Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-Time Controlled Islanding and Restoration Scheme Based on Estimated States

Power system operators are facing major challenges today to keep the system operating at the admissible limits. Recent blackouts demonstrated the need for a systematic study and design of a comprehensive system control strategy. Intentional controlled islanding (ICI) has been proposed as an effective corrective control action of final resort to save the system from a partial or a complete blackout. ICI limits the occurrence and consequences of blackouts by splitting the power system into a group of smaller, stable, and sustainable subsystems, also called islands. After a controlled system separation, power system operators should resynchronize and reconnect each island to restore the system. In this sense, real-time knowledge of the operating condition of the islands is required. In this paper, a real-time ICI and restoration scheme is proposed. The proposed scheme consists of an ICI algorithm that finds islanding solutions with minimal power-flow disruption while considering power system restoration constraints (e.g., blackstart availability, sufficient generation capacity, and observability), a real-time state estimator that monitors the system before and after the islanding, and a restoration process. The proposed ICI and restoration scheme is tested using the dynamic IEEE 39- and 118-bus test systems.
An Asynchronous Auto-biasing Circuit for Wearable Electrochemical Sensors

This work presents a circuit for asynchronous, automatic biasing of a CMOS ISFET array for wearable electrochemical measurement systems. The circuit is integrated into a temperature compensated pH-to-frequency converter utilising the floating gates of an ISFET array. The work represents the first effort to address the issue of variable bias points in integrated electrochemical sensors for wearable applications, and is a low power, low transistor solution without an output voltage ripple. Designed in a 0.35flm CMOS technology, the system achieves a low power consumption of 29. 72µW with a typical settling time of 0.7ms.
Coordinated Supplementary Damping Control of DFIG and PSS to Suppress Inter-Area Oscillations with Optimally Controlled Plant Dynamics

This paper proposes a design method to coordinate dual-channel supplementary damping controllers (SDCs) of doubly-fed induction generators (DFIGs) and power system stabilizers (PSSs) for suppression of inter-area power oscillations. A dynamic performance index is introduced to measure the dynamics of the conventional synchronous generator (SG) and DFIG during the damping control process. Hence, the proposed method designing the PSS and SDC is formulated as an optimization problem with the objective function being the sum of weighted performance indexes and the constraints indicating the requirements on the damping of the inter-area modes. Solving the optimization problem can obtain the optimal SDC and PSS which can meet the required damping results as well as optimize dynamics of the controlled plants. Moreover, by adjusting weights in the objective function, the damping control burden can be flexibly and feasibly allocated between active and reactive power channels of DFIGs or among the damping controllers. Simulations with the modified New England and New York interconnected system prove that the proposed optimization based tuning method can not only robustly coordinate the PSS and SDC to effectively damp inter-area oscillations but also improve the dynamics of controlled plants during the damping control process over different operat-ng conditions.
Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded Hybrid Microgrids

Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining the optimal control action before switching signals are sent. The proposed algorithm eliminates the needs of PI, PWM, and droop components, and offers 1) accurate PV maximum power point tracking (MPPT) and battery charging/discharging control, 2) DC and multiple AC bus voltage/frequency regulation, 3) a precise power sharing scheme among DERs without voltage or frequency deviation, and 4) a unified MPC design flow for hybrid microgrids. Multiple case studies are carried out, which verify the satisfactory performance of the proposed method.
An Improved Scheme of Active Power Differential Protection for Transmission Line

In this paper, an improved scheme of active power differential protection for transmission line is proposed. The criteria for active power differential protection are optimally designed and described in detail. Restraint characteristic criteria and additional auxiliary criteria are used to solve the "voltage dead zone" issues of active power differential protection. Simulation results show that the proposed protection scheme can accurately identify the fault area without dead zone when metallic ground fault occurs near the protection units. In addition, active power differential protection has the advantage of low data synchronization requirements, and it is verified in this study.
Spatio-temporal study for modeling high dimensional future uncertainties: Univariate to multivariate model

This paper proposes a multivariate modeling approach to tackle spatio-temporal dependency of various variables accounted in electric power system operation and planning. The stochasticity of system load as well as power generation from renewable energy sources poses special challenges to power system planners. Increasing penetration levels of wind exacerbate the uncertainty and variability that must be addressed in coming years, and can be extremely relevant to power system planners. Inefficiency of univariate models and relying on correlation is seen as a future bottleneck. A joint multivariate modeling approach using vine copula is proposed in this work considering load and wind data from US utility.
An Overview of A Load Balancer Architecture for VNF chains Horizontal Scaling

We present an architectural design and a reference implementation for horizontal scaling of virtual network function chains. Our solution does not require any changes to network functions and is able to handle stateful network functions for which states may depend on both directions of the traffic. We use connection-aware traffic load balancers based on hashing function to maintain mappings between connections and the dynamically changing network function chains. Our references implementation uses OpenFlow switches to route traffic to the assigned network function instances according to the load balancer decisions. We conducted extensive simulations to test the feasibility of the architecture and evaluate the performance of our implementation.
A MVDR- MWF Combined Algorithm for Binaural Hearing Aid System

A software defined binaural hearing aid system with a smartphone-centered architecture has been developed. This architecture takes advantage of the powerful computing hardware and memory capacity of smartphones. The sound signals are captured under complex acoustics scenes. And they need further enhancement to satisfy patients' personalized requirements. A minimum variance distortion response (MVDR) and binaural multichannel wiener filtering (MWF) combined algorithm (MMC) for speech enhancement is proposed. The proposed algorithm can achieve balance between noise reduction in complex acoustics environment and preservation of interaural cues therefore improve speech intelligence at the same time. The subjective and objective evaluation results as well as running on the binaural hearing aid with smartphone platform case demonstrate the efficiency of our work.
Static Random Access Memory Characteristics of Single-Gated Feedback Field-Effect Transistors

In this paper, we propose a novel static random access memory (SRAM) unit cell design and its array structure consisting of single-gated feedback field-effect transistors (FBFETs). To verify the SRAM characteristics, the basic memory operations and write disturbances of the unit cell are investigated through the mixed-mode technology computer-aided design simulations. The unit cell exhibits the superior SRAM characteristics including a write speed of 0.6 ns, a fast read-out speed of ~0.1 ns, and a retention time of 3600 s. Furthermore, the unit cell design exhibits advantages in density, with a small cell area of 8F<sup>2</sup>, and in the power consumption; the standby power consumption is 0.24 nW/bit for holding “1” and negligible for holding “0. ” Moreover, our SRAM array shows reliable <inline-formula> <tex-math notation="LaTeX">${3} \times {3}$ </tex-math></inline-formula> array operations without any disturbances. This paper demonstrates the promising potential of the FBFET SRAM for high-performance, high-density, and low-power memory applications.
“I am Still Learning”: Modeling LMS Critical Success Factors for Promoting Students’ Experience and Satisfaction in a Blended Learning Environment

Blended learning has been growing in demand and popularity and has become a common phenomenon in modern higher education systems, especially with the implementation of learning management system (LMS). This paper examined the effects of critical success factors on students’ experience and satisfaction with the LMS in a blended learning setting. Certain characteristics of students, instructors, system, classmates, course design, and organization were identified from the literature as the main indicators of the LMS success. A total of 174 university students (94 males and 80 females) responded to an online questionnaire. A partial least square (PLS) approach was used for data analysis. The result showed that certain factors were found to influence students’ perceived ease of use and usefulness of LMS and consequently their satisfaction. Findings from this paper provide the necessary insights as to how universities can enhance students’ experiences and satisfaction of LMS in order to support the blended learning approach.
Linear and Uniform System Dynamics of Future Converter-Based Power Systems

The share of power converter based renewable generation is steadily increasing at the expense of rotating inertia. Virtual Synchronous Machines are deemed to ensure system stability, however, there are no widely accepted criteria for the design of their controllers. In converter-dominated power systems one of the main challenges will be to analyze large-signal stability, because of the large number (and possibly divergent design) of converters. Converters, however, also offer the possibility to shape system dynamics in a way that was impossible with synchronous machines. In this paper we introduce the new concept of Linearized and Uniform Swing Dynamics. This allows to linearize the nonlinear swing equation over almost the entire state space, thereby enabling to use the small-signal stability analysis technique for large signal stability investigations. By properly choosing the controller parameters, the dynamics of the large number of converters expected in the system can be made unified and predictable.
Remote Sensing Images Recognition by Deep Convolutional Neural Networks

Remote sensing image recognition is widely applied, such as agriculture, forest monitoring and meteorology. The effect of remote sensing application analysis greatly depends on the accuracy of image recognition. In this paper, we proposed an image recognition method for remote sensing images using deep convolutional neural network. The neural network learned the feature via training data automatically. After training, the network can recognize query images in a relative high accuracy. According to the experimental results, the proposed method shows a good performance on a given remote sensing image dataset.
An Interference-Rejection Strategy for Measurement of Small Current Under Strong Interference With Magnetic Sensor Array

Traditional infrastructure and instrumentation methods in power system are transforming to cater the needs of modern and smart grid applications. In this process, contactless broad range electric current measurement has emerged as a hot research field. Under this background, an improved strategy of current measurement has been proposed in this paper to solve the problem of large crosstalk error in scenarios where the measured current is much smaller than the interfering current. In the proposed strategy, two magnetic sensors with a piece of curved magnetic shielding layer constitute the measurement structure, in which the effect contributed by the interference is damped by summing up the magnetic field detected by the two sensors with the opposite sensitive direction and by the magnetic shielding. It has been demonstrated by experiments that this improved strategy can estimate the targeted current with the minimum drift of 0.19 A when the real value of the targeted current is 0 A. The finite-element analysis proves that the deviation is 0.21 A, while the interfering current changes from 1 to 10 kA. Based on these results, the proposed easily installed structure with high accuracy proves its immense potential application of current measurement in smart grid.
A Novel Adaptive Filter for Accurate Measurement of Current with Magnetic Sensor Array

Accurate information about electric current is a key parameter in smart grid. Non-contact measurements are desirable and state of the art. One notable problem with the non-contact current measurement is that they are based on the relationship of electric current and the magnetic field generated by the current, which is susceptible to the superposition of unwanted magnetic field on the magnetic field under measurement. In this paper, a novel adaptive filter with three-sensor array to reduce the effect of the unwanted magnetic field, is proposed. Testing is performed to quantify the consequence of cross talk in alternating current measurement by means of numerical simulations and finite element analysis (FEA) method. The proposed method demonstrates that when the peak value of the current under measurement is from 500 A to 1000 A with interference current changing in a certain range, error between the processed magnetic field and magnetic field under measurement is less than 3 % in numerical simulations and less than 4 % FEA simulations.
Prediction for High Risk Clinical Symptoms of Epilepsy Based on Deep Learning Algorithm

Accurate forecasting of high-risk clinical symptoms, like epileptic seizures, has the potential to transform clinical epilepsy care and to create new therapeutic strategies for individuals in clinical decision support systems. With the development of pervasive sensor technologies, physiological signals can be captured continuously to prevent the serious outcomes caused by epilepsy. However, the progress on seizure prediction has been hindered by the lack of automatic early warning system. The existing research is classifying electroencephalograph (EEG) clips and is distinguishing the clips of onset epileptic seizures. Deep learning is a promising method to analyze the large-scale unlabeled data and to widely spread the clinical treatment and risk prediction. In this paper, we outline a patient-specific method for extracting the frequency domain and time-series data features based on the two-layer convolutional neural networks (CNNs). A data preprocessing method based on the discrete Fourier transform is proposed to convert the time-domain signal of the EEG data to the frequency-domain signal. Long short-term memory networks are introduced in seizure prediction using pre-seizure clips of the EEG dataset, expanding the use of deep learning algorithms with recurrent neural networks (RNNs). Furthermore, the proposed CNN and RNN are compared with the traditional machine learning algorithms, such as linear discriminant analysis and logistic regression, and the evaluation criteria are on the area under the curve. The extensive experimental results demonstrate that our method can effectively extract the latent features with meaningful interpretation and exhibits excellent performance for predicting epileptic preictal state changes, and hence is an effective method in detecting the epileptic seizure.
Study on the Consumption Law of Metal Passivator Irgamet 39 in Transformer Oil

In order to optimize the adding scheme of metal passivator, this paper reported the effects of temperature, DBPC (2,6-di-tert- butyl-4-methyl phenol) concentration, DBDS (dibenzyl disulfide) concentration, copper surface area, paper surface area, oil state and copper corrosion degree on the consumption of metal passivator Irgamet 39 in transformer oil. Irgamet 39 concentration in transformer oil was determined by high performance liquid chromatography (HPLC). The results showed that the temperature is a key factor for Irgamet 39 consumption. Irgamet 39 concentration remains stable at low temperature (below 60 °C) and decreases rapidly at high temperature(above 90°C); paper, fresh copper, corroded copper, oil in service and DBDS increases the consumption rate of Irgamet 39; DBPC has little effect on the consumption of Irgamet 39. According to the protective mechanism of Irgamet 39, the transformer oil and insulation paper are important factors in the consumption of Irgamet 39.
Buffer Vertical Leakage Mechanism and Reliability of 200-mm GaN-on-SOI

A comprehensive study on buffer vertical leakage mechanism and buffer reliability of 200-mm GaN-on-SOI is conducted in this paper. The buffer vertical leakage current versus bias is found to sequentially comprise three ranges of low-field leakage, variable-range hopping, and breakdown. The low-field leakage increase at high temperatures has been proven to be dominated by surface leakage. The stressing voltages of buffer time-dependent breakdown (TDB) measurements have a significant impact on shape factor $\beta $ . Stress-induced leakage current is observed in the wear-out phase of the TDB curves. Stress-and-sense measurements in this phase indicate that defects are generated probably around the threading dislocations, where more localized Joule heating and more defects are created by a positive feedback, and finally, the buffer breaks down due to thermal runway. At last, based on the Weibull plot and E-model, the operating voltage is extrapolated to be 470 V corresponding to an expected lifetime of 10 years at 175 °C, with area scaling to the drain terminal of a 150-mm power transistor and failures scaling to 0.01%. The qualification of GaN-on-SOI is crucial for future 200-V GaN power integrated circuit on this platform.
Trends in the use of new technologies on the example of Polish users

ICTs are playing an increasingly important role in most modern societies. In the current research on the state of development of the information society, on the one hand, access to the Internet and the saturation of households with modern devices are taken into account. On the other hand, the analyses include the level of diversity of Internet activity declared by people using the network. This allows describing demographic and social factors that contribute to digital exclusion resulting from the lack of access to technologies or the lack of competence to use them. These studies are based on the opposition does – does not use Internet technologies or their functionalities, which is an insufficient division for understanding the complexity of digital exclusion. The use of k-means clustering and two-step clustering in research on the use of new technologies will allow to overcome this dichotomy and characterise social categories that use new technologies at different levels and apply them in their everyday practices. This will enable a better recognition of the phenomenon of digital exclusion in Poland. The analyses were based on a set of data collected as part of Social Diagnosis in 2015.
${C}_{\textsf{OSS}}$  Measurements for Superjunction MOSFETs: Limitations and Opportunities

Small-signal measurements of output capacitance (<inline-formula> <tex-math notation="LaTeX">${C_{\textsf {OSS}}}$ </tex-math></inline-formula>) are ubiquitous in power semiconductor datasheets and determine critical features of power converters. For silicon superjunction power MOSFETs (SJs), we report <inline-formula> <tex-math notation="LaTeX">${C_{\textsf {OSS}}}$ </tex-math></inline-formula> measurements with two key anomalies: variation with ac perturbation frequency and hysteresis with dc sweep direction. Using mixed-mode simulations, we attribute the frequency shift to the fundamental SJ structure and find that dc hysteresis is caused by charge trapping from uneven depletion fronts. We show that <inline-formula> <tex-math notation="LaTeX">${C_{\textsf {OSS}}}$ </tex-math></inline-formula> measurements on SJs do not accurately characterize large-signal operation, underestimating stored energy by up to four times and giving no indication of <inline-formula> <tex-math notation="LaTeX">${C_{\textsf {OSS}}}$ </tex-math></inline-formula> losses.
Translating GDPR into the mHealth Practice

The interaction between patients and health providers through mobile apps can potentially improve the efficiency and quality of healthcare. But despite the advantages, the majority of mobile apps provide low or no security protection and there is a lack of security standards and guidelines to support its development with an adequate balance between availability and confidentiality. Since May 2018, this lack of security awareness and measures has to change. With the application of the new General Data Protection Regulation (GDPR), the European residents' personal data processing by third parties will be stricter and more controlled. On the way to understanding how GDPR affects the content and interactions of mHealth apps, this article aims to compare how previous legislation is reflected in the interactions between users and those apps and what key changes must take place now that GDPR is in force. GDPR empowers patients to ask and receive in a simple understandable manner, information about the security measures that are applied to protect their personal data and transparently see how their personal data is processed, by whom and to what purposes. Use-case scenarios are presented to discuss the impact of GDPR key changes in the visual interactions between the user/patient and mHealth apps and how the app content can be adapted to a more objective and uncluttered view. This study provides means to easily and quickly integrate the key privacy and legislation requirements from GDPR into app visualization, improving this way availability, transparency and patients' empowerment.