Speed Scenarios Research Articles

Design and Development of Simulation Computer Software for Safety Siding in Regional Expressed Railway

The safety siding is an isolated line used to ensure safety on traffic operating. Too short a safety siding does not guarantee the safe operation of the train, while too long a safety siding will increase the cost and design difficulty of construction. The prior art specification says the length of the safety siding required is not less than 50m. Obviously, there is still uncertainty about the setting length of safety siding on specific line. This paper combines the characteristics of the suburban railway to design and develop a setting simulation system of safety siding. The safety siding calculation model is established by the traction calculation method, which includes different factors such as braking ratio, train speed, line parameters, signal system and application scenarios. Moreover, it designs the structure and function of the simulation system and discusses the key techniques of simulation. Finally, taking the Fuzhou Railway Station as an example to verify the simulation system. The results show that when the different lines and train parameters are configured, the design length of the result is also different. The simulation system could accomplish the design of the safety siding in different scenes. The calculation results are reliable and have engineering application value.

Comparing the performance of OFDM and FBMC multicarrier systems in doubly-dispersive wireless channels

In mobile communications, the most widely used multicarrier technique, namely OFDM, is known to exhibit performance limitations in high speed scenarios. FBMC is an alternative approach and, among FBMC techniques, the recently introduced FBMC-PAM scheme has emerged as the most robust to CFO. Therefore, it is chosen here as the challenger of OFDM for performance comparison in doubly dispersive channels. After presentation of the system model, multicarrier scheme and wireless channel, an analytical evaluation of the BER in the particular case of single path time-varying channel is provided for FBMC-PAM and validated through simulations. Then, simulation results are reported for both OFDM and FBMC-PAM, using the 3GPP multipath channel models. It appears that FBMC-PAM can outperform OFDM in highly time-varying frequency-selective channels.

Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

AbstractStructural compactness with multiple meshing points excites multiple vibrations in a planetary gearbox of a wind turbine operating at varying speeds. The nonstationary, multicomponent vibration signals result in complex modulations challenging the effectiveness of a signal processing technique‐based fault diagnosis method. This paper aims to detect gear tooth faults under varying speed conditions based on the instantaneous frequency (IF) estimate of the decomposed component. Vibration signals were decomposed using variational mode decomposition (VMD), which is capable of frequency demodulation by exhibiting mono‐oscillatory components, followed by the IF estimation to select the most sensitive decomposed mode. The selected sensitive mode was further analyzed to deduce envelope spectrums for the extraction of frequency components highlighting fault symptoms. Furthermore, permutation entropy (PE) was evaluated to characterize the presence of randomness due to fault severity and to validate the proposed signal processing approach. In this paper, the vibration signals simulating planet gear faults were analyzed using the proposed signal processing approach. Symptoms of planet tooth faults were analyzed using the experimental investigation under real‐time varying speed to validate and exhibit the effectiveness of the proposed fault diagnosis approach for a planetary gearbox.

Modeling Human Steering Behavior in Teleoperation of Unmanned Ground Vehicles With Varying Speed

This paper extends a prior human operator model to capture human steering performance in the teleoperation of unmanned ground vehicles (UGVs) in path-following scenarios with varying speed. A prior study presented a human operator model to predict human steering performance in the teleoperation of a passenger-sized UGV at constant speeds. To enable applications to varying speed scenarios, the model needs to be extended to incorporate speed control and be able to predict human performance under the effect of accelerations/decelerations and various time delays induced by the teleoperation setting. A strategy is also needed to parameterize the model without human subject data for a truly predictive capability. This paper adopts the ACT-R cognitive architecture and two-point steering model used in the previous work, and extends the model by incorporating a far-point speed control model to allow for varying speed. A parameterization strategy is proposed to find a robust set of parameters for each time delay to maximize steering performance. Human subject experiments are conducted to validate the model. Results show that the parameterized model can predict both the trend of average lane keeping error and its lowest value for human subjects under different time delays. The proposed model successfully extends the prior computational model to predict human steering behavior in a teleoperated UGV with varying speed. This computational model can be used to substitute for human operators in the process of development and testing of teleoperated UGV technologies and allows fully simulation-based development and studies.

Second-Order Fast Terminal Sliding Mode Control for MPPT of PMSG-based Wind Energy Conversion System

This article proposes a new maximum power point tracking (MPPT) controller based on voltage-mode Second-Order Fast Terminal Sliding Mode Control (SO-FTSMC) to obtain better performance in the power extraction from Permanent Magnet Synchronous Generator (PMSG)-based Wind Energy Conversion System (WECS). The major objective of the study is the design of SO-FTSMC that can ensure the operation of the system in maximum power reference with higher efficiency, less steady-state error, and voltage fluctuation. Maximum power references are determined by mechanical sensorless MPPT approach. The small-scale WECS configuration incorporates wind turbine, PMSG, uncontrolled rectifier, boost converter, and MPPT controller. SO-FTSMC method has not been performed before as a voltage regulator in such a topology. The proposed controller has been validated for two different wind speed test scenarios in simulation environment, which can be applied effectively to PMSG-based WECS. Moreover, Conventional SMC (C-SMC) is created and then performance of controllers is compared in these wind speed scenarios according to maximum extracted average power, output voltage fluctuation, and voltage error in steady-state, MPPT efficiency, and performance indices. Obtained results indicate that proposed SO-FTSMC-based MPPT controller achieves superior performance.

Self-configuration and self-optimization process with taguchi method in hybrid optical wireless network

<p>In this paper, an alternative improvement is proposed which is the adaptive wireless access networks-based optical backhaul convergence that will greatly promote to use the existing resource of MANET (mobile ad hoc network). However, these characteristics itself acts as a drawback to the MANET applications such as the random distribution of nodes and continuously changing topology. MiNiTab statistical software was used to model the effect of the parameter variation to predict the field quality through the design of experiments while OMNeT++ network simulation was created to visualize the effect of QoS performance study in response with varying speed scenario. The result shows that the proposed ESCMDR scheme can obtain robustness and outperformed compared to the non-Taguchi previous study when it is used in random waypoint mobility model in any speed of sources. The work is based on Packet Delivery Ratio (PDR) and Packet Loss Probability (PLP) metric under the varying speed scenario. It results in better QoS network PDR of 28.9% improvement, with 83.56% improvement on average PLP. The paper shows that the MANET QoS performance constrained can be addressed with the self-configured data rate of integrated optimization with Taguchi method on AODV-UU (Adhoc On Demand Distance Vector-Uppsala University) routing technique.</p>

Sparse Elitist Group Lasso Denoising in Frequency Domain for Bearing Fault Diagnosis

The fault-induced impulse responses of localized bearing fault are usually interfered by the background noise and other harmonic components. They are strongly coupled together and are hard to be separated. It is crucial to develop a fast and reliable method to extract the impulse-based feature for online bearing fault diagnosis in the industry application. In this article, we propose a new sparse elitist group lasso denoising (SEGLD) algorithm in frequency domain to detect the incipient impulse-based fault feature, which is free of utilizing the prior knowledge. We first reveal the sparse characteristics of the bearing fault signals in frequency domain. Then, a tailored denoising model is proposed. To obtain a satisfactory analytical stationary solution, the Douglas-Rachford splitting solver is employed for the denoising model. Moreover, we explore the relationship between the best regularization parameters, the periodic information and the normalization estimated noise of the rolling bearing fault signal. A rule of adaptively selecting the best regularization parameters is demonstrated. Finally, the robustness and effectiveness of the proposed SEGLD algorithm are profoundly verified by the numerical simulation and two evaluation experiments under the conditions of early fault stage and low speed scenario. Also, it is demonstrated that the proposed approach outperforms the state-of-the-art method for extracting the weak fault feature.

The electromagnetic modeling and the co-simulation of a direct drive axial flux permanent magnet synchronous generator

Direct drive gearless axial flux permanent magnet synchronous generators (AFPMSG) are designed as multi-pole for use in vertical axis wind turbines. In particular, there are multi-pole core/coreless stator structures with axial flux for use in vertical axis wind turbines (WT) that can be designed in a compact structure at low wind speeds. In this study, the parametric simulation studies have been carried out according to rotor mechanical speeds with certain linear steps depending on different wind speed scenarios for an AFPMSG designed with 16-pole and cored stator for 5 kVA rated power with the finite element analysis (FEA) software. According to the analysis results obtained, the performance of the generator is reported and current, voltage, power losses and flux distribution are investigated. In addition, the DC bus voltage at the output of the DC-DC boost converter circuit due to wind speed changes is adaptively controlled for AFPMSG, which is co-simulated with the power electronics interface used at the generator output. Thus, both power electronics circuit performance and generator side have been simulated simultaneously with electromagnetic modeling. Therefore, the performance of the designed AFPMSG, which is modeled in three dimensions (3D) before the prototype stage, can be determined under more realistic conditions.

Analysis of mobility speed impact on end-to-end communication performance in VANETs

Vehicular ad hoc networks (VANETs) have the potential to enable communication among vehicles either directly using a vehicle-to-vehicle (V2V) communication model or through a road side unit (RSU) while using a vehicle-to-infrastructure (V2I) communication model. In multi-hop messaging, end-to-end (E2E) communication delay and messages reachability are essential performance indicators for VANET applications, which in-fact are critical criteria for safety related messaging services. Several investigations have been carried on the impact of different cars mobility patterns on communication performance in VANETs and over different road networks. However, and to the best of our knowledge, a study aiming towards the analysis of mobility speed differences and their impact on E2E delays and messages reachability has not been thoroughly investigated for sender-oriented messaging schemes in VANETs. This paper evaluates the impact of speed differences among vehicles over the performance of two widely adopted classes of messaging schemes in VANETs, namely the furthest distance and link quality based schemes. The considered schemes are devised using a sender-oriented relay selection mechanism due to its efficient utilization of channel bandwidth. Simulation experiments are performed over two mobility speed scenarios with the first depicting a small speed difference among vehicles while the other depicting a large difference in speed. Analysis of the obtained results reveals the following major findings. In scenarios with small speed differences, the furthest distance scheme exhibits lower E2E delays when compared to link quality based scheme with a difference of up to 29.5 msec. Conversely, large differences in mobility speed have noticeable adverse effects on furthest distance scheme resulting into it having higher E2E delays as compared to link quality scheme, with a difference of up to 127.43 msec. In-terms of message reachability, the link quality scheme prominently outperforms the further distance scheme in both speed difference scenarios. Consequently, the link quality scheme shows robustness towards the adverse effects resulting from large differences in mobility speed while further distance scheme struggles to do so.

Driver Profiling Using Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) Methods

Driver profiling has a major impact on traffic safety, fuel consumption and gas emission. LSTM and CNN based neural network models were developed to classify and assess bus driver behavior characterized by deceleration, engine speed pedaling, corner turn and lane change attempts. Deceleration, engine speed and corner turn test scenarios were performed on concrete paved test track while lane changing tests were conducted on a commercial asphalt highway. Despite the majority of studies relying on image, vehicle data and additional sensor fusion, here only the data streams received from vehicle CAN Bus system were used to train the proposed network architectures. After parsing the data into meaningful characteristic parameters, different LSTM and CNN architectures were trained by varying the number of layers, neurons and epoch number. Both LSTM and 1D-CNN networks resulted in comparable success rates. CNN architecture indicates better performance indices for identification of aggressive driving compared to LSTM network for behavioral modelling.

Multiple CFOs Estimation and Implementation of SC-FDMA Uplink System Using Oversampling and Iterative Method

Multiple carrier frequency offsets (CFOs) estimation in high speed single carrier frequency division multiple access (SC-FDMA) uplink system is a challenging issue. Most of the existing blind CFO estimation methods work for a certain type of carrier mapping scheme (CMS) or address a single CFO estimation by utilizing long data over frequency selective fading channel. These estimation methods become irrelevant at high speed and multi-user scenarios where CFO changes with symbols and users. In this paper, we propose a blind multiple CFOs estimation method for all CMS of SC-FDMA and implement over testbed. The proposed method requires only one SC-FDMA symbol to attain reliable estimation even in a high mobility environment, i.e., in a doubly selective channel. It exploits the phase difference present between even and odd samples of the received oversampled signal to estimate CFOs. Subsequently, an iterative joint CFO estimation and compensation (IJCEC) method is introduced to remove residual interference and increase CFOs estimation accuracy. Cramer-Rao lower bound is derived to characterize the CFO estimation accuracy of the proposed method. The work is compared with the conventional methods such as MUSIC, ESPRIT, CAZAC, and some existing methods and validated through Monte Carlo simulations. Finally, we authenticate the proposed IJCEC method by performing implementation and measurement on National Instrument testbed setup in an indoor propagation environment.

A novel unified maximum power extraction framework for PMSG based WECS using chaotic particle swarm optimization derivatives

The most important issue in the use of wind energy conversion systems (WECS) is to ensure maximum power extraction for attaining increased efficiency. In this study, maximum power extraction frameworks operating the state-of-the-art optimization methods are presented for permanent magnet synchronous generator (PMSG) based WECS. These frameworks consist of a fast terminal sliding mode control (FTSMC) based MPPT controller and a hybrid MPPT approach that combines chaotic based particle swarm optimization (PSO) derivatives and optimal relation based (ORB) method. Chaotic dynamic weight PSO (CDW-PSO) and Gauss map based chaotic PSO (GM-CPSO), which are remarkable and recent optimization techniques, are utilized to achieve optimum coefficients to ensure efficient MPPT operation. After acquiring the optimum coefficients, the framework passes to ORB operation part. Moreover, the proposed frameworks track extracted power within certain limits during the operation of WECS and make transition between the best coefficients if necessary. On the other hand, FTSMC is used to track the MPPT references that are determined via hybrid MPPT algorithms. To validate the proposed frameworks, they are tested in Matlab/Simulink environment for three specific wind speed scenarios. GM-CPSO based ORB and CDW-PSO based ORB MPPT methods are compared with ORB and tip speed ratio (TSR) methods under these scenarios. Consequently, it is revealed that proposed methods contribute to MPPT efficiency by providing higher power extraction than other conventional methods.

Routing Electric Vehicles on Congested Street Networks

Freight distribution with electric vehicles (EVs) is a promising alternative to reduce the carbon footprint associated with city logistics. Algorithms for planning routes for EVs should take into account their relatively short driving range and the effects of traffic congestion on the battery consumption. This paper proposes new methodology and illustrates how it can be applied to solve an electric vehicle routing problem with stochastic and time-dependent travel times where battery recharging along routes is not allowed. First, a new method for generating network-consistent (correlated in time and space) and time-dependent speed scenarios is introduced. Second, a new technique for applying branch and price on instances defined on real street networks is developed. Computational experiments demonstrate the effectiveness of the approach for finding optimal or near-optimal solutions in instances with up to 133 customers and almost 1,500 road links. With a high probability, the routes in the obtained solutions can be performed by EVs without requiring intermediate recharging stops. An execution time control policy to further reduce the chances of stranded EVs is also presented. In addition, we measure the cost of independence, which is the impact on solution feasibility when travel times are assumed statistically independent. Last, we give directions on how to extend the proposed framework to handle recourse actions.

Relativistic effects in the large-scale structure with effective dark energy fluids

We study the imprints of an effective dark energy fluid in the large scale structure of the universe through the observed angular power spectrum of galaxies in the relativistic regime. We adopt the phenomenological approach that introduces two parameters {Q,η} at the level of linear perturbations and allow to take into account the modified clustering (or effective gravitational constant) and anisotropic stress appearing in models beyond ΛCDM . We characterize the effective dark energy fluid by an equation of state parameter w=−0.95 and various sound speed cases in the range 10−6⩽ cs2⩽ 1, thus covering K-essence and quintessence cosmologies. We calculate the angular power spectra of standard and relativistic effects for these scenarios under the {Q,η} parametrization, and we compare these relative to a fiducial ΛCDM cosmology. We find that, overall, deviations relative to ΛCDM are stronger at low redshift since the behavior of the dark energy fluid can mimic the cosmological constant during matter domination era but departs during dark energy domination. In particular, at z=0.1 the matter density fluctuations are suppressed by up to ∼3% for the quintessence-like case, while redshift-space distortions and Doppler effect can be enhanced by ∼15% at large scales for the lowest sound speed scenario. On the other hand, at z=2 we find deviations of up to ∼5% in gravitational lensing, whereas the Integrated Sachs-Wolfe effect can deviate up to ∼17%. Furthermore, when considering an imperfect dark energy fluid scenario, we find that all effects are insensitive to the presence of anisotropic stress at low redshift, and only the Integrated Sachs-Wolfe effect can detect this feature at z=2 and very large scales.

RETRACTED: Energy-aware resource management for uplink non-orthogonal multiple access: Multi-agent deep reinforcement learning

Abstract Non-orthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and the high data rate requirements of the next generation networks. In this paper, we investigate the joint subchannel assignment and power allocation problem in an uplink multi-user NOMA system to maximize the energy efficiency (EE) while ensuring the quality-of-service (QoS) of all users. Different from conventional model-based resource allocation methods, we propose two deep reinforcement learning (DRL) based frameworks to solve this non-convex and dynamic optimization problem, referred to as discrete DRL based resource allocation (DDRA) framework and continuous DRL based resource allocation (CDRA) framework. Specifically, for the DDRA framework, we use a deep Q network (DQN) to output the optimum subchannel assignment policy, and design a distributed and discretized multi-DQN based network to allocate the corresponding transmit power of all users. For the CDRA framework, we design a joint DQN and deep deterministic policy gradient (DDPG) based network to generate the optimal subchannel assignment and power allocation policy. The entire resource allocation policies of these two frameworks are adjusted by updating the weights of their neural networks according to feedback of the system. Numerical results show that the proposed DRL-based resource allocation frameworks can significantly improve the EE of the whole NOMA system compared with other approaches. The proposed DRL based frameworks can provide good performance in various moving speed scenarios through adjusting learning parameters.

Adaptive Sliding Mode Control of Rack Position Tracking System for Steer-by-Wire Vehicles

This paper describes adaptive sliding mode control of a steer-by-wire (SBW) system to guarantee rack position tracking performance in various driving situations. The proposed control algorithm was developed using only motor position sensors (MPS) without information on tire/road friction. A stiffness parameter adaptation law was designed to compensate for disturbances in the SBW rack system. It is demonstrated that the proposed adaptation algorithm provides good tracking performance without using an additional gain tuning approach under various road conditions. Moreover, in the proposed algorithm, a dynamic stiffness model has been developed to improve rack position tracking performance under a zero vehicle speed scenario. In a dynamic stiffness model, the stiffness center is not fixed but changes depending on the actual rack position. In the event than an SBW vehicle is parking, it is important to ensure rack position tracking performance at low and zero vehicle speeds. Computer simulations and vehicle tests were performed under various driving situations to test the performance of the proposed control algorithm. The results demonstrate that the proposed control algorithm ensures tracking performance on dry asphalt and wet road conditions, as well as at zero vehicle speed.

Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function

Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE's experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE's Signal-to-Interference-plus-Noise-Ratio (SINR), cells' load and UE's speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms.

Stochastic economic dispatch considering the dependence of multiple wind farms using multivariate Gaussian kernel copula

Wind farms usually gather in areas with abundant wind resources. Therefore, spatial dependence of wind speeds among nearby wind farms should be taken into account when modeling a power system with large-scale wind power penetration. This paper proposes a novel non-parametric copula method, multivariate Gaussian kernel copula (MGKC), to describe the dependence structure of wind speeds among multiple wind farms. Wind speed scenarios considering the dependence among different wind farms are sampled from the MGKC by quasi-Monte Carlo (QMC) method, so as to solve the stochastic economic dispatch (SED) problem, for which an improved mean-variance (MV) model is established, which targets at minimizing the expectation and risk of fuel cost simultaneously. In this model, confidence interval is applied in the wind speed to obtain more practical dispatch solutions by excluding extreme scenarios, for which the quantile-copula is proposed to construct the confidence interval constraint. Simulation studies are carried out on a modified IEEE 30-bus power system with wind farms integrated in two areas, and the results prove the superiority of the MGKC in formulating the dependence among different wind farms and the superiority of the improved MV model based on quantile-copula in determining a better dispatch solution.

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

Ensuring a seamless connection during the mobility of various User Equipments (UEs) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operations, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs' mobility as well as future research directions on mobility management in 5G networks and beyond.

Primary Frequency Response of Microgrid Using Doubly Fed Induction Generator With Finite Control Set Model Predictive Control Plus Droop Control and Storage System

This paper presents a new methodology for primary frequency response (PFR) in a microgrid through the finite control set-model predictive control (FCS-MPC) plus droop control applied to the grid side converter (GSC) of a doubly fed induction generator (DFIG). In this configuration, the rotor side converter (RSC) is responsible for maintaining wind turbine operation at the maximum power point (MPP) extraction, even at the time of a disturbance, while the GSC is responsible for processing the power required to reestablish the microgrid frequency at its rated value. The power required for frequency control comes from a battery energy storage system (BESS) connected to the DC-link, and its value is selected via the FSC-MPC by continuously adjusting the droop gain value. This control configuration has considerable benefits such as continuous operation at the MPP extraction, injection of power proportional to the frequency imbalance, the capability to impose restrictions through the control and it does not use any type of communication between the storage system and the control. Through the FCS-MPC, the gain of the droop controller is selected, which maximizes the power needed to control the frequency of the microgrid. To verify the performance of the proposed control strategy, simulations are performed for an unexpected islanding of the microgrid under different wind speed scenarios. The results show that the DFIG equipped with the proposed control strategy is able to provide ancillary services such as PFR in all DFIG operating modes.