Influence of the Distributed Grounding Layout for Intercity Trains on the “Train–Rail” Circumflux

Xiao, Song; Cao, Ye; Wu, Guangning; Guo, Yujun; Gao, Guoqiang; Liu, Puyang; Wang, Zijing; Li, Pupu; Yu, Jie; Zhang, Yuhui; Chen, Zheng; Sykulski, J.K.

doi:10.1109/tcsii.2022.3223984

Cited by 13 publications

(12 citation statements)

References 10 publications

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“…Pavement failures can endanger trafc safety, and various additives and nanomaterials can be taken into account in this regard, which can be examined in a feld investigation that is in line with this study [35][36][37][38][39][40]. Simulation devices and vehicles can also be adopted in incorporation with the Internet of Tings (IoT), vehicle-mounted equipment, and sensors to develop the potential of driver motion and physiological signal monitoring [41][42][43][44][45]. Various human factors may cause a driver to leave the travel lane and afect the occurrence of accidents, such as driver's fatigue and inattention, which can be investigated in future research studies [46,47].…”

Section: Discussionmentioning

confidence: 97%

Safety Comparison of Simple and Spiral Horizontal Curves Based on Side Friction Factor Dynamic Modeling

Aldeen

Kordani

Fallah

et al. 2023

Journal of Advanced Transportation

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A horizontal curve’s geometric design is considered an important factor in highway accidents, and simple and spiral curves are regarded as the most common types of horizontal curves. Various factors affect the safety of horizontal curves, one of the most important of which is the side friction factor in the horizontal curves. Therefore, in this study, the safety of simple and spiral horizontal curves was investigated for the E-class sedan, E-class SUV, and two-axle conventional truck based on the side friction factor. In this regard, CarSim and TruckSim vehicle dynamic simulation software were utilized using 360 scenarios, including vehicle speed, vehicle type, curve radius, and road geometry. It was revealed that the maximum side friction factor for all vehicles in the simple horizontal curve was higher than the spiral horizontal curve. Also, the process of increasing the side friction factor was carried out with a gentler slope in the spiral horizontal curve. Except for the radius of 0.7 times the maximum radius of the spiral horizontal curve (R) for the truck and the radii of 0.7 R and 0.9 R for the sedan and SUV, the maximum side friction factor in simple and spiral horizontal curves was lower than the AASHTO recommended values, which shows that the spiral horizontal curve was better and safer compared to the simple horizontal curve based on the side friction factor.

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Section: Discussionmentioning

confidence: 97%

Safety Comparison of Simple and Spiral Horizontal Curves Based on Side Friction Factor Dynamic Modeling

Aldeen

Kordani

Fallah

et al. 2023

Journal of Advanced Transportation

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“…The optimization model contains objective function [23][24][25][26][27][28][29][30][31][32][33], and it includes constraints [34][35][36][37][38][39][40][41][42][43][44]. To implement this problem on network, it is needed to uses smart devices [45][46][47][48][49][50] and Telecommunication equipment [51][52][53][54][55]. This section presents the optimal scheduling modelling of the ADN in the presence of IUEE to improve economic status, operation, and power quality (reducing the impact of harmonics) of the network.…”

Section: Formulation Of the Schemementioning

confidence: 99%

“…Expected energy price in this network for off-peak period, 1:00-7:00, middle-load period, 8:00-16:00, and 23:00-00:00, and peak period, 17:00-22:00 is 16, 24, and 30 $/MWh, respectively [15]. This network consists of 10 IUEEs located in 5,11,22,31,41,47,53,60,66, and 68 buses (buses with blue colour in Figure 3), respectively. Each IUEE has a parking lot that can accommodate 200 EVs.…”

Section: Problem Datamentioning

confidence: 99%

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Power management of harmonic polluted active distribution network in the presence of integrated unit of electric spring and electric vehicles parking lot

Rohani¹,

Nahavandi

Abasi

et al. 2023

IET Generation Trans & Dist

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This paper presents the optimal scheduling of active distribution network (ADN) with linear and non‐linear loads in the presence of an integrated unit of electric spring and electric vehicle parking lot (IUEE). The proposed scheme is formulated in the form of three‐objective optimization, which minimizes the expected operating cost of the ADN, voltage deviation function (VDF), and the total harmonic distortion (THD) of the voltage in separate objective functions. Constraints include harmonic power flow (HPA) equations, operation and power quality limits, and operation model of the IUEE. This scheme has uncertainties of load, energy price, and energy demand of EVs, which are modelled here using stochastic programming. Next, the sum of the weighted functions method as a subset of the Pareto optimization technique is adopted to extract the integrated single‐objective problem. Then, Sine‐Cosine Combined Algorithm (SCA) and Gray Wolf Optimization (GWO) are used to achieve an optimal solution. In the end, by examining the numerical results obtained from the implementation of the proposed scheme on a 69‐bus ADN, the capability of the design in enhancing the economic situation, operation, and power quality of the ADN compared to network power flow studies obtained by optimal scheduling of the IUEE is confirmed.

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“…In the section, the study introduces a novel gray model that is called NODGM. Rather than estimating the corresponding differential model [62][63][64][65] by solving the parameters with the least squares approach, the NODGM model defines the function fx1(t), t, q, which replaces estimate 1 with t. The AGO family can treat the bleaching equation as a knot, and by teaching its ODE a better predictive model can be obtained.…”

Section: Nodgm Statementmentioning

confidence: 99%

Neural ordinary differential gray algorithm to forecasting models of controlled systems

Meng

Jiang

et al. 2023

International Journal of Advanced Robotic Systems

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Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the neural ordinary differential equation, which is named the neural ordinary differential gray mode. This neural ordinary differential gray mode permits the forecasting model to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of neural ordinary differential gray mode can provide comprehensive applications as well as learning the properties of distinct data specimens. To acquire a better model which has highly predictive efficiency, afterward, this study trains the model by neural ordinary differential gray mode using the Runge–Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensively spreads the utilize extent of mechanical elastic vehicle wheel.

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Influence of the Distributed Grounding Layout for Intercity Trains on the “Train–Rail” Circumflux

Cited by 13 publications

References 10 publications

Safety Comparison of Simple and Spiral Horizontal Curves Based on Side Friction Factor Dynamic Modeling

Safety Comparison of Simple and Spiral Horizontal Curves Based on Side Friction Factor Dynamic Modeling

Power management of harmonic polluted active distribution network in the presence of integrated unit of electric spring and electric vehicles parking lot

Neural ordinary differential gray algorithm to forecasting models of controlled systems

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