dataesr/scientific-paragraphs-categorization huggingface dataset:
Our single-stage method uses hybrid anchor boxes to directly regress the 2D bounding box as well as the 3D parameters. We thus avoid the problem related to the fluctuation of the accuracy when the ROIs are not fixed. This new approach is the fastest method for 3D prediction from images without sacrificing accuracy. Figure  3  shows an overview of our 3D multi-object detection method. Fig. 2  Images with 3D bounding box ground truths. Our dataset presents a variety of environments and conditions. From top left to bottom right: road with clear weather, Road at night, Road with rainy weather and Rail with clear weather. The ground truths shown are for the same classes as KITTI: Car, Person and Cycle Fig. 3  Overview of our method for 3D multi-object detection. A single RGB image is used as input.
Model size Our method can be presented in different versions with a different size for each, such as in YOLOv5. The depth and width of the network vary with the versions which result in several different learnable parameters. Thus our model L-CNN comes in 3 versions: Large, Medium, and Small. To analyze the performance in terms of 3D detection accuracy and computation time of each version, we have trained and evaluated each of them on the KITTI and GTA datasets.
KITTI We trained our method on the KITTI dataset dedicated to 3D detection on the same training and validation split defined in  [36]. The training split contains 3712 images and (  7 ) L dim = Mean( 1 N N ∑ k=1 | | -̃ - | | ). ( 8 ) L orient = 1 N N ∑ k=1 e k , e k = 0. 5(𝛼 k -αk ) 2 ∕𝛽, if |𝛼 k -αk | < 𝛽 |𝛼 k -αk | -0. 5 × 𝛽, otherwise. the validation split 3769 images. To speed up the training process and improve accuracy, we trained our models with either pre-trained weights from YOLOv5 or pre-trained weights from our model after training for 15 epochs on our GTAV dataset. We demonstrate that pre-training our model on our dataset significantly improves the accuracy of our method on the KITTI dataset. Furthermore, as in  [27]  we perform the training with both the left and right images of the training split of KITTI thus doubling the number of images available for the training resulting in improved performances of our method during evaluation.
GTA Inspired by previous work on dataset creation  [16]  using images from the video game Grand Theft Auto V, we created our hybrid dataset of road and rail images. This new dataset allows us to overcome the problem of having a railway dataset with a ground truth rich enough to allow 3D bounding box learning for cars, pedestrians, and motorcycles. The training of our method was conducted on a split containing road and railway images (107 K images). The evaluation was then conducted on the validation split of the dataset containing 11,630 images. The training on this dataset was performed on 50 epochs.
Data augmentation Data augmentation is very useful to improve performance of CNN models through the construction of new and different examples for training. Even though KITTI and our GTA-V dataset presents a variety of environments and conditions (in GTA-V dataset for example, we have different situations like road with clear weather, road at night, road with rainy weather, and rail with clear weather as it shown in Fig. 1 ), the risk of overfitting our models is still present. Data augmentation gives multiple advantages: 1. Reduce overfitting, 2. Increases the generalisation capabilities of the trained models thus yielding better results in real-world environment. This is a good way to introduce "non-linearity" into the dataset model making it more similar to the real world. We use Mosaic data augmentation as defined in YOLOv4  [37]  which consists in mixing 4 training images to improve the understanding of the spatial context of objects for our method.
3D average precision For evaluation on the KITTI dataset, we used the official benchmark metrics such as the 3D Average Precision (AP) with 40 recall positions as presented by  [39]. The official KITTI benchmark uses an IOU threshold of 0. 7 for the car class and a 0. 5 threshold for the person and cycle classes. We also added evaluation results for the car class with a 0. 5 threshold.
2D detection To evaluate the performance of the 2D detection, we used the metrics described by the authors of YOLOv5 on each class of the dataset. Given that the regression of the 3D bounding box parameters relies on accurate RoI and classes for the objects, evaluating the precision of the 2D detector is a necessity. The error metrics are the Average Precision (AP), the Recall (R), and the mean Average Precision ( mAP 50 ).
Result analysis We present the quantitative results of our models on the KITTI dataset using the official 3D AP metric in Table  1  for the Car class, Table  2  for the Person class, and Table  3  (9)  k=  [1. 3 ] = max( z gt z pd , z pd z gt ) < 𝛿 k. ( 10 ) DS = min( V pd V gt , V gt V pd ). ( 11 ) CS = (2 + cos( x gt -x pd w pd ) + cos( y gt -y pd h pd ))∕4. ( 12 ) OS = (1 + cos( gt -pd ))∕2. 1 3 for the Cycle class. We present our results along with stateof-the-art methods for comparison. These results show that our approach, although less complex than state-of-the-art methods, has accuracy comparable or even superior to them. Our model is also significantly faster than all other tested approaches, with a computation time of 11. 2ms per image on our Large model when inferring on an RTX 3080 GPU.
Our method is even able to achieve higher accuracy using pre-trained weights on our GTAV dataset, which shows the potential of video game datasets to improve 3D object detection methods on real datasets. Thanks to this, our "Large" model can outperform state-of-the-art methods in moderate and difficult scenarios. Finally, we also conducted experiments on a Jetson TX2 embedded board to confirm the applicability of our smallest model for real-time tasks for embedded applications. We present the results in Table  4. Table  1  Quantitative results for the Car class from our evaluation on the KITTI dataset. We can see that our method benefits from being pre-trained on our GTA dataset and manages to outperform other state-of-the-art methods, especially for moderate and hard targets. Our new SA Small model results show that it performs the same, or worse when not using pre-trained weights, than the Small model when training at full resolution ( 1312 × 416 ).
However, when using a smaller resolution ( 672 × 224 ) it significantly outperforms the Small model. It is a particularly interesting model for applications on systems where the computational capabilities are limited since its memory requirements are lower and it runs at 3. 1ms/img while having greater accuracy than the Small and Medium models trained at the same resolution. Our tests with the Jetson TX2 also show that it is the fastest of our methods on this platform. The quantitative results of our models on both datasets for all classes along with the results of our baseline based on our previous multi-stage approach based on YOLOv5 are presented in Table  5  and in Fig. 5. We can see that we achieved significant accuracy gains by switching from a multi-stage approach based on YOLOv3  [40]  from our previous approach  [33]  to an anchor-based approach using YOLOv5.
We conducted the experiments using either the ROIs from YOLOv3 predictions or the ground truth for the feature alignment necessary for the regression of the 3D bounding box parameters. This to highlight the problem posed by ROIs variation in the 3D parameter regression in multi-stage approaches. We show that our new single-stage approach significantly outperforms our previous method in both cases, especially for depth estimation and orientation. We also showcase the various models from our current approach (small, SA small, medium, large) and we can see that the precision between the different models for 2D detection, Dimensions, Center, and Orientation does not significantly benefit from the heavier models while Distance estimation improves when using larger models. Qualitative results on both KITTI and GTA datasets are presented in Fig.
Conclusion and outlook In this paper, we presented our new anchor-based 3D object detection method from single-image input using a modified YOLOv5 network. Along with this method, we also presented our new virtual dataset based on the video game GTAV for both road and rail environments, filling the gap in railway datasets for 3D object detection and localization. Through our work, we showed that state-of-the-art precision on the KITTI benchmark could be reached by adopting transfer learning from models trained on our new dataset while being the fastest method available for 3D object detection from images. Our approach is particularly well suited for embedded use, as demonstrated by our tests on the Nvidia Jetson TX2 embedded board. We also provided    an extensive evaluation of our approach, along with our previous 2 stage network on both the KITTI and our own GTAV road/railway virtual dataset.
These results showed that our method is suitable for railway applications in a virtual environment, yet we still need to prove the applicability of our light-weight network under real conditions. Our fastest model ( Small-SA ) has also an important gap in precision with our largest model (Large). To address these issues, we first plan to acquire our railway dataset with realworld images (as a multimodal dataset provided by 2 cameras, 1 LiDAR, 1 Radar, 1 GPS/IMU) to further validate our approach. To improve the precision of our smallest model (Small-SA) we aim to use knowledge distillation to improve its precision and thus reduce the gap in precision without augmenting the computation time. We also aim to develop a lightweight 3D tracking algorithm and integrate it into our method to provide a complete method for road and rail safety and further improve detection accuracy using Multi-Task Learning (MTL).
The present study investigates stall-induced vibrations on a IEA-15-RWT wind turbine blade. At standstill and under specific strong crosswind conditions, simulations using the codes OpenFAST and DeepLines Wind TM exhibit large-amplitude vibrations. Results of both codes are first compared, showing an overall good agreement. Then, the influence of the wind misalignment angle is assessed as well as the choice of aerodynamic polar datasets. Vibrations are observed within two well-marked angular sectors, and the high sensitivity of the phenomenon to the choice of polars is highlighted. Finally, a simplified model based on a spring-mounted aeroelastic section is used to help identifying which type of unstable aeroelastic modes may be at play.
Note that this simplified configuration is not particularly restrictive considering the physics involved: simulations of the complete turbine showed similar behaviour to that presented in the following  [8]. OpenFAST is an open-source computational framework that couples various modules developed for the simulation of the dynamic, coupled response of (floating) wind turbines. Specifically, we have used the structural model BeamDyn, coupled to the aerodynamics module AeroDyn. In the following, we first describe the modelling of the blade and the aerodynamics within both codes. Then, pure steady and unsteady mechanical calculations, i. e. , without aerodynamics, are performed. Finally, a representative coupled aeroelastic simulations are described and compared to an analytical model. • DeepLines Wind TM : the modelling of all structural elements is based on a geometrically exact beam theory very similar to that proposed by Simo and Vu-Quoc  [10].
Unsteady blade aero-mechanics computations Let us now consider a coupled aero-elastic case. The wind speed is set to 45 m/s, and the misalignment angle γ to -40 •. For this value of γ, the blade is in stalled conditions. The coupled response is computed using DeepLines Wind TM coupled to the BEM model AeroDeeP on one hand, and using BeamDyn coupled to AeroDyn through OpenFAST on the other hand. With DeepLines Wind TM , an automatic time-step strategy is adopted, which results in time-step of the order of 0. 04 s. Because of the current limitations of OpenFAST/BeamDyn dynamic coupling capabilities, a much smaller time-step of 0. 001 s had to be considered. In addition, it is worth noting that many simulations blew out for quite unexpected reasons. A workaround was (i) to vary the spectral order of the elements used within BeamDyn (BeamDyn key order_elem varied between 3 and 10)this was not, however, a very satisfactory method, since it may influence the results -and (ii) to increase the number of sub-iterations between OpenFAST modules (NumCrctn key).
Their exact expression is obtained from the same look-up table as those used in the previous calculations. The application of Lagrange's equations gives an evolution equation that can be further linearized to study the small-amplitude vibrations about the position q = 0. Vibration modes q ′ (t) = q exp (λ + 2πf )t can be then obtained by solving an eigenvalue problem of type (λ + 2πf ) 2 M s + (λ + 2πf ) C s -C a + K s -K a q = 0 (1) where M s , K s and C s are the structural Jacobian matrices, and C a and K a are aerodynamic added-damping and added-stiffness matrices. The explicit expressions of these matrices are quite cumbersome in the general case, for that reason they were determined using the symbolic differentiation capabilities of the Python library Sympy. There is a priori no direct link between this "section-wise" model and the behaviour of the complete IEA-15 blade.
Conclusion An isolated IEA-15-RWT blade subject to extreme wind conditions has been studied in this work, in configurations where severe stall flutter vibrations can occur. We have first performed code-to-code comparisons between DeepLines Wind T M and OpenFAST (BeamDyn/AeroDyn), showing that both codes are in good agreement. Nevertheless, it is observed that the coupling strategy implemented at this stage within OpenFAST has some limitations in terms of time-step (convergence required time-steps 40 times smaller compared to DeepLines Wind TM ) and/or overall convergence. We hope that the comparisons presented here may help for the development of improved versions of OpenFAST. We have also shown how sensitive is the stall flutter phenomenon with respect to the polar data used in the calculations, and proposed a link between the behaviour of the full three-dimensional blade configuration and "local" vibration modes of equivalent spring-mounted airfoil sections distributed along the span.
For this reason, experts were selected from those directly involved in the MPS process of our industrial partner. Regarding sample size, Loo  [26]  suggested the selection of 15-30 experts for a heterogeneous population and 5-10 for homogeneous populations. Considering that the experts were part of the same company, they could be listed as belonging to the same population. Then, to model the planning process, we employed the BPMN 2. 0 (business process modeling notation). This version was proposed in 2011 by the Object Management Group (OMG). We worked with the Lucid Chart software. One of the advantages of this methodology is that the notation is easy to understand. The different parties (technical, developers, and business) and the modeler used a common modeling standard  [27]. As mentioned by Geiger et al.
This update was carried out manually in the ERP at the level of each order. By uploading the MPS into the ERP, this type of task could be automated. Regarding the technologies and systems used in MPS activities, we noticed that most of the calculation activities were performed using custom Excel files. There was currently no Industry 4. 0 technology employed in the planning process. According to the scale proposed by  [1] , the current MPS process was at the integrated level because an ERP supported it. However, the other IT tools used were not integrated with the ERP. We also observed a lack of communication between the different participants. The master planner was at the core of all exchanges. Most information was transmitted orally or through emails without being recorded in a database or reports.
Defining General MPS Parameters During the workshop with the experts, we identified six activities of this subprocess that could benefit from Industry 4. 0 technologies. Figure  5  presents the BPMN model for the definition of the general MPS parameters in Tn. For the identification of critical resources (activity 1. 1-Tn), the experts proposed to use simulations to generate multiple production flow scenarios. The simulation would enable the impacts of disruptions to the workshop to be evaluated, such as machine breakdowns, employee absenteeism, and the repositioning of employees in different workstations. Workflow data could be retrieved through the IoT or CPS. Then, production flow verification could be carried out in nearly real-time conditions (activity 1. 18-Tn). Access to these data would allow for evaluating production scenarios as soon as disruptions were detected, identifying if new bottlenecks appeared, and integrating the control, planning, and execution levels.
8-Tn). The experts clarified that even if activities 1. 8 and 1. 9 of Tn used real-time production flow data, this did not imply that the macrorouting or the macro-BOM were updated in real time. The company must define the frequency at which these parameters were to be updated. For the identification of critical resources (activity 1. 1-Tn), the experts proposed to use simulations to generate multiple production flow scenarios. The simulation would enable the impacts of disruptions to the workshop to be evaluated, such as machine breakdowns, employee absenteeism, and the repositioning of employees in different workstations. Workflow data could be retrieved through the IoT or CPS. Then, production flow verification could be carried out in nearly real-time conditions (activity 1. 18-Tn). Access The association or updating of macrorouting and macro-BOM to master items could become an automated activity using machine learning (activity 1.
Conducting MPS Meeting Figure  8  presents the Tn version of this subprocess. The experts proposed to use machine learning to identify risks and problems related to the MPS (activity 4. 9-Tn) and to define the actions to be launched (activity 4. 10-Tn). A database of problems' causes associated with activity 4. 9 would allow the algorithm to identify the problem related to the proposed MPS. This database could be fed with workflow data (activity 4. 13-Tn) and with component availability (activity 4. 14-Tn) collected in real time through the IoT or CPS. Similarly, activity 4. 10 would require a database of prior solutions that would allow the algorithm to propose a solution to the problem or risk detected in the previous activity. This database could be updated with solutions proposed by the participants during the meeting.
Adjusting MPS after MRP Most of the activities in this subprocess were associated with Industry 4. 0 technologies. The model for this subprocess is presented in Figure  9. The computation and validation of loads at all load stations (activities 5. 1 and 5. 2-Tn) could use simulations to study different load-capacity ratio scenarios. Fuzzy logic and/or heuristic algorithms could be used to select the best scenario. Real-time maintenance and workflow data are essential for the detailed load analysis. The experts proposed to use machine learning to identify risks and problems related to the MPS (activity 4. 9-Tn) and to define the actions to be launched (activity 4. 10-Tn). A database of problems' causes associated with activity 4. 9 would allow the algorithm to identify the problem related to the proposed MPS.
This database could be fed with workflow data (activity 4. 13-Tn) and with component availability (activity 4. 14-Tn) collected in real time through the IoT or CPS. Similarly, activity 4. 10 would require a database of prior solutions that would allow the algorithm to propose a solution to the problem or risk detected in the previous activity. This database could be updated with solutions proposed by the participants during the meeting. The computation and validation of loads at all load stations (activities 5. 1 and 5. 2-Tn) could use simulations to study different load-capacity ratio scenarios. Fuzzy logic and/or heuristic algorithms could be used to select the best scenario. Real-time maintenance and workflow data are essential for the detailed load analysis. If the detailed load analysis was to not meet the company's objectives, machine learning could be used to propose solutions (activity 5.
3-Tn). This algorithm requires a historical record of load-capacity conditions and a database of solutions for overload conditions. With these inputs, the algorithm could propose an adaptable solution to deal with the load-capacity balance situation. Once the solutions are proposed, a simulation could be employed to evaluate the impacts of their implementation in the production process. For the scheduling (subprocess 7-Tn), digital twin simulations with optimization approaches were proposed by the experts. The use of the IoT or CPS for workflow management (activity 5. 6) would enable the collection of data of each resource, the cycle times of each workstation, the idle time, and the production shutdowns. If the detailed load analysis was to not meet the company's objectives, machine learning could be used to propose solutions (activity 5.
3-Tn). This algorithm requires a historical record of load-capacity conditions and a database of solutions for overload conditions. With these inputs, the algorithm could propose an adaptable solution to deal with the load-capacity balance situation. Once the solutions are proposed, a simulation could be employed to evaluate the impacts of their implementation in the production process. For the scheduling (subprocess 7-Tn), digital twin simulations with optimization approaches were proposed by the experts. The use of the IoT or CPS for workflow management (activity 5. 6) would enable the collection of data of each resource, the cycle times of each workstation, the idle time, and the production shutdowns. The experts proposed to use the IoT or CPS to monitor the positions of products in real time (activity 5.
Big Data and AI Pipeline patterns provide a good foundation for the analysis and selection of technical architectures for Big Data and AI systems. Experiences from many projects in the Big Data PPP program has shown that a number of projects use similar architectural patterns with variations only in the choice of various technology components in the same pattern. The project DataBench has developed a Big Data and AI Pipeline Framework, which is used for the description of pipeline steps in Big Data and AI projects, and supports the classification of benchmarks. This includes the four pipeline steps of Data Acquisition/Collection and Storage, Data Preparation and Curation, Data Analytics with AI/Machine Learning, and Action and Interaction, including Data Visualization and User Interaction as well as API Access.
Introduction Organizations rely on evidence from the benchmarking domain to provide answers to how their processes are performing. There is extensive information on why and how to perform technical benchmarks for the specific management and analytics processes, but there is a lack of objective, evidence-based methods to measure the correlation between Big Data Technology (BDT) benchmarks and business benchmarks of an organization and demonstrate return on investment. When more than one benchmarking tool exist for a given need, there is even less evidence as to how these tools compare to each other, and how the results can affect their business objectives. The DataBench project has addressed this gap by designing a framework to help European organizations developing BDT to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance.