Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Algorithms of Neural Networks for Traditional Time-Domain Equalizer in Optical Communications

Neural networks (NNs) have been successfully applied to channel equalization for optical communications. In optical fiber communications, the linear equalizer and the nonlinear equalizer with traditional structures might be more appropriate than NNs for performing real-time digital signal processing, owing to its much lower computational complexity. However, the optimization algorithms of NNs are useful in many optimization problems. In this paper, we propose and evaluate the tap estimation schemes for the equalizer with traditional structures in optical fiber communications using the optimization algorithms commonly used in the NNs. The experimental results show that adaptive moment estimation algorithm and batch gradient descent method perform well in the tap estimation of equalizer. In conclusion, the optimization algorithms of NNs are useful in the tap estimation of equalizer with traditional structures in optical communications.
A Current-Based Approach for Short-Circuit Fault Diagnosis in Closed-Loop Current Source Inverter

In this article, a method based on output currents is proposed to diagnose short-circuit faults of single or double switches in current source converter. The method only needs the feedback currents and their corresponding reference signals that can be obtained from the main control system. This diagnosis algorithm needs to calculate the average values of the feedback currents and the average absolute values of the current reference signals in one power frequency cycle. Diagnosis variables can be obtained from the ratio of the two values. Then, three detection thresholds are set to judge and locate the short-circuit power switches. The method can be implemented in the main control program directly, without additional voltage sensors and other auxiliary detection devices. Meanwhile, the method is simple and easy to implement in practice. The effectiveness of proposed algorithm is validated with experiments.
High-Efficiency Three-Phase Single-Stage Isolated Flyback-Based PFC Converter With a Novel Clamping Circuit

This article analyzes a three-phase single-stage isolated flyback-based power factor correction converter and presents a novel clamping circuit to capture the transformers leakage inductance energy. The converter unity power factor operation is obtained by using the inherent resistance property of discontinuous conduction mode. Thus, the input current shaping circuit is eliminated and resulted in a single-loop control system. Therefore, the converter control circuit requires only one sensor, which decreases the system cost, and also increases the system robustness to high-frequency noise. With the proposed clamping circuit, the transformers leakage inductance energy is successfully captured in clamping capacitor, and thereby, it is fed to the dc load by using an auxiliary two-switch forward converter which enhanced the overall operating efficiency of a converter. The converter detailed steady-state operation and the design considerations are presented. Each power component voltage stress expressions are derived to simplify the converter design, and the converter small-signal model is presented to help the controller design. The novelty and performance of the converter is verified by both simulation and experimentation.
Analysis of Factors Effecting on Optimal Configuration of Two Receivers for a Three-Coil Wireless Power Transfer System

In this paper, we investigate the optimal configuration of receivers for a three-coil wireless power transfer system in which a transmitter is located between receivers. Based on an equivalent circuit model, we first find the compensatory reactance terms to eliminate cross coupling between receivers. Neglecting cross coupling, we derive an optimal coupling coefficient between a receiver and transmitter for a given arrangement of a second receiver, as a means of maximizing the transmission efficiency (TE). Using this theoretical analysis, it is revealed that the TE can be improved by locating the first receiver far from the transmitter when the first receiver has higher quality factor or the distance between the transmitter and the second receiver is lower. Results for various cases show that our analytical results are in a good agreement with experimental ones.
Driver Inattention Detection in the Context of Next-Generation Autonomous Vehicles Design: A Survey

Driver inattention is among major contributing factors to traffic accidents. There have been and continue to be efforts by governing bodies, car manufacturers, and researchers to prevent driver inattention or, failing that, to mitigate its effects. Many vehicles nowadays come equipped with driver monitoring systems that can alert the driver to, or compensate for, inattention. Moreover, the research community continues to explore and investigate more robust approaches to deal with inattention. Meanwhile, vehicle automation, to various degrees, is becoming more prevalent, with the human’s role in the driving task changing depending on the level of autonomy. This necessitates that inattention detection, moving forward, be studied and designed in view of automation and in the context of a specific level of vehicle autonomy. Driver inattention and vehicle automation interact in a complex way, and that needs to be taken into account in the design of future vehicles. We explore this interaction in this paper in light of research findings, and survey inattention detection systems and attempt to contextualize them within popular frameworks for next-generation autonomous vehicles.
Real Time Adaptive Tracking System Using Computer Vision

This project studies long-time object tracking in a sequence of frames. In this project, a detector is trained with specimens found on the path of a tracker that itself does not rely on the object detector. We attain high robustness and outdo current adaptive tracking-by-detection (11) approaches by decoupling object tracking and object detection. A substantial reduction of calculating time is attained by means of simple features for object detection and by using a cascaded method. The object location is marked in each frame. The task is to find the position of object in that frame else it must notify that the object is absent in the consecutive frames. We have developed a Real Time Tracking framework. The task of long-time tracking is divided as follows: Tracking, Learning and Detection. The tracker must follow the marked object of interest in consecutive frames. The detector restricts all observed appearances and amends the tracker when required. To evade these blunders there forth, the learning approximates the detector’s blunders and re-evaluates it. This project studies methods to recognize the detector’s faults and learn from, by developing a learning method with the help of “experts” which will estimate these blunders. We call it the P-N learning. With the help of RAT and P-N learning, our real-time processing can be described as an extremely integrated arrangement providing very precise object detection with RGB-D sensor.
Output voltage tracking controller embedding auto‐tuning algorithm for DC/DC boost converters

This study suggests a non-linear output voltage tracking controller for DC/DC boost converters in the form of a classical cascade control structure. Non-linearities in the converter dynamics as well as parameter uncertainties and load variations are considered. The first contribution of this study is the design of an auto-tuner, which automatically adjusts the control gain according to the output voltage error to enhance transient performance. The second contribution involves proving that the closed-loop system ensures the performance recovery without any steady-state errors in the presence of parameter and load variations. The effectiveness of the proposed algorithm was verified through experimental investigations using a 5 kW prototype DC/DC boost converter.
Calibration of Stochastic Radio Propagation Models Using Machine Learning

This letter proposes a machine learning-based method for the calibration of stochastic radio propagation models. Model calibration is cast as a regression problem involving mapping of the channel transfer function or impulse response to the model parameters. A multilayer perceptron is trained with summary statistics computed from synthetically generated channel realizations using the model. To calibrate the model, the trained network is used to estimate the model parameters from channel statistics obtained from measurements. The performance of the proposed method is evaluated with propagation graph and Saleh–Valenzuela models using both simulated data and in-room channel measurements. Results show accurate estimation of the parameters of both models.
Integrating synthetic minority oversampling and gradient boosting decision tree for bogie fault diagnosis in rail vehicles

Bogies are critical components of a rail vehicle, which are important for the safe operation of rail transit. In this study, the authors analyzed the real vibration data of the bogies of a railway vehicle obtained from a Chinese subway company under four different operating conditions. The authors selected 15 feature indexes – that ranged from time-domain, energy, and entropy – as well as their correlations. The adaptive synthetic sampling approach–gradient boosting decision tree (ADASYN–GBDT) method is proposed for the bogie fault diagnosis. A comparison between ADASYN–GBDT and the three commonly used classifiers (K-nearest neighbor, support vector machine, and Gaussian naïve Bayes), combined with random forest as the feature selection, was done under different test data sizes. A confusion matrix was used to evaluate those classifiers. In K-nearest neighbor, support vector machine, and Gaussian naïve Bayes, the optimal features should be selected first, while the proposed method of this study does not need to select the optimal features. K-nearest neighbor, support vector machine, and Gaussian naïve Bayes produced inaccurate results in multi-class identification. It can be seen that the lowest false detection rates of the proposed ADASYN–GBDT model are 92.95% and 87.81% when proportion of the test dataset is 0.4 and 0.9, respectively. In addition, the ADASYN–GBDT model has the ability to correctly identify a fault, which makes it more practical and suitable for use in railway operations. The entire process (training and testing) was finished in 2.4231 s and the detection procedure took 0.0027 s on average. The results show that the proposed ADASYN–GBDT method satisfied the requirements of real-time performance and accuracy for online fault detection. It might therefore aid in the fault detection of bogies.
Unimodular Waveform Design With Desired Ambiguity Function for Cognitive Radar

In this correspondence, we target the problem of radar waveform design based on the ambiguity function. The problem is formulated as an optimization, where the nonconvex unimodularity constraint is also considered. The problem is solved by successive application of majorization minimization (MM) and projected gradient descent algorithm (PGD). The proposed method has the unprecedented ability to synthesize nonzero subregions. The superiority of the proposed algorithm in achieving zero subregions is confirmed through simulation, where a suppression superiority of at least 6 dB is evident compared to the best state-of-the-art benchmark.
Automated Component‐Selection of Design Synthesis for Physical Architecture with Model‐Based Systems Engineering using Evolutionary Trade‐off

Component‐Selection is an important task in design synthesis of MBSE. A trade study is commonly used to help systems engineers and stakeholders selecting the components of a systems design. A simple analysis may be sufficient when it involves only two parameters. However, when the components and their integration become more complex, the trade study also becomes harder, time‐ and cost‐consuming, and error‐prone. This paper aims to propose a method to automatically generate the solution by performing an evolutionary search. Sample components of a hybrid car which consists of an engine, an electric motor, and a battery are used in our initial prototype. The logical architecture is represented in the OMG SysMLTM via CSMTM. Through the experimental result, this paper shows that the proposed technique allowed the system design to be efficiently selected.
A Mixed-Integer Second-Order Cone Programming Algorithm for the Optimal Power Distribution of AC-DC Parallel Transmission Channels

For the controllability of the transmission power of DC transmission channels, the optimal power distribution (OPD) of AC-DC parallel transmission channels is an effective measure for improving the economic operation of an AC-DC interconnected power grid. A dynamic optimal power flow model for day-ahead OPD of AC-DC parallel transmission channels is established in this paper. The power flow equation constraints of an AC-DC interconnected power grid and the constraints of the discrete regulation requirement of the transmission power of DC channels are considered, which make the OPD model of the AC-DC parallel transmission channels a mixed-integer nonlinear non-convex programming (MINNP) model. Through a cone relaxation transformation and a big M method equivalent transformation, the non-convex terms in the objective function and constraints are executed with the convex relaxation, and the MINNP model is transformed into a mixed-integer second-order cone programming model that can be solved reliably and efficiently using the mature optimization solver GUROBI. Taking an actual large-scale AC-DC interconnected power grid as an example, the results show that the OPD scheme of the AC-DC parallel transmission channels obtained by the proposed algorithm can effectively improve the economical operation of an AC-DC interconnected power grid.
Time-Domain Bloch Boundary Conditions for Efficient Simulation of Thermoacoustic Limit Cycles in (Can-)Annular Combustors

Thermo-acoustic eigenmodes of annular or can-annular combustion chambers, which typically feature a discrete rotational symmetry, may be computed in an efficient manner by utilizing the Bloch-wave theory. Unfortunately, the application of the Bloch-wave theory to combustion dynamics has hitherto been limited to the frequency domain. In this study, we present a time-domain formulation of Bloch boundary conditions (BBC), which allows to employ them in time domain simulations, e.g., computational fluid dynamics (CFD) simulations. The BBCs are expressed as acoustic scattering matrices and translated to complex-valued state-space systems. In a hybrid approach an unsteady, compressible CFD simulation of the burner-flame zone is coupled via characteristic-based state-space boundary conditions to a reduced order model of the combustor acoustics that includes BBCs. The acoustic model with BBC accounts for cross-can acoustic coupling and the discrete rotational symmetry of the configuration, while the CFD simulation accounts for the nonlinear flow–flame acoustic interactions. This approach makes it possible to model limit cycle oscillations of (can-)annular combustors at drastically reduced computational cost compared to CFD simulations of the full configuration and without the limitations of weakly nonlinear approaches that utilize a flame describing function. In this study, the suggested approach is applied to a generic multican combustor. Results agree well with a fully compressible CFD simulation of the complete configuration.
Virtual Dressing Room Implementation Using Body Image –Clothe Mapping

with recent advance in web technology, many online shopping websites have been emerged. Despite its advantages, however, online shopping presents certain drawbacks. One drawback is that it may be difficult for a person to visualize how a given article would look if worn by that person-owing to the rich variation in body size and shape, hair and skin color, etc., in the human population. Testing the fitness of clothes is highly important for both customer and trader. Our approach concentrate on how the selected garment fitted the user’s body and how it will be appear as if he/she in real world. This was carried out by identifying critical points on garment and user’s body dimensions using image processing techniques. In this paper, an application for mitigating the virtual dressing room was designed, implemented and tested.
Development History Of The World Wide Web

: There are many technologies which are used on the Internet to share files, each of them have different features, methods and protocols. However, the most common and easiest one is the Web which was established by few simple features. The Web continuously developing to be as much as easy for the users. The Web developers want to make a machine which thinks like humans by adding new tools, methods and protocols to the current Web. This paper focuses on the most widely used technologies in the Web, and presents the stages of the development of the World Wide Web. Moreover, the evolution of the Web from Web1.0 to Web3.0 and semantic web is revealed. The paper, in addition, explains the technologies and tools of the Web and compares between Web portals and search engines. Finally, assessment of the activation period for each one is presented.
Analysis and Design of Current-Fed Three-Phase-Isolated LCC-T Resonant Converter for Low-Voltage High-Current Applications

A three-phase-isolated current-fed dc–dc converter with LCC-T resonant tank to achieve an efficient and compact design is proposed. Traditional dc–dc converters require an external parallel inductor to increase the transformer magnetizing current to realize soft switching under light-load conditions. The proposed LCC-T configuration eliminates this requirement and realizes light-load soft switching with reduced circulating current. In addition, the proposed topology maintains zero-voltage switching (ZVS) turn-ON of primary switches, ZVS turn-ON and zero-voltage-zero-current switching turn-OFF of secondary diodes for all load conditions. Steady-state operation, analysis, and design of the proposed converter are reported. Experimental results of 1-kW hardware prototype to verify the claims of the proposed analysis and design are presented.
Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.
Predicting China's Economic Running State Using Machine Learning

China's business index of macro-economic includes early warning index, coincidence index, leading index and lagging index, among which early warning index reflects the economic running state. However, obtaining these indexes is a complex and daunting task. To simplify the task, this article mainly explores how to use machine learning algorithms including multiple linear regression(MLR), support vector machine regression(SVM), random forest(RF), artificial neural network(ANN) and extreme learning machine(ELM) to accurately predict early warning index. Finally, it can be found that the warning index can be well predicted by above machine learning algorithms with coincidence index, leading index and lagging index to be variables, furthermore, extreme learning machine and random forest are superior to other methods.
COMPASS Production System Overview

Migration of COMPASS data processing to Grid environment has started in 2015 from a small prototype, deployed on a single virtual machine. Since summer of 2017, the system works in production mode, distributing jobs to two traditional Grid sites: CERN and JINR. Now the infrastructure of COMPASS Grid Production System includes 6 virtual machines, each is reserved for one production service: database, PanDA, Auto Pilot Factory, Monitoring, CRIC information system and, finally, production system (ProdSys) management instance, which provides a user interface for production manager and hosts services of automatic processing. Support of COMPASS virtual organization is provided by CERN IT. CRIC is also deployed at CERN Cloud Service. Other ProdSys services are deployed at JINR Cloud Service. There are two storage elements at CERN: EOS for short-term storage and Castor for long-term storage. During last year, along with providing a 24/7 service, the system was instrumented by many features, which allow automating data processing as much as possible. Recently, Blue Waters HPC has become a member of the computing infrastructure of the experiment. Details of implementation, workflow management, and infrastructure overview are presented in this article.
First-Order Seismic Loss Assessment at Urban Scale: A Case Study of Skopje, North Macedonia

ABSTRACT Relatively simple method for seismic loss assessment is presented based on probabilistic hazard, event scenarios and local building inventory. EMS98-dependent vulnerability model is transformed to continuous fragility functions correlating damage probability to peak ground acceleration. The proposed method illustrated over 59,950 inventoried buildings and evaluated with replacement cost of $25.5B. For a repeat of the M6.1 1963 earthquake, simulations predict immediate losses of about $6.8B and 1,908 red-tagged buildings, compared to about $5B and 15,800 red-tagged building at the time. Average losses from probabilistic scenarios range from $1.6B (return period of 100y) to $17.8B (10,000y).
Control of an articulated wheeled mobile robot in pipes

We propose a control method in which an articulated wheeled mobile robot moves inside straight, curved and branched pipes. This control method allows the articulated wheeled mobile robot to inspect a larger area. The articulated wheeled mobile robot comprises pitch and yaw joints is and propelled by active wheels attached to the robot. Via the proposed control method, the robot takes on two different shapes; one prevents the robot from slipping inside straight pipes and the other allows movement in a pipe that curves in any direction. The robot is controlled by a simplified model for the robot's joint angles. The joint angles of the robot are obtained by fitting to a continuous curve along the pipe path. In addition, the angular velocity of the robot's active wheels is determined by a simplified model. The effectiveness of the proposed the control method was demonstrated with a physical implementation of the robot, and the robot was able to move inside straight, curved and branched pipes. GRAPHICAL ABSTRACT