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108 Research products, page 1 of 11

  • Publications
  • 2012-2021
  • Conference object
  • DK
  • Transport Research

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  • Open Access
    Authors: 
    Anders Fjendbo Jensen; Mikkel Thorhauge; Stefan Eriksen Mabit; Jeppe Rich;
    Country: Denmark

    Abstract The electrification of transport systems requires a change in the composition of the vehicle fleet towards higher shares of electric vehicles. A successful transition, however, depends on many factors of which some relate to purchase prices and vehicle features, while others relate to technology and charging infrastructure. This paper analyses the transition towards plug-in electric vehicles. We use data from a large representative Danish stated choice survey. Based on these data, we estimate a mixed logit model that allows for correlated random effects across fuel types and car segments as well as systematic heterogeneity. The results show that correlation and substitution indeed goes across these dimensions. Willingness-to-pay (WTP) measures are estimated for a variety of attributes. These suggest that the WTP for range varies with fuel types, that the possibility for home charging is highly valued, and that CO2 is a significant concern among individuals in the sample.

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Open Access
    Authors: 
    Panagiotis Tsagkaroulis; Andreas Thingvad; Mattia Marinelli; Kenta Suzuki;
    Publisher: IEEE
    Country: Denmark

    Electric Vehicles (EV)s can with the right charger and aggregated in large numbers be considered as a large storage unit. If the chargers have bidirectional power converters, EVs connected to the grid, could provide Vehicle-to-Grid (V2G) ancillary services. The annual earnings from delivering frequency-controlled normal operation reserve in Denmark is calculated, based on individual user profiles. The individual earnings are strongly dependant on the driving time, distance and parking time at different locations which determine the availability of each EV to provide ancillary services. The specific user behaviour is based on a set of telematics data acquired from 7,163 Nissan LEAFs in the United States, with information about every driving and charging sessions during one year. The profit of the individual EV, spreads from 51 to 1654 AC/year. A data set of one year of system frequency measurements from the Nordic grid is used to calculate the impact of the service provision on the State of charge (SOC).

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Restricted
    Authors: 
    Mohsen Banaei; Fatemeh Ghanami; Mohammad Hassan Khooban; Jalil Boudjadar;
    Publisher: IEEE

    This paper investigates the energy management of a roll-on/roll-off emission-free ship (RoRoEF-ship) that can carry passengers and vehicles. Fuel cells (FCs), energy storage systems (ESS), and cold-ironing (CI) are considered to be the energy resources of the ship. Moreover, the charging and discharging abilities of the electric vehicles (EVs) carried by the ship are used as an auxiliary source for managing the energy flow in the ship. In this paper we propose three cost-effective strategies to use EVs as a backup in the ship energy system based on the agreements between the ship operator and EV owners. A mixed-integer linear programming (MILP) approach is proposed to a highly efficient and cost-effective control for the energy management of the integrated solution (ship resources and EVs). The proposed model considers the non-linearity in the efficiency of FCs, power ramp-rate constraints of FCs and ESSs, and preferences of the EV owners. The proposed control model is applied to a test system and the simulation results are discussed. GAMS software is used to achieve the optimization process.

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Closed Access
    Authors: 
    Reza Nasirigerdeh; Reihaneh Torkzadehmahani; Jan Baumbach; David Blumenthal;
    Publisher: ACM
    Country: Denmark

    Federated learning (FL) is becoming an increasingly popular machine learning paradigm in application scenarios where sensitive data available at various local sites cannot be shared due to privacy protection regulations. In FL, the sensitive data never leaves the local sites and only model parameters are shared with a global aggregator. Nonetheless, it has recently been shown that, under some circumstances, the private data can be reconstructed from the model parameters, which implies that data leakage can occur in FL. In this paper, we draw attention to another risk associated with FL: Even if federated algorithms are individually privacy-preserving, combining them into pipelines is not necessarily privacy-preserving. We provide a concrete example from genome-wide association studies, where the combination of federated principal component analysis and federated linear regression allows the aggregator to retrieve sensitive patient data by solving an instance of the multidimensional subset sum problem. This supports the increasing awareness in the field that, for FL to be truly privacy-preserving, measures have to be undertaken to protect against data leakage at the aggregator.

  • Closed Access
    Authors: 
    Enrica Raheli; Qiuwei Wu; Changyun Wen;
    Publisher: IEEE

    The power system and the natural gas network are becoming increasingly interconnected due to the rising number of gas-fired power plants and the emergence of power-to-gas technology. The coordinated operation of power and gas systems is a promising solution to add flexibility to future energy systems, facilitating renewable integration. The optimal operation of the power system is commonly modeled as a mixed-integer linear problem (MILP). Conversely, the natural gas network optimization is a mixed-integer nonlinear problem (MINLP), due to the highly nonlinear and nonconvex Weymouth equation modeling the gas flow in pipelines. Different linearization and convexification approaches have been investigated in the literature for the gas optimization problem, but few authors have verified the quality of the solution. In this paper, a MILP model for the gas problem is developed using an outer approximation technique, and the feasibility of the solution is assessed. A sensitivity analysis on the number of linearization breakpoints is conducted to show the impact on the solution quality and computational time.

  • Closed Access
    Authors: 
    Pınar Kaygan; HARUN KAYGAN; Asuman Özgür;
    Country: Denmark

    The social construction of gender through the design of technological artefacts, such as automobiles, motorcycles and domestic technologies, has received growing interest within feminist technology studies (FTS). Building on the extant FTS literature, in this research we explore how design of public transport (bus, minibus, metro) as a sociotechnical system shapes women's experiences of commute in their everyday lives. Drawing on empirical data that comes from interviews with 32 women, we focus on the complex entanglements of the women’s interactions (1) within the vehicle as a technological artefact with its layout, interior elements and technologies such as cameras, and (2) with other passengers (both men and women) and the driver. These entanglements constitute gendered experiences in public transport. Our findings specify the strategies women develop with concerns of (physical and social) personal space, safety, and travel hours in public transport; some of which have gained more prominence during the Covid-19 pandemic. We underline the diversity of these strategies depending on vehicle types, routes, and time of travel within which women negotiate the material and social interactions. We argue that such interactions can, and should, inspire all stakeholders for responsible innovation for inclusive and egalitarian public transport design.

  • Closed Access
    Authors: 
    Vajira Thambawita; Steven Alexander Hicks; Jonas L. Isaksen; Mette Haug Stensen; Trine B. Haugen; Jørgen K. Kanters; Sravanthi Parasa; Thomas de Lange; Håvard D. Johansen; Dag Johansen; +3 more
    Publisher: IEEE

    Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.

  • Closed Access English
    Authors: 
    Kishor V. Bhadane; P. Sanjeevikumar; Baseem Khan; Mohan Thakre; Akbar Ahmad; Tuhsar Jaware; Dipak P. Patil; Arvind.S. Pande;
    Publisher: IEEE
    Country: Denmark

    At present scenario, globally commonly used transportation system are based on IC Engine based vehicles which affects environment due to emission of greenhouse gases. due to the systematic approach for electric transportation system towards green transportation and reducing the crucial issues of global climate changes are the initiative for saving the environment. In recent years the transport industry has become very popular with Electrical Vehicles (EVs). Due to its recent development which will likely to replace the ordinary IC Engine based vehicle in near future to save the nature against the pollution. In this paper, present EV subsystems and its configurations, components of EV are discussed. This paper focus on reviewing the present scenario of EV, latest development of EV and challenges, opportunities for effective deployment of EVs are highlighted. Also there is scope for effective implementation of smart grid technology in EV, grid to vehicle and vehicle to grid, vehicle to home as well as home to vehicle technology along with renewable power incorporation connectivity to EVs and Grid framework and future study developments are also underlined. The paper is intended to include the latest technology and new solutions for future production of electrical vehicles in order to lead to future research into this area.

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Closed Access English
    Authors: 
    Huamin Ren; Filippo Maria Bianchi; Jingyue Li; Rasmus Løvenstein Olsen; Robert Jenssen; Stian Normann Anfinsen;
    Publisher: IEEE
    Country: Denmark

    Non-intrusive load monitoring is an important energy disaggregation technology, which can provide appliance-level consumption estimation given a series of total consumption over time, aiding users green awareness and living as well as device fault detection. Despite of the claimed performance, an insightful analysis on practical perspectives is still lacking. In this paper we therefore propose a unified pipeline to conduct comparative experiments on four representative state-of-the-art algorithms. We further provide an in-depth look at two crucial factors affecting algorithms’ feasibility in practice, namely sampling rate and transferability, as well as the algorithms’ performance on energy disaggregation.

  • Publication . Contribution for newspaper or weekly magazine . Conference object . 2021
    Open Access
    Authors: 
    Meisam Jamshidi Seikavandi; Kamal Nasrollahi; Thomas B. Moeslund;
    Publisher: SPIE
    Country: Denmark

    Recent advances have shown sensor-fusion’s vital role in accurate detection, especially for advanced driver assistancesystems. We introduce a novel procedure for depth upsampling and sensor-fusion that together lead to an improved detectionperformance, compared to state-of-the-art results for detecting cars. Upsampling is generally based on combining datafrom an image to compensate for the low resolution of a LiDAR (Light Detector and Ranging). This paper, on the otherhand, presents a framework to obtain dense depth map solely from a single LiDAR point cloud that makes it possible touse just one deep network for both LiDAR and image modalities. The produced full-depth map is added to the grayscaleversion of the image to produce a two-channel input for a deep neural network. The simple preprocessing structure isefficiently competent in filing cars’ shapes, which helps the fusion framework to outperforms the state-of-the-art on theKITTI object detection for the Car class. Additionally, the combination of depth and image makes it easier for the networkto discriminate highly occluded and truncated vehicles.

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