants were tested using online target reaching tasks. On average, the subjects hit 85% ± 9% and 91% ± 11% of single finger targets with ENV and ACT features, respectively. The hit rate for combined finger targets decreased to 29% ± 16% (ENV) and 53% ± 23% (ACT). The non-linear transformation (ACT) therefore improved the performance, leading to higher completion rate and more stable control, especially for the non-trained movement classes (better generalization). These results demonstrate the feasibility of proportional multiple finger control in intact subjects by regression on non-linear EMG features with a minimal training set of single finger tasks.
Robust Spherical Formation Tracking Control of First-order Agents with An Adaptive Neural Flow Estimate

This paper addresses the robust cooperative control for lateral formation tracking a set of circles on the given sphere in an absolutely unknown spatial flowfield. Different from the adaptive estimation method for the unknown flow speed with knowledge of the velocity direction in the literatures, a novel adaptive neural estimate is constructed based on the neighbors' information to approximate the unknown flow velocity. It is noted that such neighbor-based adaptive neural estimation develops the traditional adaptive neural approach by consensus. Then, a robust spherical formation tracking control law is established according to flow estimation. The uniform ultimate boundedness is proven when the communication topology associated with networked first-order agents. The effectiveness of the analytical results is verified by numerical simulations.
Low-Power Scalable 3-D Face Frontalization Processor for CNN-Based Face Recognition in Mobile Devices

A low-power scalable 3-D face frontalization processor is proposed for accurate face recognition in mobile devices. In spite of recent improvement in face recognition accuracy mainly from convolutional neural networks (CNNs), their performance is limited to face images with frontal view. For face recognition with human-level accuracy in real-life environment, in which most of the face images are captured from arbitrary angles, 3-D face frontalization is essential as a preprocessing stage for CNN-based face recognition algorithms. The proposed face frontalization processor shows scalability in two aspects: image resolution and accuracy. For low-power consumption and scalability, the processor proposes three features: 1) scalable processing element (PE) architecture with workload adaptation; 2) accuracy scalable regression weight quantization to reduce the external memory access (EMA) down to 81.3%; and 3) pipelined memory-level zero-skipping to further reduce the EMA by 98.4% without any latency overhead. From the proposed EMA reduction features, the EMA is reduced by 99.7% with little accuracy degradation in face frontalization results. The proposed face frontalization processor is implemented in 65-nm CMOS process, and it shows 4.73 frames/s) throughput. Moreover, power consumption of the implemented face frontalization processor is 0.53 mW, which is suitable for applications on mobile devices.
Tensor Completion From Structurally-Missing Entries by Low-TT-Rankness and Fiber-Wise Sparsity

Most tensor completion methods assume that missing entries are randomly distributed in incomplete tensors, and the low-rank prior or its variants are used to well pose the problem. However, this could be violated in practical applications where missing entries are not only randomly but also structurally distributed. To remedy this, this paper proposes a novel tensor completion method equipped with double priors on the latent tensor, named tensor completion from structurally-missing entries by low tensor train (TT) rankness and fiber-wise sparsity. In the proposed model, the underlying tensor is regularized by a low-TT-rankness prior to exploit the inter-fibers/-slices correlations, and its fibers are regularized by a sparsity prior under dictionaries to exploit intra-fibers correlations. The proposed model is solved by an alternating direction method under the augmented Lagrangian multiplier framework. Experimental results on both synthetic and real data demonstrate the effectiveness and superiority of the proposed model in completing tensors with both random and structural missing entries, compared with state-of-the-art tensor completion approaches.
A Misalignment-Tolerant Series-Hybrid Wireless EV Charging System With Integrated Magnetics

Electric vehicles (EVs) are becoming increasingly popular as a mean of mitigating issues associated with fossil fuel consumption in transportation systems. A wireless inductive power transfer (IPT) interface between EV and the utility grid has several key advantages, such as safety, convenience, and isolation. However, physical misalignments between the pads of IPT charging systems used in EVs are unavoidable and cause variations in key system parameters, significantly increasing losses and affecting power throughput. This paper presents a novel series-hybrid topology in which the series inductors of the primary and pick-up inductor–capacitor–inductor (LCL) networks are integrated into polarized magnetic couplers to improve the system performance under pad misalignment. A mathematical model is developed to investigate the behavior of the proposed system under misalignment. To demonstrate the viability of the proposed method, the results of a 3.3-kW prototype series-hybrid IPT system are presented, benchmarked against a conventional IPT system. Experimental results clearly indicate that the proposed system maintains the output power within ±5% of its rated power despite the pad misalignment. The proposed system is efficient, reliable, and cost effective in comparison to conventional LCL- and CL-compensated IPT systems.
Supply Delivery Monitoring System Using Mobile Device Based on Internet of Things Case Study (Pt. Semen Gresik Persero Tbk.)

Indonesia's industrial development in the transportation and logistics sector will experience a 15.4% increase until 2020 in accordance with the calculations of one of the global market research institutions, Frost and Sullivan. In dealing with this change, a good management is needed from planning to the distribution process to estimate the time when shipping goods. This final project will design and create a system that can optimize the route by congestion, damaged roads, and travel distance using the Djikstra method which considers the weight of each node as a representation of the condition. In addition to optimizing, the system will monitor by looking at the list of active and operating trucks along with notifications containing a list of shipments for each distributor and estimated delivery time. In this application a map will be shown and provided information in the form of pop-ups and colors on each road that describes the current condition of the road. The report on road conditions is directly input by the driver to provide notifications on maps so that the application can provide alternative ways to avoid congestion and damaged roads. The results of this study indicate that, in field trials, this application has succeeded in determining the fastest route based on the shortest distance and the weight on the damaged road using the Djikstra algorithm. The travel monitoring system has been able to receive and display travel conditions that are visualized in the form of symbols and lines on a map managed by the admin in real time.