bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the economical impact of innovations for offshore wind farms through a holistic modelling approach
Authors: Sumetha-Aksorn Potchara , Dykes Katherine , Ceyhan Yilmaz Ozlem 
Published: June 2022

Abstract
In this work, a holistic modelling approach is developed and applied to the valuation of innovations in offshore wind energy. Two innovations are considered: 1) a more accurate modelling of vessel movement related to operations and maintenance activities and 2) an operational strategy for market participation. In both cases, a standard process was followed of first mapping the effects of the technologies on wind energy system’s levels, then assessing them through development and augmentation of a holistic cost and valuation model. The process is demonstrated for each innovation and quantifies their potential impacts to LCoE and NPV respectively. The models and methods can be extended to other innovations for quantitative assessment of potential benefits.
Development of engineering cost models for integrated design optimization of onshore and offshore wind farms
Authors: Yilmazlar Kutay , Cacciola Stefano , Xiolesmy Alfonso Rodriguez Maryi , Croce Alessandro 
Published: June 2022

Abstract
The goal of any multidisciplinary design optimization problem for wind power plants is to reduce the overall levelized cost of the energy. When it comes to designing a wind farm, one has to find the best combination of multiple parameters, such as turbine types, turbine dimensions and farm layout, to ensure the minimum cost. Clearly, since any design parameter may affect several cost items and performance indices of a farm, the most cost-effective solution should handle the mutual coupling among all design variables. For example, increasing the turbine spacing surely has a positive impact on energy production due to the minimization of wake losses, but, at the same time, may have a detrimental impact on the cost of cabling. In order to assist multidisciplinary design activities for wind farms and wind turbines, the present work is aimed at developing a tool for preliminary estimation of the levelized cost of energy of land- and sea-based wind farms. Such a tool is based on a modular architecture, which will ease the integration of the tool in a multi-level design framework. Each module of the tool implements one or more engineering models to estimate all cost items along with the annual energy production of the farm, starting from a few pieces of information related to turbine types and dimensions, farm geometry and wind conditions.
Providing insight into what can be expected from Offshore Wind Farm Layout Optimisation
Authors: Thomson Michael , Zaaijer Michiel 
Published: June 2022

Abstract
This is not yet another study into better modelling or optimiser selection for OWFLO. Instead, this study aims to provide insight into what performance can be expected from offshore wind farm layout optimisation(OWFLO) and to know when further optimising is not justifiable anymore. The study consists of three parts. All three parts make use of a referent. (The definition of the term’referent’ as used here is given in the paper.) The first part uses the referent to find and understand the characteristics of the OWFLO problem. Wind farms with 9, 25 and 64 turbines have been optimised 100 times with the referent. The results show a small spread in the performance of the found optimised layouts, indicating that many local optima exist with similar performances in an OWFLO problem. The second part compares performances from optimised layouts with 25 turbines resulting from optimisations with alternative implementation choices, evaluated by the referent model. The difference in performance resulting from the alternative optimisers indicates that improvement of a state-of-the-art optimiser is not expected to lead to much better results. The third part explores the need to improve the analysis by adding a phenomenon currently not considered in OWFLO. The influence of neighbouring wind farms(NBWFs) on layout optimisation without including atmospheric stability is investigated. It is evident that adding NBWFs for accurate energy yield assessments is necessary. However, for layout optimisation, the benefit of including NBWFs is not apparent.
Operation and Maintenance Modelling for Multi Rotor Systems: Bottlenecks in Operations
Authors: McMorland Jade , Pirrie Paul , Collu Maurizio , McMillan David , Carroll James , Coraddu Andrea , Jamieson Peter 
Published: June 2022

Abstract
As the installed capacity of individual turbines increases, so do costs associated with manufacture and maintenance. One proposed solution to this problem is the Multi-Rotor System (MRS) which utilises many small rotors to yield the same energy capture as a single large turbine. The operational advantage of the MRS is the built in redundancy between rotors on the same structure. However, despite this advantage, an increase in number of components is likely to result in an increase in transfers. This work examines the balance between additional crew and vessel requirements for such a structure against the expected savings in downtime due to redundancy and small rotor power rating. Three scenarios are analysed to determine the distribution of the failures which contribute to downtime. The study aims to find the optimal vessel fleet which limits downtime without drastically increasing direct operational expenditure (OpEx). As site size increases, the impact of global failures, which shut down the whole asset, is lessened. However, there is a significant increase in the number of vessels required to reduce downtime to <10% of the total OpEx. While a large fleet can offer significant downtime savings, there are practical limitations and challenges which must be acknowledged.
Unsteady aerodynamic simulations of a multi-megawatt airborne wind energy reference system using computational fluid dynamics
Authors: Pynaert N , Wauters J , Crevecoeur G , Degroote J 
Published: June 2022

Abstract
Airborne wind energy (AWE) is an emerging technology for the conversion of wind energy into electricity by flying crosswind patterns with a tethered aircraft. Having a proper understanding of the unsteady interaction of the air with the highly dynamic system during operation is key to developing viable AWE systems. High fidelity simulation tools are needed to correctly predict these interactions, which will provide insights into the design and operation of advanced and efficient AWE systems. The research goal of this contribution is to predict the time-varying aerodynamic forces acting on a reference system, which will be achieved using high-fidelity modelling. This work contains a feasibility study that uses the Chimera overset technique to simulate aerodynamic behaviour over the complete flight trajectory. The motion of the AWE system is included by overlaying the moving body-fitted mesh attached to the AWE aircraft over the background mesh. The Chimera overset technique connects both meshes, interpolating the solution at the overset boundary. This technique has been proven to be a robust approach to simulate the motion of an AWE system in computational fluid dynamics (CFD) simulations.
Experimental Investigation on the Effect of Lateral Turbine Spacing on Interactions of Wakes
Authors: Maus J , Peinke J , Hölling M 
Published: June 2022

Abstract
In recent years, development in wind energy has put great attention on the manifold possibilities of increasing wind farm efficiencies. Motivated by multiple positive effects dualrotors promise at controlling and mitigating wakes, it is goal of this work to gain experimental insight on the characteristics of a double-rotor wake in dependency of the lateral spacing between the turbines. Additionally, the influence of the rotational direction of the model wind turbine showed to be of great relevance and will be studied under laminar inflow conditions between 4 – 7 ms−1. An array of 21 hot-wires at a sampling frequency of 6 kHz will be traversing until 8 D downstream of the double rotor plane to allow for one- and two-point statistics in the wake interaction region. It will be presented that both lateral distance and direction of rotation has great effect on the entrainment of undisturbed air into the dual wake. In terms of turbine control, decreasing the lateral distance increases the generated power by 4-9%. The knowledge on turbulence structures and their decay in the mixing area could be used to adapt, optimise and implement control strategies in wind farms for a better entrainment and thus an overall quicker wake recovery.
A Three-Dimensional Target Recognition Algorithm Utilizing Spatiotemporal Information Perception
Authors: Wang Hongmei , Huang Cheng 
Published: June 2022

Abstract
In order to solve the low accuracy of three-dimensional target recognition by traditional algorithms, an improved three-dimensional target recognition model based on spatiotemporal convolutional neural network is established in this paper. The high-level feature presentation ability of deep neural network and the spatiotemporal perception performance of sequential modelling methods are combined perfectly in this model. Triplet loss function is also introduced to improve the representation of three-dimensional moving targets under different attitudes, which effectively reduces the feature distance of the same targets under various attitudes, and increases the feature distance of different targets at the same time. The performance of different feature extractors and aggregation algorithms is compared in this paper. Experimental results demonstrated that the ResNet is more suitable for feature extraction and the local soft attention is more effective in feature aggregation. Compared with the traditional three-dimensional target recognition methods, the spatiotemporal perception model proposed in this paper can achieve higher recognition accuracy for three-dimensional target recognition. At the same time, the calculation speed of the model is fast, which is conducive for its application.
Unmanned Aerial Vehicle Visual Simultaneous Localization and Mapping: A Survey
Authors: Tian Y , Yue H , Yang B , Ren J 
Published: June 2022

Abstract
Simultaneous Localization and Mapping (SLAM) has been widely applied in robotics and other vision applications, such as navigation and path planning for unmanned aerial vehicles (UAVs). UAV navigation can be regarded as the process of robot planning to reach the target location safely and quickly. In order to complete the predetermined task, the drone must fully understand its state, including position, navigation speed, heading, starting point, and target position. With the rapid development of computer vision technology, vision-based navigation has become a powerful tool for autonomous navigation. A visual sensor can provide a wealth of online environmental information, has high sensitivity, strong anti-interference ability, and is suitable for perceiving dynamic environments. Most visual sensors are passive sensors, which prevent sensing systems from being detected. Compared with traditional sensors such as global positioning system (GPS), laser lightning, and ultrasonic sensors, visual SLAM can obtain rich visual information such as color, texture and depth. In this paper, a survey is provided on the development of relevant techniques of visual SLAM, visual odometry, image stabilization and image denoising with applications to UAVs. By analyzing the existing development, some future perspectives are briefed.
Research on Pavement Crack Detection Algorithm based on Deep Residual Unet Neural Network
Authors: Yang Yalong , Zhao Zihao , Su Liangliang , Zhou Yu , Li Hui 
Published: June 2022

Abstract
Pavement crack is one of the main factors threatening highway safety. Rapid and accurate identification of pavement crack is very important to protect highway. Traditional image processing methods have been difficult to meet the requirements of practical engineering. So, this paper proposes an improved pavement crack detection method based on deep convolution neural network model, to overcome the inaccurate segmentation caused by complex background and fuzzy crack edge in asphalt pavement image, which is an encoder-decoder network structure, and integrates the idea of semantic segmentation into image detection. The encoder of the proposed method adopts the ResNet’s residual structure to strengthen image feature extraction, the decoder uses transpose convolution from the original UNet structure for up sampling, and integrates the encoder features with the up sampled features after cutting, Strengthen the learning of network features. Experimental results show that the F1 score, Precision and Recall are significantly improved on the asphalt crack dataset. Compared with the traditional algorithm, the proposed algorithm can segment the cracks better and improve the accuracy of pavement crack detection.
Fire Detection and Localization Method Based on Deep Learning in Video Surveillance
Authors: Fang Q S , Peng Z , Yan P 
Published: June 2022

Abstract
Fire detection and localization in video surveillance had become a particularly important part of disaster rescue. Considering the fire detection and localization of slow detection speed, low detection accuracy, and low localization precision in video surveillance, we proposed a fire detection and localization method based on deep learning. The first thing we improved the SuperPoint method to extract video keyframe in video surveillance. The next thing we employed Convolutional Neural Network (CNN) model to detect the fire on the extracted video keyframes. The last thing we located the fire via superpixel and CNN on the extracted video keyframes which broke out a fire. The experimental results on open fire dataset revealed that the recall of keyframe extraction reached 0.83, the precision of fire detection reached 0.96 and the F1-score of fire localization reached 0.90. Our method realized rapid and accurate detection and precise localization of fire in video surveillance.
Prediction Method of Biological Fermentation Data Based on Deep Neural Network
Authors: Li Kang , Jiang Yizhang 
Published: June 2022

Abstract
This paper proposes a (Takagi-Sugeno-Kang) TSK fuzzy regression model that based on self-supervised learning and deep autoencoder to predict and monitor the real-time concentration of each ingredient in the fermentation process. The entire model consists of the following steps: obtaining and preprocessing sample spectral data to obtain a training set; using the training set to train a self-supervised feature extraction network model to optimize the parameters of the feature extraction network model; training the autoencoder network model to establish a dimensionality reduction model by using the feature-extracted data; performing TSK fuzzy regression on the data selected by the dimensionality reduction model to establish a concentration prediction model; inputting the spectral data of the solution to be tested to predict the concentration of the solution. Combined with the deep autoencoder feature extraction method of self-supervised learning, our model can not only construct a more complex nonlinear map than the traditional principal component analysis (PCA), but also ensure that the extracted features have semantic information that is beneficial to the subsequent regression prediction method. Combined with TSK regression prediction, our model can avoid the problem of excessive spectral data dimension and redundant information, and can give accurate and interpretable results.
YOLO-A2G: An air-to-ground high-precision object detection algorithm based on YOLOv5
Authors: Li Lei , Yao Ziyu , Miao Zhiyong , Qiu Xuyi , Yang Xizhong 
Published: June 2022

Abstract
Air-to-ground object detection is playing an increasingly important role in a variety of ground awareness and cognitive missions such as fighter aircraft attempting to assault and defend ground defense barrier fortifications and strike and destroy ground objects. However, air-to-ground object detection becomes very challenging due to the insufficient number of battlefield samples in air-to-ground imaging, many ground background disturbances and large-scale variation. In this paper, an improved air-to-ground object detection algorithm, YOLO-A2G, is proposed to solve this problem based on YOLOv5. In YOLO-A2G, firstly, in response to the insufficient number of samples, we used the direct and inverse Visual Focus (VF) affine a data augmentation algorithm to enrich and expand the samples in addition to the original data augmentation algorithm of YOLOv5. We then introduced the Coordinate Attention (CA) mechanism into the head network of YOLOv5 to autonomously learn explicit and implicit knowledge for the purpose of feature focusing and redundancy removal. Finally, in the post-processing stage after the network prediction, we used Weighted Boxes Fusion (WBF) instead of the traditional NMS to achieve spatial scale fusion. We performed an experimental validation using the Air-to-Ground (A2G) dataset and mAP of YOLO-A2G reached 94%.
Sketch Based Image Retrieval for Architecture Images with Siamese Swin Transformer
Authors: Xu Yuxin , Yan Yuyao , Lin Yiming , Yang Xi , Huang Kaizhu 
Published: June 2022

Abstract
Sketch-based image retrieval (SBIR) is an image retrieval task that takes a sketch as input and outputs colour images matching the sketch. Most recent SBIR methods utilise deep learning methods with complicated network designs, which are resource-intensive for practical use. This paper proposes a novel compact framework that takes the siamese network with image view angle information, targeting the SBIR task for architecture images. In particular, the proposed siamese network engages a compact SwinTiny transformer as the backbone encoder. View angle information of the architecture image is fed to the model to further improve search accuracy. To cope with the insufficient sketches issue, simulated building sketches are used in training, which are generated by a pre-trained edge extractor. Experiments show that our model achieves 0.859 top-one accuracy exceeding many baseline models for an architecture retrieval task.
Quantum tomography and Schwinger’s picture of quantum mechanics*
Authors: Ciaglia F M , Di Cosmo F , Ibort A , Marmo G 
Published: June 2022

Abstract
In this paper the problem of tomographic reconstruction of states is investigated within the so-called Schwinger’s picture of quantum mechanics in which a groupoid is associated with every quantum system. The attention is focussed on spin tomography: in this context the groupoid of interest is the groupoid of pairs over a finite set. In a nutshell, this groupoid is made up of transitions between all possible pairs of outcomes belonging to a finite set. In addition, these transitions possess a partial composition rule, generalizing the notion of groups. The main goal of the paper consists in providing a reconstruction formula for states on the groupoid-algebra associated with the observables of the system. Using the group of bisections of this groupoid, which are special subsets in one-to-one correspondence with the outcomes, a frame is defined and it is used to prove the validity of the tomographic reconstruction. The special case of the set of outcomes being the set of integers modulo n, with n odd prime, is considered in detail. In this case the subgroup of discrete affine linear transformations, whose graphs are linear subspaces of the groupoid, provides a quorum in close analogy with the continuous case.
Performance evaluation of image reconstruction algorithms for a megavoltage computed tomography system on a helical tomotherapy unit
Authors: Tegtmeier Riley C , Ferris William S , Bayouth John E , Culberson Wesley S 
Published: June 2022

Abstract

Objective. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system. Approach. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey’s honestly significant difference post-hoc tests were utilized for statistical analysis. Main Results. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results. Significance. An overall improvement in image quality for metrics most important to IGRT and ART—mainly image noise and contrast—was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.
Electron beam lithography on non-planar, suspended, 3D AFM cantilever for nanoscale thermal probing
Authors: Swami R , Julié G , Singhal D , Paterson J , Maire J , Le-Denmat S , Motte J F , Gomès S , Bourgeois O 
Published: June 2022

Abstract
Electron beam lithography (EBL) on non-planar, suspended, curved or bent surfaces is still one of the most frequently stated problems for fabricating novel and innovative nano-devices and sensors for future technologies. Although spin coating is the most widespread technique for electron resist (e-resist) deposition on 2D or flat surfaces, it is inadequate for suspended and 3D architectures because of its lack of uniformity. In this work, we use a thermally evaporated electron sensitive resist the QSR-5 and study its sensitivity and contrast behaviour using EBL. We show the feasibility of utilizing the resist for patterning objects on non-planar, suspended structures via EBL and dry etching processes. We demonstrate the integration of metal or any kind of thin films at the apex of an atomic force microscopy (AFM) tip. This is showing the great potential of this technology in various fields, such as magnetism, electronic, photonics, phononics and other fields related to near field microscopy using AFM probe like for instance scanning thermal microscopy.