Dynamic simulation of the effect of vehicle-side pressure loss of hydrogen fueling process

This study explains the fundamental mathematical equations used for the main component models that are implemented in freely available library for hydrogen fueling station. The paper provides a background to the model formulation and theory, useful for the further investigations of hydrogen fueling stations. The model was verified against a specific manufacturer model, and it was validated by using test data from an actual fueling station. The study works as documentation and validation of the model formulation. The simulation library is used to make a model for investigating how the pressure loss in the vehicle affects the fueling process. Keeping the temperature out of the station constant and fueling to 80 MPa in the compressed hydrogen storage system, the pressure loss in the compressed hydrogen storage system directly correlates to the final temperature. The final temperature increases with increasing pressure losses. It is also shown that with no pressure loss in the vehicle the fueling has no limit in fueling speed as the heat of compression depends on the mass filled and the enthalpy of the mass, and not the filling time.     

Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing

Developing low carbon cities is a key goal of 21st century planning, and one that can be supported by a better understanding of the factors that shape travel behaviour, and resulting carbon emissions. Understanding travel based carbon emissions in mega-cities is vital, but city size and often a lack of required data, limits the ability to apply linked land use, transport and tactical transport models to investigate the impact of policy and planning interventions on travel and emissions. Here, we adopt an alternative approach, through the development of a static spatial microsimulation of people’s daily travel behaviour. Using Beijing as a case study, we first derive complete activity-travel records for 1026 residents from an activity diary survey. Then, using the 2000 population census data at the sub-district level, we apply a simulated annealing algorithm to create a synthetic population at fine spatial scale for Beijing and spatially simulate the population’s daily travel, including trip distance and mode choice at the sub-district scale. Finally, we estimate transport CO₂ emission from daily urban travel at the disaggregate level in urban Beijing.     

Decision support system for collision avoidance of vessels

Although there are many advanced systems to assist vessels with passing through narrow waterways, it continues to be a serious problem because of disturbance factors and geographic structure. By taking Istanbul Strait as a model for this study, we aimed to develop a decision support system and/or a guidance method to assist reciprocally passing vessels. The main purpose is to develop an Artificial Neural Network (ANN) that uses the data of manually controlled vessels to generate predictions about the future locations of those vessels. If there is any possibility of collision, this system is aimed to warn the operators in the Vessel Traffic Services (VTS) centre and to guide the personnel of the vessels. In this study, manually controlled and reciprocally passing vessels’ data were used (including coordinates, speed, and environmental conditions), neural networks were trained, and predictions were made about the locations of vessels three minutes after the initial point of evaluation (this duration was determined by considering the conditions of Istanbul Strait). With this purpose, we used data gathered from vessels and proved the success of the system, especially concerning predictions made during turnings, by determining the possibility of collision between two vessels three minutes after the data was gathered.     

车载自组网城市环境信道传播模型设计与仿真

针对车载自组网（VANET）在城市环境中信道传播模型不真实的问题,提出了一个考虑障碍物影响的信道传播模型。首先,通过地图位置处理把信号的传播分为直接的视距传输、经过1次转向的非视距传输和禁止传输三类;其次,给出了视距与非视距传输下接收端的接收功率表达式;最后,推导了Nakagami分布下的分组传递率。理论分析和洪泛广播协议仿真实验结果表明,该模型有效反映了信号传播受路旁障碍物影响的真实情况,在轻负载的稀疏场景降低了31.4个百分点的覆盖率,而在重负载的稠密场景则提升了13.32个百分点的覆盖率。所提模型模拟了5.9 GHz高频信号的直线传输效应,为通信协议的设计策略以及真实的VANET仿真提供了依据。     

Thin-walled structures as impact energy absorbers

The key structural components of the majority of transportation vehicles are designed as thin-walled components. During a crash event, a number of structural components must sustain abnormal loadings in order to meet stringent integrity requirements. At the same time other components must dissipate impact energy in a controlled manner that limits the deceleration of a vehicle to a required safety limit. The present paper focuses on the crushing mechanics of thin-walled components. The analysis method is based on the Superfolding Element (SE) concept, which originates from experimentally observed folding patterns of crushed shell elements. The paper presents milestones of the underlying theory of plastic shells and basic design considerations that are coupled with the SE-based predictive techniques in a CAE software. The paper also presents basic examples of the design process of typical energy absorbing components.     

A Metacognitive Fully Complex Valued Functional Link Network for solving real valued classification problems

In this paper, a sequential learning based meta-cognitive fully complex valued functional link network (Mc-FCFLN) is developed for solving complex real world problems. Mc-FCFLN network consists of two components: a cognitive component and a meta-cognitive one. A fully complex-valued functional link network (FCFLN) is a cognitive component and the self-regulatory learning method is its meta-cognitive component. As the network does not possess hidden layers, the multi-variable polynomials are represented in the input layer for capturing the nonlinear relationship between the input and the output sample. Moreover, when the sample is presented to the Mc-FCFLN network, the meta-cognitive component decides what to learn, when to learn, and how to learn depending on the knowledge gained by the FCFLN network and the novel information present in the sample. The network can learn sample one after the other and thus the drawback existing with the batch learning strategy can be eliminated while orthogonal least square principle is used for selecting the best polynomial and the recursive least square update is used for tuning the network. Multi-category and binary datasets chosen from the UCI machine learning repository is used for the validation of the proposed classifier. Lastly, a performance comparison of the Mc-FCFLN applied for classification problems shows better classification ability when compared with the other existing classifiers in the literature.     

Detecting and tracking vehicles in traffic by unmanned aerial vehicles

Using unmanned aerial vehicles (UAV) as devices for traffic data collection exhibits many advantages in collecting traffic information. This paper introduces a new vehicle detecting and tracking system based on image data collected by UAV. This system uses consecutive frames to generate vehicle's dynamic information, such as positions and velocities. Four major modules have been developed: image registration, image feature extraction, vehicle shape detecting, and vehicle tracking. Some unique features have been introduced into this system to customize the vehicle and traffic flow and to jointly use them in multiple consecutive images to increase the system accuracy of detecting and tracking vehicles. Field tests demonstrate that the present system exhibits high accuracy in traffic information acquisition at different UAV altitudes with different view scopes, which can be used in future traffic monitoring and control in metropolitan areas.     

Adaptive linear discriminant regression classification for face recognition

Linear discriminant regression classification (LDRC) was presented recently in order to boost the effectiveness of linear regression classification (LRC). LDRC aims to find a subspace for LRC where LRC can achieve a high discrimination for classification. As a discriminant analysis algorithm, however, LDRC considers an equal importance of each training sample and ignores the different contributions of these samples to learn the discriminative feature subspace for classification. Motivated by the fact that some training samples are more effectual in learning the low-dimensional feature space than other samples, in this paper, we propose an adaptive linear discriminant regression classification (ALDRC) algorithm by taking special consideration of different contributions of the training samples. Specifically, ALDRC makes use of different weights to characterize the different contributions of the training samples and utilizes such weighting information to calculate the between-class and the within-class reconstruction errors, and then ALDRC seeks to find an optimal projection matrix that can maximize the ratio of the between-class reconstruction error over the within-class reconstruction error. Extensive experiments carried out on the AR, FERET and ORL face databases demonstrate the effectiveness of the proposed method.     

Energy transmittance of focused femtosecond pulses at different air pressures

Transmission of intense femtosecond laser pulses in air is accompanied by energy depletion. By measuring the transmitted spectra of the focused femtosecond pulses in air, we study the influence of air pressure and initial pulse energy on the spectra around the central wavelength (800 nm) after the interaction of the focused femtosecond laser with air. On this basis, the energy transmittance of the central wavelength of the femtosecond pulses is obtained. It is found that as the air pressure is lower than 1 kPa, the transmitted spectra of focused femtosecond pulses change with the pressure, but there is almost no energy depletion, while as the air pressure is higher than 1 kPa, femtosecond filamentation occurs and the energy transmittance of the central wavelength of the femtosecond pulses decreases with the increase of air pressure and pulse energy. According to the different regimes (i.e., nonfilamentation, and filamentation regimes), we discuss the effect of energy conversion and transfer on the energy transmittance. This work can help to understand the energy depletion during the transmission of ultrashort intense laser pulses in air and provide a guidance for the practical applications of femtosecond filamentation.