Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Solutions for Wave Process in Electrical Line with Capacitive, Inductive and Active Load

The methodology for obtaining exact analytical solutions for the waveform regimes in the electric lines with loads with concentrated parameters of various types is justified. As a mathematical model the partial differential equations (telegraph equations) are used and the coefficients of these equations are presented in system of relative units. The system of equations is changed by introducing two new unknown functions, which are functions of voltage and current in the line. The mathematical relations are transformed into equations with one independent function. The new variables represent the Riemann invariants. The boundary conditions for these two functions are described by an algebraic relation and an integral- differential equation. The last equation represents the Cauchy problem for which the solutions have been determined. It was found that solutions for variables (voltage, current and Riemann invariants) are discontinuous functions. A new formulation for the boundary conditions has been proposed. It is proposed and described the process for obtaining the exact solution for the capacitance, inductive and resistive loads. The proposed method is applicable for determining voltage and current as time functions at any point of the power line. Analytical solutions can serve as test solutions for verifying precision of numerical solutions.
Polar-Coded GFDM Systems

Generalized frequency division multiplexing (GFDM) is a block-based non-orthogonal multicarrier modulation scheme proposed for 5G PHY layer. In this paper, to efficiently coordinate the features of interference existing in the GFDM system, we propose a theoretical framework of the polar-coded GFDM (PC-GFDM) system, which allows jointly optimizing the combination of binary polar coding and GFDM modulation. The original GFDM channel is decomposed into multiple bit polarized channels by using a two-stage channel transform. The general modulation partition is performed in the first stage and the bit polarization transform is done in the second stage. Specifically, two schemes are considered for the first stage channel transform, multilevel coding (MLC) and bit-interleaved coded modulation (BICM). Based on the theorem we have proved in this paper that the capacities of channels corresponding to all transmitted symbols are identical, the MLC based PC-GFDM system is designed to optimize the combining of polar codes and GFDM system. Then the BICM based PC-GFDM system is designed to reduce the complexity and processing latency, which yields the suboptimal performance. Simulation results indicate that the proposed PC-GFDM systems significantly outperform the existing turbo-coded GFDM systems because of the joint design between the polar coding and the GFDM modulation.
Impact of Voltage Close Loop Control on Small Signal Stability of Virtual Synchronous Generators

Virtual Synchronous Generators (VSG), as an effective mean to improve the frequency and voltage regulation ability of the photovoltaic (PV) power generation, had aroused wide concern. Different PV-VSG control structures had been conducted in existing researches. One significant distinction between various VSG control structures was whether voltage close loop control (VCLC) was contained in VSG control system or not. To study the impact of VCLC on PV-VSG stability, this paper developed the small-signal models of PV VSGs with and without VCLC. The oscillation modes of both VSGs were calculated based on the developed models, which were applied to investigate the influence from VCLC on VSG damping characteristics as well. On the basis of that, the impact of VCLC on the adaptation and frequency regulation ability of VSG were analyzed. The results indicated that if PV VSG was connected to a low voltage or a weak power grid, the implementing of VCLC was benefit for the oscillation modal characteristics of PV VSG.
Electricity Consumption and Weather Reflect Macro-Economic Status

Electricity is closely related to economy. With the development of society and deployment of green energy, a more complex and close relationship between electricity and economy is coming into being. Electricity consumption and Gross Domestic Product (GDP) are the most intuitive indicator of electricity and macro-economy. On one hand, this paper aims to find out the relationship between electricity consumption and GDP; on the other hand, it builds GDP forecast model to replace the costly traditional economy census. Aiming at predicting economic status, 29 provincial GDP, electricity consumption and weather factors in China during 2006-2017 have been analyzed in this paper. The results indicate that although electricity consumption is bound up with local economic development, and GDP standard could be forecasted better combining with local weather factors such as average temperature. Moreover, the method not only revealed the macro-economy instantaneously with nearly no cost, but also provide a new way to assess risky signal in local economy structure.
Telemetry of Hospitalized High-Risk Patients with Cardiovascular Diseases

The paper presents an ECG telemetry monitoring system including: (i) portable analyzers of the heart activity; (ii) hybrid network architecture with wireless (bluetooth) and wired (RS485 interface) parts; and (iii) central unit. The system is applied on 85 cardio-vascular patients in the Cardiology unit of the Department of propedeutics of internal medicine in the University hospital “Alexandrovska” – Sofia. It has helped for the registration of rhythm and/or conduction disturbances in 89.4 % of the patients which has allowed the application of a timely therapeutic treatment. Serious technical failures have not been reported during the follow-up period of the ECG telemetry monitoring system.
Nonconvex Robust Low-Rank Tensor Reconstruction via an Empirical Bayes Method

Low-rank tensor reconstruction has attracted a great deal of research interest in signal processing, image processing and machine learning. To deal with outliers that are now ubiquitous in many modern applications, a representative approach is to decompose and reconstruct the data into a low-rank component and a sparse outlier component. As surrogate functions of the matrix rank and the sparsity that are non-convex and discontinuous, the nuclear norm and the $\ell _1$ norm are often exploited for recovering higher order tensors and removing outliers, respectively. However, the nuclear norm and the $\ell _1$ norm are loose approximations of the matrix rank and the sparsity, respectively. Furthermore, the tensor nuclear norm is not guaranteed to be the tightest convex envelope of a multilinear rank. In this paper, we develop a new method for robust tensor reconstruction. By capitalizing on the empirical Bayes method, whose performance has been shown to act as state-of-the-art to the performance of standard sparse and low-rank matrix recovery, we formulate the objective function of the empirical Bayes method for robust tensor reconstruction, and then adjust the objective function to facilitate algorithm derivation, while keeping desirable attributes including concavity and symmetry of the objective function of the empirical Bayes method. We demonstrate the superior performance of the proposed algorithm in comparison with state-of-the-art alternatives by conducting experiments on both synthetic data and real data.
Zero Voltage Switching DC-DC Buck Converter with Predictive High Current Mode Control

A Zero Voltage Switching (ZVS) Buck DC-DC converter with predictive high current mode control and control system models are proposed, developed and simulated. The proposed buck topology with predictive high current mode control has advantages, which improve conversion efficiency, current sharing, and cycle-by-cycle current limiting protection, single-pole roll off as the means to achieve high efficiency and no reduction of the output current over the output voltage without reducing throughput power. In this paper the transient process at the start of the converter’s model is simulated, as well as the response of the output voltage regulator to an unexpected change in the load. The proposed circuits are developed in OrCad. Zero voltage switching technique used in a buck converter is more desire and preferable over a hard switched buck converter for low power and high frequency DC-DC conversion applications. In the paper work simulation models of a buck ZVS DC-DC converter and control system model has been simulated and investigated using an OrCad Capture CIS.
Manitoba Rectifier—Bridgeless Buck–Boost PFC

This paper presents a new bridgeless buck–boost power factor corrector with the use of the active virtual ground technique, which is named as the Manitoba Rectifier. The proposed topology can convert the grid ac voltage into a wide range of voltage outputs within a single-stage circuit. It is in a bridgeless structure and simple in design. During the operation, an LC filter is generated at the system input, where continuous grid current is guaranteed in a buck–boost characteristic topology. In addition, the filter capacitor helps to clamp the voltage ripple between the grid and the output bus terminal. Both leakage current and common mode noise are kept in a relatively small value. Thus, a single-stage and low common-mode buck–boost converter system is built. The proposed topology is successfully implemented on an 800 W prototype, and the performance is experimentally verified, which shows good agreement with the theoretical findings.
Study on Machine Learning Techniques In Financial Markets

Portfolios, securities, stock market forecasting, risk management, debt management are all important pillars of the financial world. These pillars rely heavily on adequate and accurate prediction. These are problems that, on the small scale, affect individuals and their financial conditions and, on the larger scale, can be detrimental to a country’s financial well-being. This paper, is an attempt at understanding the various algorithms and platforms involving Machine Learning in Financial Markets and reaching informed conclusions on parameters like accuracy, efficiency, speed and usability. In this paper, primarily, different trading techniques are introduced and their effectiveness in quantitative trading and, in general, finance to generate alphas is observed. These techniques, as observed, are categorised by their reliance on Neural Networks, Support Vector Machines and other quantitative variables in finance. Classifications on the basis of supervised and unsupervised techniques and K-Mean clustering are also made. Further, this paper also delves into the hitherto unpredictable and unmovable phenomena in market and public psychology and attempts to suggest a viable solution to it.
A Review on Virtual Power Plant Concept, Application and Challenges

Due to the high penetration of distributed energy resources (DER) in the power grid and electricity market, numerous papers have focused on the new idea of virtual power plant (VPP). VPP can integrate DERs such as distributed generation (DG), energy storage, controllable loads, electric vehicle (EV) to provide capacity and ancillary services to the grid operations, sell power to electricity wholesale markets, enhance the overall system economics and reliability, promote efficient optimization in resources, and facilitate renewable integration to the grid. This paper reviews the VPP in terms of concept, applications and challenges and proposes a new concept of VPP. In the paper, 9 typical VPP projects are introduced, which are mainly distributed in Europe, USA and Australia. In addition, VPP also brings challenges in resource allocation, operation and control system, communication technology and power transactions.
Wind Turbine Generator Fault Detection Based on Multi-Layer Neural Network and Random Forest Algorithm

In order to achieve fast and accurate fault diagnosis of wind turbine generators, a fault diagnosis algorithm based on multi-layer neural network (MNN) and random forest algorithm (RF) is designed based on the data of supervisory control and data acquisition (SCADA). The algorithm consists of two parts: The first part is the fault detection algorithm based on MNN. The reconstructed value of SCADA data is obtained by MNN, and the change trend of the reconstruction error and it exceeding the threshold are analyzed to predict the Wind Turbine (WT) fault and extract the fault sample. The second part is based on the random forest fault identification algorithm to establish an RF fault identification algorithm. The example verification proves that the MNN algorithm can detect the fault of the wind turbine generator earlier, and the RF identifies the fault type more accurately than other basic algorithms. It is concluded that the proposed diagnostic method based on multilayer neural network and random forest algorithm has good performance.
Detection and Classification of Groundnut Leaf Diseases using KNN classifier

One of the ultimate key factors besttow less yield is disease attack. The groundnut plant disease such as fungi, soil borne and viruses. In this paper, we are given that software determination to robotically classify and categorize groundnut leaf diseases. This method will improve production of crops. It comprises of number of steps viz. image acquisition, image pre-processing, segmentation, features extraction and classifier using K Nearest Neighbor (KNN). To increase performance of existing algorithm the SVM classifier is replaced with KNN classification. In this paper we categorized only 4 different disease using KNN classifier algorithm.
Regular Expression Based Medical Text Classification Using Constructive Heuristic Approach

Medical text classification assigns medical related text into different categories such as topics or disease types. Machine learning based techniques have been widely used to perform such tasks despite the obvious drawback in such “black box” approach, leaving no easy way to fine-tune the resultant model for better performance. We propose a novel constructive heuristic approach to generate a set of regular expressions that can be used as effective text classifiers. The main innovation of our approach is that we develop a novel regular expression based text classifier with both satisfactory classification performance and excellent interpretability. We evaluate our framework on real-world medical data provided by our collaborator, one of the largest online healthcare providers in the market, and observe the high performance and consistency of this approach. Experimental results show that the machine-generated regular expressions can be effectively used in conjunction with machine learning techniques to perform medical text classification tasks. The proposed methodology improves the performance of baseline methods (Naive Bayes and Support Vector Machines) by 9% in precision and 4.5% in recall. We also evaluate the performance of modified regular expressions by human experts and demonstrate the potential of practical applications using the proposed method.
Deep Learning based Ensemble Convolutional Neural Network Solution for Distributed Denial of Service Detection in SDNs

Software defined networks (SDNs) are considered to be the future of networking as it decouples the control plane from the forwarding logic and fulfils the escalating demand of faster and more proficient networks. However, emergence of SDNs also bring security challenges to its centralized architecture such as Distributed Denial of Service (DDoS) attack. Therefore, the need for a timely detection of large-scale sophisticated DDoS attack is of paramount concern for subsequent countermeasures. This paper presents a deep learning (DL) based CNN (Convolutional Neural Network) ensemble solution for efficient detection of DDoS in SDNs. The proposed framework's performance is evaluated through standard evaluation parameters with state-of-the-art Flow-based dataset (ISCX 2017). Empirical results of the proposed framework demonstrate high attack detection accuracy: 99.48% in minimum time with conducive computational complexity.
Recovery of High-Level Intermediate Representations of Algorithms from Binary Code

One of the tasks of binary code security analysis is detection of undocumented features in software. This task is hard to automate, and it requires participation of a cybersecurity expert. The way of representation of the algorithm under analysis strongly determines the analysis effort and quality of its results. Existing intermediate representations and languages are intended for use in software that either carries out optimizing transformations or analyzes binary code. Such representations and intermediate languages are unsuitable for manual data flow analysis. This paper proposes a high-level hierarchical flowchart-based representation of a program algorithm as well as an algorithm for its construction. The proposed representation is based on a hypergraph and it allows both automatic and manual data flow analysis on different detail levels. The hypergraph nodes represent functions. Every node contains a set of other nodes which are fragments. The fragment is a linear sequence of instructions that does not contain call and ret instructions. Edges represent data flows between nodes and correspond to memory buffers and registers. In the future this representation can be used to implement automatic analysis algorithms. An approach is proposed to increasing quality of the developed algorithm representation using grouping of single data flows into one flow connecting logical algorithm modules.
Continuous Antenna Calibration for the Transient Array Radio Telescope

We describe the current state of antenna design for the all-sky Transient Array Radio Telescope, and describe the algorithms used to continuously calibrate the antenna array gains and phases of each array element during normal operation. We show how a basin-hopping high-dimensional minimization algorithm can calculate the antenna calibration parameters effectively from real-time visibilities, and the known position and signal-structure of the GNSS satellites. Finally we describe current attempts to integrate individual antenna models that capture direction dependent effects into the calibration procedure.
AndMFC: Android Malware Family Classification Framework

As the popularity of Android mobile operating system grows, the number of malicious software have increased extensively. Therefore, many research efforts have been done on Android malware analysis. Besides detection of malicious Android applications, recognizing families of malwares is also an important task in malware analysis. In this paper, we propose a machine learning-based classification framework that classifies Android malware samples into their families. The framework extracts requested permissions and API calls from Android malware samples and uses them as features to train a large set of machine learning classifiers. To validate the performance of our proposed approach, we use three different malware datasets. Our experimental results show that all of the tested models classify malwares efficiently. We also make a study of detecting unknown malwares that never seen before and we notice that our framework detects these malwares with a high accuracy.
Gateway OPC UA/Modbus applied to an energy recovery system identification

In this paper, a gateway OPC unified architecture (UA)/Modbus that runs on a Raspberry Pi 2 is presented. This piece of software allows using a PLC or other low-cost hardware in applications that were banned for this type of devices. In this way, the possibilities for instrumentation and control applications are expanded. As an application tool, data acquisition by means of the OPC UA/Modbus gateway, in this work, is used in the identification of an energy recovery system. The energy system consist in using a centrifugal pump in a twofold way. In the first one, the pump is used as a device controlling the water distribution pressure, and, in second place, the asynchronous motors is used as a generator which gives back electrical energy to the grid.
Electromagnetic and mechanical analyses of a 3D-printed ka-band integrated twist and orthomode transducer

In this paper the design and 3D manufacturing of a Ka-band integrated twist and orthomode transducer is discussed. The prototype has been manufactured in the AlSi10Mg alloy through SLM. The device has been measured showing a significant agreement with the predicted results. Moreover, exploiting a computed scan tomography the accuracy on the internal channel has been analysed and a sensitivity analysis has been carried out in order to understand the repeatability of the manufacturing process.
The Influence of Pantograph Arcing Radiation Disturbance on LTE-R

Long Term Evolution (LTE) has the characteristics of high speed, low delay, high mobility and high security. The International Railway Union (UIC) has confirmed LTE as the next generation of railway wireless communication technology, while the 400 MHz frequency band is currently available as a band resource in China. However, considering that the disturbance generated by pantograph arcing is strong in low frequency band, it is necessary to study the disturbance of LTE-R system under the pantograph arcing radiation disturbance. In this paper, the radiation characteristics of the pantograph arcing are studied by theoretical numerical simulation of electromagnetic field. Meanwhile, the antenna-coupling model at the roof of the train is established for analyzing the disturbance received by LTE-R antenna, which is based on electromagnetic computation software. Furthermore, the TD-LTE downlink simulation model is established via analyzing the basic principles of the LTE physical layer downlink including Orthogonal Frequency Division Multiplexing (OFDM) and Hybrid Automatic Repeat Request (HARQ). The influence of pantograph arcing on the Block Error Rate (BLER) and throughput of LTE-R system is analyzed by using the simulation model of TD-LTE downlink, and the conclusion proves that the simulation results are consistent with the theoretical analysis. It can be concluded that the TD-LTE system will be interfered by pantograph arcing in 400 MHz frequency band, leading to throughput degradation.
Static Security Assessment of Power System Considering Governor Nonlinearity

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.