Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-time Online Aircraft Neural Network System

In order to meet the information processing requirements that large amount of heterogeneous input data are in the real-time flight process of aircraft, a neural network is proposed in this paper, including convolution fixed-point sliding IP core, pooling compression quantization IP core and fully connected compression fusion IP core. Heterogeneous sensor data of the aircraft as the input of the system; The recognized result serves as the output of the system. Convolution of sliding window IP core can quickly extract data features by eliminating redundant data sliding window; Pooling compression quantization IP core, using compression quantization technology, improves system execution efficiency; Fully connected compressed fusion IP core is compressed fusion after reduction and quantification, whose output meets the requirements of high reliability and low power consumption of the aircraft online intelligent integration design.
Tracking Maritime Cooperative Target with Real Time Motorized SAR Antenna

Since 2017, ONERA uses 2-axes motorized antenna to collect SAR images on various geometric acquisitions. Recently, a system has been developed to track maritime targets using the automatic identification system to follow them during illumination time. This paper describes each component of the system and presents all design and implementation steps.
The QoS Improvement Using CDN for Live Video Streaming with HLS

Nowadays, people commonly use streaming technology to enjoy audio or video broadcasted from the server. With this technology, they do not need to download the content first. But, they can directly enjoy the content through the streaming mechanism. Generally, this technology requires high bandwidth to maintain its Quality of Service (QoS) to stay at a good level. The problem can occur, such as packet loss because of limited bandwidth supported by the network provider. This condition can reduce the quality of the content delivered. One technology that can be used to improve the quality of content delivered by streaming service is CDN (Content Delivery Network). This study is going to improve the quality of live video streaming by using CDN. The global CDN infrastructure used is Cloudfront, supported by AWS (Amazon Web Services). The broadcaster is located in Bandung, Indonesia, and the client is placed in Tokyo, Japan. HTTP Live Streaming (HLS) format is performed for content streaming. It is proved CDN can increase the QoS of content delivered. The result presents live video streaming with CDN has better achievement than the other one without CDN. The broadcasting of live video streaming gives a throughput average of 4452.6 kbps and a packet loss ratio average of 0.08% by using CDN. Without using CDN, it presents the throughput average of 3990.4 kbps and the packet loss ratio average of 0.33%.
Direction-of-arrival Estimation Based on Direct Data Domain Approach for Coprime Array

Direction-of-arrival (DOA) estimation for coprime array, which promises an enlarged array aperture and enhanced degrees-of-freedom (DOFs), is expected to operate on a single snapshot of the derived coprime coarray that contains holes. In this paper, we propose a coprime array DOA estimation algorithm that is based on the single-snapshot direct data domain (D3) approach, where DOAs are estimated from both the nonuniform samples of the coprime coarray, and the interpolated samples of the holes. Simulation results demonstrate the advantages of the proposed algorithm in achievable DOFs, estimation accuracy and computational complexity.
Effect of Thermal Pretreatment on Anaerobic Digestion Performance of Recycled Paper Sludge

The anaerobic digestion (AD) experiment for recycled pulp and paper sludge (RPPS) was carried out by a CSTR digester “Continious Stirred Tank Reactor” under mesophilic conditions (37 °C) at laboratory scale. The objective of this study is to evaluate the feasibility of AD technology of effluents from the RPPS, and applying a thermal pretreatment to the substrate studied in order to improve methane production and biodegradability, as well as monitoring and control process for the two experiments at laboratory scale. It can be seen that thermal pre-treatment has contributed to a significant improvement in methane potential and biodegradability rate. Indeed, these two parameters have increased from 105 to 116 NmL CH4 /g SVadded and from 75 to 81% respectively. The pH average is about 7.83. In these mesophilic conditions, the results got show that AD is a good solution for the treatment of this type of industrial waste.
Analysis of high frequency current at the sampling instant of SPMSM Based on High Frequency signal Injection method

This paper presents the influence factors of the high frequency current at the sampling instant based on the high frequency square-wave-type voltage injection method. Due to the use of SVPWM control strategy, the mathematical model of the surface-mounted permanent magnet synchronous motor (SPMSM) under the high frequency square-wave-type voltage injection method is established. Further, it can be deduced that the high frequency current at the sampling instant is affected by the amplitude of the injection voltage, DC-link voltage, switching period, estimated rotor position and motor parameters. Through changing these variables, the high frequency current at the sampling instant can be increased to guarantee the higher signal to noise ratio and the accuracy of the position tracking. Finally, the analysis are verified by simulation results.
Analysis of the Influence of the Channel Model on the Optimum Switching Points in an Adaptive FEC System in a Nakagami Fading Channel

Defining optimum switching points between neighboring modulations or error correction codes is essential to optimize the performance of adaptive modulation and coding systems. This paper analyzes the influence of the fading channel model to determine the optimum switching points of an adaptive Forward Error Correction (FEC) scheme in a wireless network with a Nakagami-m fading channel. The optimum switching points are determined based on three performance criteria: maximum throughput, minimum delay, and power metric. Thus, the influence of the performance criterion to determine the optimal switching points is also analyzed.
A Complex-Valued Convolutional Neural Network with Different Activation Functions in Polarimetric SAR Image Classification

It is well known that the activation function and the gradient descent optimization algorithm have a great influence on convolutional neural network (CNN). Based on the Adam optimization algorithm, this paper proposes a CvAdam optimization algorithm suitable for complex-valued convolutional neural network (CV-CNN), and then in the typical polarization SAR image classification task, Adam and CvAdam were compared using four different activation functions of sigmoid, tanh, Leakey-ReLU and ELU. Experiments on the benchmark dataset of Oberpfaffenhofen show that CvAdam performs better than Adam in both convergence speed and accuracy, no matter which activation function is used.
Efficient Model For Automated Home Management System

With in a household, a home automation system automates the majority of electronic and electrical tasks. This uses a mix of hardware and software to control and manage appliances and devices inside a home. The fundamental purpose of tracking electronic devices in the modern world through the use of the Internet of Things (IoT) is to regulate them according to situational requirements. The need for efficient monitoring is greater with the advancement of technology as it optimizes efficiency and saves excessive power and resource wastage. Turning on lights during daytime can be avoided in this way. NodeMCU is very popular in Home Automation. It’s Wi-Fi features and support for Arduino IDE makes it easier for IoT Applications. Home appliances can be controlled using the Android Application which is Blynk App.
Research on Stereoscopic Monitoring and Positioning Technology of Mountain Fire in EHV Transmission Lines

The HVDC/AC transmission has entered a period of rapid development in China. HVDC/AC transmission lines pass through mountainous and forest areas, and mountain fire threaten the safe and stable operation of the lines. This paper proposed a mountain fire prevention and control strategy for different circumstances, compensates for the disadvantages of satellite and surface terminal monitoring. Proposed a ignition point identification model and a multi-dimensional credibility models, to improve the accuracy of mountain fire monitoring. Finally, develop a stereoscopic mountain fire monitoring system for HVDC/AC transmission lines suitable for large geographical areas based on satellite and surface terminal monitoring, it improves the accuracy of mountain fire monitoring, reduce costs and increase efficiency.
Study on Mesh Adaptive Direct Search Algorithm for Distribution Network Reconfiguration with Distribution Generators

Firstly, based on the distribution network connection model provided by IEC 61970-301 CIM, the mapping rules have been established, and the equivalent reconfiguration topology of the actual distribution network has been obtained, which omits the unnecessary switches to decrease the coding dimension. Secondly, aiming at the distribution network reconfiguration with distribution generators (DG), the multi-objective model, including power loss reduction, voltage deviation, and DG consumption, has been established; and the mesh adaptive direct search (MADS) algorithm has been applied, which has been improved by forming the self-adaptive updating mechanism for mesh size parameter. Finally, the algorithm has been programmed in Visual Studio 2015 with C++ tool. And combined with IEEE 33-bus distribution network, the improved method has been verified the feasibility and effectiveness.
Improved Predictive Model Controller for Voltage Regulation in Standalone Power Generation System

This paper discusses an improved predictive model control strategy with a prediction horizon of one sampling time for three phase pulse width modulation (PWM) converter used for standalone power generation system in the presence of sudden load disturbances and sampling time variations. The proposed control law uses the system model to predict directly the behavior of the output voltage at each sampling prediction interval for all switching state. Next, a cost function is evaluated as a criterion to choose the switching state that will be applied in the next sampling prediction instant. To regulate constant and sinusoidal the AC voltage at the point common coupling (PCC), an LRC high harmonics filter is used in the output of the PWM converter for the power quality improvement. The performances of the proposed system and their control strategies are verified using MATLAB/Simulink.
Fault line selection based on cross wavelet and Romanovsky Criterion

A fault line selection method for small current grounding systems based on cross wavelet transform (XWT) and Romanovsky criterion is proposed. The transient zero-sequence current of each branch is analyzed by cross-wavelet transform. The characteristic time-frequency window with significant correlation is adaptively determined according to the cross-wavelet power spectrum, and the average cross-wavelet phase forming feature vector in the feature time-frequency window is calculated. Based on the coarse error theory, the fault line selection criterion based on the Romanovsky criterion is constructed. The line with absolute deviation greater than the critical value is the fault line. If all absolute deviations are not greater than the critical value, it is the bus line fault. Simulation analysis proves that the proposed method is accurate and reliable and suitable for various fault conditions.
Intelligent Fault Detection System for State Grid IoT Equipment Based on Edge Computing

At present, the amount of data generated by IoT terminal devices has become extremely large. The traditional centralized data processing model centered on the cloud computing model has been unable to support the current power IoT system. This paper proposes an intelligent fault detection system for power IoT devices based on edge computing for the defects of the cloud center computing model. The system model is divided into cloud center layer, edge layer, and field layer. In the field layer, the DTW complement algorithm is used to complement the sensor data; the DNN algorithm is used to implement the fault primary classification in the edge layer, and the classification result is uploaded to the cloud center layer; the correlation between the data is used in the cloud center layer to realize the fault classification. After that, an experiment was designed to verify the efficacy of the modified model.
Arduino Based Platform for Managing a PV Battery Charge

The battery management system (BMS) is indispensable for photovoltaic installations with storage because they optimize the use of batteries, protect them against damage and prolong their life. This paper presents a hardware platform for the experimentation of battery storage station. This platform contains a BMS for lead-acid batteries built around an Arduino Mega. The software of BMS allows, in addition to the functions of battery management and load control, to store the data in an excel sheet for later use.
A Malware Variant Resistant To Traditional Analysis Techniques

In today’s world, the word malware is synonymous with mysterious programs that spread havoc and sow destruction upon the computing system it infects. These malware are analyzed and understood by malware analysts who reverse engineer the program in an effort to understand it and provide appropriate identifications or signatures that enable anti-malware programs to effectively combat and resolve threats. Malware authors develop ways to circumvent or prevent this analysis of their code thus rendering preventive measures ineffective. This paper discusses existing analysis subverting techniques and how they are overcome by modern analysis techniques. Further, this paper proposes a new method to resist traditional malware analysis techniques by creating a split-personality malware variant that uses a technique known as shadow attack. The proposal is validated by creating a malware dropper and testing this dropper in controlled laboratory conditions as a part of the concept of proactive defense.
Cognitive Approach for Heart Disease Prediction using Machine Learning

Prediction of patterns to prevent and control diseases is a challenging and a prominent requirement in medical domain. In this paper, we propose a machine learning framework to predict the possibility of having heart disease using various algorithms. The framework is executed using five algorithms Random Forest, Naïve Bayes, Support Vector Machine, Hoeffding Decision Tree, and Logistic Model Tree (LMT). Cleveland dataset is used for training and testing the model. The dataset is preprocessed followed by feature selection to select most prominent features. The resultant dataset is then used for training the framework. The results are combined and show that Random forest gives maximum accuracy.
Multi-Phase Interleaved Bidirectional DC/DC Converter with Two-Winding Coupled Inductors

The interleaved and magnetically integrated bidirectional DC/DC converters are widely used in more and more applications. Currently in these converters, most of the phase inductors are normally integrated to one single-core coupled inductor. With the number of phases increasing, however, this may suffer several problems, such as asymmetry of magnetic components, difficulties of designing suitable multi-phase magnetic core, large DC flux and so on. To solve these issues, a multi-phase interleaved bidirectional DC/DC converter (IBDC) with two-winding coupled inductors (TCIs) is proposed and analyzed. A four-phase IBDC is taken as an example in this paper, where four TCIs are used to replace the single-core coupled inductor. The steady-state and transient-state performances of the proposed converter are studied in detail. The parameter design criteria of TCIs is also given. Finally, the superiority and practicability of the proposed converter are verified by the prototype experiments.
An Alternative Algorithm for ARIMA Model Selection

Modelling and forecasting of time series data are essential components of data science and big data analytics. Over the years, the Box-Jenkins Methodology has gained tremendous popularity and use for modelling univariate time series. However, the plots of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) often give conflicting and biased pictures, which makes it di cult for proper Autoregressive Integrated Moving Average (ARIMA) model identification or selection. In this paper, we propose an alternative algorithm based on the principles of Cartesian products of sets in mathematics. The practicality of this algorithm was demonstrated with application to univariate ARIMA modelling of Nigerian stock price data. The results show that this algorithm is flexible, modular and widely applicable for quick ARIMA model selection.
Transient Stability Enhancement of Grid Integrated DFIG Based Wind Energy Conversion System

The wind power generation is increasing day by day with the advancement in technology. The most popular type wind turbine in these days is DFIG due to its benefits like variable speed operation and smooth control of reactive & active power. This paper uses Butterworth polynomial method to calculate gains of various PI controllers used in Rotor side converter and Grid side converter in order to improve the dynamic performance of the DFIG and to improve the stability of the system. This paper analyze a grid connected DFIG of 9 MW (consist of 6 units of 1.5 MW DFIGs) simulated in MATLAB. And the system is analyzed for different transient conditions such as three phase fault, voltage sagging and voltage swelling in the power system. This paper also examines the grid connected DFIG under different grid disturbances along with the use of STATCOM.
Stress Testing of Delaying Investment on Power Infrastructure in Context of Mixed Ownership Reform

In recent years, the China government proposes many policies on the reform of mixed ownership, which provides guidance for the mixed ownership reform of power grid enterprises. Reform of mixed ownership is an irresistible trend. Based on this, it is important to study the risks faced by power grid enterprises in the process of mixed ownership reform. The traditional risk assessment methods cannot effectively estimate the possibility of extreme events. Stress testing shows the excellent performance on extremely environmental risk measures such as financial crisis, which plays an indispensable role in modern bank risk assessment. It also becomes an important supplement to traditional tools such as VaR. This paper adopts the method of financial stress testing, and focuses on potential economic and security risks faced by China's power grid enterprises in the process of mixed ownership reform. Taking risk analysis of delaying investment of power infrastructure as an example, we first carry out the qualitative analysis and then conduct the stress testing including the sensitivity analysis. We screen out key risk indicators through sensitivity stress testing. On this basis, we investigate the comprehensive influence of key pressure factors on pressure variables and the specific affects in extreme cases through scenario analysis. The results show that the stress testing method can directly reflect the degree of risk exposure in the future power market. It also can be an effective tool for risk assessment for power grid enterprises during the mixed ownership reform.
Estimation of Performance Parameters Using Charge Freed from Deep Traps Located at Interfacial Region of Oil-Paper Insulation

De-trapped charge dislodged from interfacial region of oil-paper insulation can be used as an effective insulation sensitive parameter. Over time, various physiochemical reactions takes place at interfacial region and consequently results in the formation of different trap sites (deep and shallow). Charges which resides at these traps sites are de- trapped after gaining sufficient energy. In present analysis, it is found that charge dislocated from deep traps maintains some specific type of relationship with different insulation sensitive parameters. Before using de-trapped (dislodge from deep traps) as an effective insulation sensitive parameter the effect of geometry must be reduced, as amount of de-trapped charge depends on the area of interfacial region which is not identical for all units. Result presented in this paper shows that use of geometric capacitance for normalization purposes significantly reduces the effects of insulation physical dimensions on de- trapped charge. The capability of deep charge (normalized using dc insulation resistance and geometrical capacitance) is also compared in the present work.
Estimation of Drone Micro-Doppler Signatures via Track-Before-Detect in Array Radars

In this work, we consider the estimation of micro-Doppler signatures of small rotary-wing aircraft such as drones using a pulsed radar with a uniform planar array (UPA) receiver in order to identify/classify them within the processing performed for their detection. Such objects induce low signal to noise ratio (SNR) reflections which complicate their detection and Doppler processing. We propose a novel track-before-detect algorithm which simultaneously estimates complex reflection coefficients, micro-Doppler features in the form of additional frequency shifts generated by rotating blades and the motion trajectory before the decision on object existence is made. We demonstrate the efficacy of our estimation scheme in a simulation example and compare with conventional techniques.
Decentralized Multi-Subgroup Formation Control With Connectivity Preservation and Collision Avoidance

This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control.