Temperature Field Online Reconstruction for In-Service Concrete Arch Dam Based on Limited Temperature Observation Data Using AdaBoost-ANN Algorithm

Temperature is one of the factors affecting the safety operation of concrete arch dams. To accurately reconstruct the temperature field of the concrete arch dam online based on the temperature data of several typical dam sections, this paper proposes the AdaBoost-ANN algorithm. The algorithm uses artificial neural network (ANN) to establish a training set of the measured temperature data and the temperature field of the concrete arch dam obtained by the three-dimensional finite element model; these trained artificial neural networks are used as weak classifiers of the AdaBoost algorithm. Then, the AdaBoost-ANN algorithm is used to establish the mapping relationship between the measured temperature data and the temperature field, and the online reconstruction of the temperature field of the concrete arch dam is realized. The case study shows that the temperature field of the concrete arch dam can be accurately established by AdaBoost-ANN algorithm based on limited temperature observation data. The algorithm is more time-saving and labor-saving than the finite element method and is convenient for online reconstruction of the temperature field and assessment of the safety status of the concrete arch dam.

LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction

Deep learning approaches for tomographic image reconstruction have become very effective and have been demonstrated to be competitive in the field. Comparing these approaches is a challenging task as they rely to a great extent on the data and setup used for training. With the Low-Dose Parallel Beam (LoDoPaB)-CT dataset, we provide a comprehensive, open-access database of computed tomography images and simulated low photon count measurements. It is suitable for training and comparing deep learning methods as well as classical reconstruction approaches. The dataset contains over 40000 scan slices from around 800 patients selected from the LIDC/IDRI database. The data selection and simulation setup are described in detail, and the generating script is publicly accessible. In addition, we provide a Python library for simplified access to the dataset and an online reconstruction challenge. Furthermore, the dataset can also be used for transfer learning as well as sparse and limited-angle reconstruction scenarios.

Software for online reconstruction and filtering at the Belle II experiment

The Belle II experiment is designed to collect 50 times more data than its predecessor. For a smooth collection of high-quality data, a robust and automated data transport and processing pipeline has been established. We describe the basic software components employed by the high level trigger. It performs a reconstruction of all events using the same algorithms as offline, classifies the events according to physics criteria, and provides monitoring information. The improved system described in this paper has been deployed successfully since 2019.

GPU-based reconstruction and data compression at ALICE during LHC Run 3

In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous read out of minimum bias Pb-Pb collisions. The reconstruction strategy of the online offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration, and a posterior calibrated asynchronous reconstruction stage. The significant increase in the data rate poses challenges for online and offline reconstruction as well as for data compression. Compared to Run 2, the online farm must process 50 times more events per second and achieve a higher data compression factor. ALICE will rely on GPUs to perform real time processing and data compression of the Time Projection Chamber (TPC) detector in real time, the biggest contributor to the data rate. With GPUs available in the online farm, we are evaluating their usage also for the full tracking chain during the asynchronous reconstruction for the silicon Inner Tracking System (ITS) and Transition Radiation Detector (TRD). The software is written in a generic way, such that it can also run on processors on the WLCG with the same reconstruction output. We give an overview of the status and the current performance of the reconstruction and the data compression implementations on the GPU for the TPC and for the global reconstruction.