Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Review of Data Models for the Big Data Problem

Nowadays, data are generated in a continuous streaming manner as the inputs of various applications. The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, e-commerce, social networks, and online communities. The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem. The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on. Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems’ design, DB languages, etc.) rely on. So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data. It is also the essence in various areas of cloud migration, web-scale, and so forth. In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another. To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.
Multi Objective Low Carbon Economic Dispatch Of Power System Considering Integrated Flexible Operation Of Carbon Capture Power Plant

Carbon capture power plant is the key technology to respond to low-carbon economy and realize low-carbon development of power system. However, the static operation mode of carbon capture power plant has the coupling problem of capture and regeneration process, and its low-carbon effect needs to be further improved. In view of the above problems, this paper first deeply excavates the low-carbon and economy of the integrated flexible operation mode of carbon capture power plant, and makes a detailed modeling of the integrated flexible operation mode of carbon capture power plant; Secondly, considering the economic benefits and low carbon level of the system, a day ahead scheduling model with comprehensive cost and overall carbon emission optimization is constructed. Finally, an IEEE-39 bus system is simulated to verify the effectiveness of the proposed scheduling model.
A Single BJT Bandgap Reference With Frequency Compensation Exploiting Mirror Pole

In this article, we propose a bandgap reference (BGR) with a single bipolar junction transistor (BJT) branch and a proportional-to-absolute-temperature (PTAT)-embedded amplifier. Having fewer BJT and passive components, the proposed BGR uses less power and area compared to the conventional BGR that uses multiple BJTs and resistors. As the proposed BGR achieves temperature compensation by employing a feedback loop that has two gain stages, frequency compensation is required. We propose frequency compensation scheme where a compensation capacitor is connected between the BGR output and a current mirror node in the PTAT-embedded amplifier. By using this scheme, location of a mirror pole associated with the current mirror node can be optimized to achieve both improved stability and low power consumption. Fabricated in 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS, the proposed BGR achieves temperature coefficient of 26.3 ppm/°C over −40 °C–140 °C; voltage accuracy (<inline-formula> <tex-math notation="LaTeX">$\sigma /\mu $ </tex-math></inline-formula>) of 0.33%; and power supply rejection (PSR) of −52 dB, −44 dB at 100 Hz, 1 MHz respectively; while occupying 0.0082 mm<sup>2</sup> and consuming 192 nW without startup circuit and trimming.
3-D Steady Heat Conduction Solver via Deep Learning

Conventional numerical heat conduction solvers are exceedingly computationally expensive and memory demanding. Recent advances in deep learning have witnessed its extensive applications in computational physics field. Compared to traditional methods, deep learning techniques emerge superior computational efficiency, providing a substitute for speeding up the calculation. In this paper, we propose an innovate deep learning framework to predict the 3D temperature field in a cube region filled with random objects of various geometries and materials. The framework is capable of resolving both passive and active heat conduction problems. After being fully trained, it can achieve a similar precision compared to the finite element method (FEM), while the calculation speed is accelerated by about 280 times. Furthermore, the deep learning model has demonstrated robust generalization ability in predicting real-world objects not existing in the data set. We believe that the framework paves the way for solving complex heat conduction problems in engineering, as well as inverse problems in the future.
Accident Detection and Notification System Using AWS

With the constant advancements in the automotive industry and the increase in traffic volume, chances of fatality in road accidents have increased. This poses a dire need to develop a good and reliable solution to reduce the fatalities. One way to achieve this goal is to build a system that detects road accidents and notifies medical services as soon as possible. The aim of this research paper is to provide a robust accident detection and notification system where the data collected from the accident site is communicated to an emergency medical service provider as well as the victim's emergency contacts such as family members or close friends. The system described in this paper successfully takes values from accelerometer, detects a crash and sends a notification, a text message and an email using AWS to the concerned parties with the latency of approximately 20 seconds and this was achieved by using an internet connection with a speed of 3.76 MBPS. Since AWS is reliable, scalable and fast, the cloud infrastructure provides a better platform for the system.
A Pitman-Yor Process Self-Aggregated Topic Model for Short Texts of Social Media

In recent years, with the rapid growth of social media, short texts have been very prevalent on the internet. Due to the limited length of each short text, word co-occurrence information in this type of documents is sparse. Conventional topic models based on word co-occurrence are unable to distill coherent topics on short texts. A state-of-the-art strategy is self-aggregated topic models which implicitly aggregate short texts into latent long documents. But these models have two problems. One problem is that the number of long documents should be defined explicitly and the inappropriate number leads to poor performance. Another problem is that latent long documents may bring non-semantic word co-occurrence which brings incoherent topics. In this article, we firstly apply the Chinese restaurant process to automatically generate the number of long documents according to the scale of short texts. Then to exclude non-semantic word co-occurrence, we propose a novel probabilistic model generating latent long documents in a more semantically way. Specifically, our model employs a pitman-yor process to aggregate short texts into long documents. This stochastic process can guarantee that the distribution between short texts and long documents following a power-law distribution which can be found in social media like Twitter. Finally, we compared our method with several state-of-the-art methods on four real short texts corpus. The experiment results show that our model performs superior to other methods with the metrics of topic coherence and text classification.
Seismic Vulnerability Analysis of High Voltage Power Transformer

Transformers are one of the most essential electrical equipment in a substation, assessing their seismic withstand capacity is of great significance to sustain the normal function of substation. To evaluate the earthquake resistance performance of a 500 kV transformer, a finite element model of a whole transformer system composed of tank, turrets and bushings was first established. And dynamic properties and time history analysis were carried out to find its most vulnerable points. Then seismic vulnerability analysis can be conducted using the incremental dynamic analysis (IDA) method. Results show that the dynamic amplification of the tank and turret to the bushing is much larger than the threshold of 2.0 specified in the relevant codes, and the root section of the high voltage (HV) bushing is the most vulnerable and it is not likely to be acceptable for the ground motions with a peak ground acceleration (PGA) of 0.3 g, which is even less than the local fortification intensity. Therefore, the influence of transformer tank and turrets can not be ignored when evaluating the seismic performance of a transformer bushing, and some retrofitting measures should be taken to enhance their seismic withstand capacity.
DC-DC High Voltage Gain Switched Capacitor Converter With Multilevel Output Voltage and Zero-Voltage Switching

This paper presents the concept and results of a novel resonant DC-DC converter which achieves high voltage gain at a low number of utilized switches, low voltage stress on transistors, ZVS and ZCS operation, high efficiency and variable voltage gain. The converter uses resonant switched-capacitors (SC) circuits with five switches which allows it to reach a six-fold voltage gain. By the application of adequate switching patterns, seven levels of voltage adjustment can be achieved. The basic operation mode of the converter is at zero current switching (ZCS) but a part of the existing transitions can be switched-on at a zero drain-source voltage. Zero voltage switching (ZVS) allows for a reduction of $C_{\mathrm {oss}}$ losses and a decrease in disturbances of voltages and currents. In the proposed converter, voltage stress on switches are noticeably lower than the output voltage which is beneficial from viewpoint the cost reduction and switching quality improvement. The converter output stage consists of two series connected capacitors which allows for a supply of 3-level NPC inverters. Resonant inductors, used in the proposed topology, store very low amount of energy and their volume is significantly lower than in case of chokes typically used in switch-mode converters. Qualities of the converter such as low switch count, efficiency, ZCS and ZVS operation as well as voltage gain variation are demonstrated in this paper. Furthermore, an impact of MOSFET transistors type on the efficiency of the converter is investigated. The design based on low $C_{\mathrm {oss}}$ superjunction (SJ) MOSFETs is compared with the implementation where $R_{\mathrm {ds(on)}}$ of switches is minimized. Due to low voltage stress, application of Schottky rectifiers is also demonstrated in the converter. The concept of the topology and switching patterns is verified by the results of analysis, simulations and experiments.
Learning a Secondary Source From Compressive Measurements for Adaptive Projection Design

Recent work has established that the gradient of the mutual information (MI) in Gaussian channels with input noise can be used for projection design in a compressive sensing (CS) scenario with two independent, Gaussian mixture (GM)- distributed inputs. The resulting CS projection matrices have been shown to be capable of controlling the input-output MI terms for the two inputs. One downside of such information-theoretic strategies is their reliance on access to a priori knowledge of the input source statistics. In this paper, we assume that the GM distribution of a primary input is known and that the GM distribution of a secondary input is unknown. We derive a methodology for the online training of the distribution of the secondary input via compressive measurements and illustrate that once the distribution of this secondary source is known, we can use projection design to control the input-output MI of the system. We demonstrate through simulations the various performance trade-offs that exist within the proposed methodology.
A Resonant Frequency Tracking Technique for LLC Converter-Based DC Transformers

Large circulating current and reactive power exist for the <italic>LLC</italic>/<italic>CLLC</italic> converter in the case that <italic>f</italic><sub>s</sub> < <italic>f</italic><sub>r</sub>, where <italic>f</italic><sub>s</sub> is the switching frequency and <italic>f</italic><sub>r</sub> is the resonant frequency; on the other hand, in the scenario of <italic>f</italic><sub>s</sub> > <italic>f</italic><sub>r</sub>, the switching losses for both primary and secondary semiconductor devices are increased due to the large turn-<sc>off</sc> current and high <italic>di</italic>/<italic>dt</italic> related reverse recovery issue, respectively. When operating at the natural resonant frequency of inductor and capacitor or <italic>f</italic><sub>s</sub> = <italic>f</italic><sub>r</sub>, the regulation capability for <italic>LLC</italic>/<italic>CLLC</italic> converter is lost since the voltage conversion ratio is constant. This application is also known as dc transformer (DCX), which has been widely adopted in energy storage systems as interconnection between different units. However, due to the variation of circuit parameters, the switching frequency is different from the resonant frequency, which results in converter efficiency degradation and loss of output voltage regulation capability for two-stage applications. In this article, by sensing the output voltage and input voltage, the converter voltage conversion ratio is computed on-line. Based on the comparison result between the calculated gain and unity, the switching frequency is adjusted accordingly and the resonant frequency tracking is achieved. In addition, multiple frequency adjustment step strategy is proposed to improve the tracking speed. Various scenarios are emulated in the experiment part to examine the effectiveness of the solution.
A Fast Converged Voltage Control Method based on Deep Reinforcement Learning

Keeping the voltage level in desired range is of great importance to power grid’s stability. Thus, the voltage control is one of the most important task in daily power grid’s operation. However, with the integration of distributed energy resources, demand response mechanism, and new devices, ensuring the stability of power grid becomes a challenging task. The traditional secure and economic operation, which is defined off-line, should be re-determined. In this article we propose a deep reinforcement learning based fast converged autonomous voltage control method, along with well-defined training paradigm and fine-tuned hyper parameter. A IEEE 14-bus case is used for testing the applicability and performance of the method. The result shows that our method has enough capability of controlling the voltage level in power grid with high convergence speed.
Drowsiness Detection with OpenCV

Dozing off while driving is one of the major causes of the road accident. It can't be completely avoided but can be prevented with this model. Drowsiness detection is a technology that is used to prevent riders from falling asleep during driving. This technology brings protection to riders and co-passengers. By using an IR Camera in Infront of the driver, the algorithm can monitor the area enclosed between the eyes of the driver. When the area falls under curtain measurement for several consecutive frames then the system triggers an alarm to warn the driver to take control of the vehicle. When the driver opens back their eyes and the area goes above the safe region the alarm stops. Thus, this could stop the driver from dozing and save lives. The algorithm gives 96% accuracy.
Real-time Co-simulation Design of Micro-grid and Information Communication System Based on Multi-agent System

In future, demand response scenarios, a multitude of different types of resources are potentially to be used, e.g. electric vehicles, flexible residential loads and battery storage systems. In order to solve the problem of real-time communication of data among the various resources, it is likely to be using different communication protocols. An aggregator utilizing several types of resources is faced with a problem of semantic interoperability. This paper addresses this challenge by presenting a testbed consisting of a microgrid model integrated with several controllers communicating with industrial grade protocols for demand response, including IEC 61850, OpenChargePoint protocol (OCPP), OpenADR and UDP.
Improved Back-Stepping Control for Nonlinear Small UAV Systems With Transient Prescribed Performance Design

This paper proposes an improved back-stepping control approach and its application to small nonlinear UAV control systems with uncertainties such as external disturbance. Unlike traditional back-stepping control methods, the idea of prescribed performance function (PPF) is incorporated into the control design, such that both the transient and steady-state control performance can be strictly guaranteed. Moreover, we design a novel tracking differentiator to avoid the “differential expansion” problem well caused by the calculation of derivative. Significantly, the function approximators (e.g. neural networks) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. Finally, the numerical simulation verifies the convergence and robustness of the system, and the results show that the control strategy can obtain better transient and steady-state performance.
Reactive power sharing control strategy of multi-inverter based on weak communication

The voltage source inverter used droop control has the characteristics of no communication, plug and play and high flexibility. Since the impedance in the line cannot be accurately measured, if multiple inverters are connected in parallel, the reactive power in parallel system cannot be accurately distributed in proportion. Therefore, a reactive power distributing control strategy of multi-inverter under weak communication conditions is proposed. First, the reactive power generated by each inverter is calculated. Then the impedance between the AC bus and each inverter is adaptively matched according to reactive power deviation. The matching algorithm will stop, when the reactive power is less than the specified threshold. Finally, the virtual impedance parameter of each inverter is adjusted to a fixed value. The proportional distribution of reactive power will no longer rely on communication. Therefore, the proportional distribution of the reactive power of multiple inverters is realized in the weak communication scenario.
Performance Analysis of UTB and Nanowire Tunnel FET by varying various Device Intrinsic Parameters

This paper systematically investigates three types of Tunnel Field Effect Transistors (TFET) namely, the single-gated Ultra-Thin Bodies (UTB), double-gated UTB, and the gate-all-around nanowire TFET by varying different parameters and performing simulations to understand the underlying physics. Channel length, channel thickness, and oxide thickness are the three parameters that are varied to know the effects on the drain current (ID) - gate voltage (VG) characteristics, the ON-state and OFF-state curves, and complex band structures. Performance analysis of TFETs constructed with various materials has been conducted in terms of their ON-OFF current ratios. The simulations have been accomplished using the Tunnel FET compact model tool based on analytic modeling and the WKB method. It can be observed that nanowire provides greater OFF current than double gate UTB. GaAs nanowire TFET provides approximately 93% more OFF current than GaAs UTB TFET. The UTB TFET shows 90.7 % more ON current compared to the GaAs nanowire TFET with other parameters at optimal range and oxide thickness of 5nm. Current depends on the potential barrier between the valence band of source and conduction band of intrinsic region. It is observed that on increasing the channel thickness and oxide thickness the ON-OFF current ratio decreases. In comparison, when the channel length is increased, there is a smaller drain current.
Risk Assessment for Renewable Energy Penetrated Power Systems Considering Battery and Hydrogen Storage Systems

Energy storages can significantly relieve the pressure of the power system brought by a large amount of renewable energy generation. Under this situation, the risk assessment method becomes critical. In this paper, an explicit model for diverse energy storages with battery and Hydrogen Storage Systems (HSS) is built. Further, an optimal load shedding model by utilizing the sequential Monte Carlo (SMC) method is proposed to assess the risk of the power system with diverse energy storages. Then, the proposed method is test on a power system which is adapted from the IEEE 24-bus system. The numerical results show that diverse energy storages can improve risk assessment results of the power system.
A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

For dealing with the electricity theft detection in the smart grids, this article introduces a hybrid deep learning model. The model tackles various issues such as class imbalance problem, curse of dimensionality and low theft detection rate of the existing models. The model integrates the benefits of both GoogLeNet and gated recurrent unit (GRU). The one dimensional electricity consumption (EC) data is fed into GRU to remember the periodic patterns of electricity consumption. Whereas, GoogLeNet model is leveraged to extract the latent features from the two dimensional weekly stacked EC data. Furthermore, the time least square generative adversarial network (TLSGAN) is proposed to solve the class imbalance problem. The TLSGAN uses unsupervised and supervised loss functions to generate fake theft samples, which have high resemblance with real world theft samples. The standard generative adversarial network only updates the weights of those points that are available at the wrong side of the decision boundary. Whereas, TLSGAN even modifies the weights of those points that are available at the correct side of decision boundary that prevent the model from vanishing gradient problem. Moreover, dropout and batch normalization layers are utilized to enhance model’s convergence speed and generalization ability. The proposed model is compared with different state-of-the-art classifiers including multilayer perceptron (MLP), support vector machine, naive bayes, logistic regression, MLP-long short term memory network and wide and deep convolutional neural network. It outperforms all classifiers by achieving 96% and 97% precision-recall area under the curve and receiver operating characteristics area under the curve, respectively.