New fusion research experiments will generate massive experimental data. In this context, fusion research appears in the scope of the big data where both search and access functions require new ...approaches and optimizations.
One common data access functionality is decimation. It allows to retrieve a limited number of points of the total available. One classical mechanism of decimation is downsampling by an integer factor called step. It works by selecting a value every ‘step’ number of values. The main characteristic of classical decimation is that the selected values are uniformly distributed along the total. However, in case of time evolution experimental signals, the relevancy of data is not uniform. There are some intervals where the provided information is more complex and richer, and usually more interesting from the user point of view.
This contribution presents a new data decimation technology for unidimensional time evolution signals where the limited number of accessed points are distributed following the criterion of data interest level. The new method implements, on one hand, a heuristic function which is able to determine the level of interest of an interval based on its data characteristics, and on the other hand, a selection algorithm where points are distributed based on weighted intervals.
In this paper, we present a new method, called Spectral Global Silhouette method (GS), to calculate the optimal number of clusters in a dataset using a Spectral Clustering algorithm. It combines both ...a Silhouette Validity Index and the concept of Local Scaling. First, the GS algorithm has first been tested using synthetic data. Then, it is applied on real data for image segmentation task. In addition, three new methods for image segmentation and two new ways to calculate the optimal number of groups in an image are proposed. Our experiments have shown a promising performance of the proposed algorithms.
•A new approach to find the optimal number of clusters is presented.•This approach is used for the Spectral Clustering algorithm.•The method is tested using synthetic data and images.•Two ways to calculate the optimal number of groups in an image are presented.•Three methods for image segmentation are proposed.
This article proposes an enhancement of a singular spectrum analysis (SSA) technique based on Hilbert matrix (SSA-H) for autonomous navigation systems in a harsh environment. SSA is a technique that ...has caught the attention of different fields and is used to extract trends and patterns in the time series domain. In this work, SSA-H is used to transform raw optical signals into meaningful information to build machine learning (ML) models. A technical vision system (TVS) with laser scanning for depth measurements is presented. According to the implementation of the TVS outdoors, some particular issues, such as interference radiation detected by its photodiode, can affect the system's performance in determining depth measurements. This research extracts the laser beam patterns in a real environment to create ML models to solve the problem and address some of the technical challenges in a real environment. The main contribution of this work is designing an ML framework for recognizing the laser beam of the TVS based on SSA-H. Furthermore, a comparative analysis of ML techniques to discriminate sunlight interference was studied and compared with different configurations of SSA known in the literature. According to the results, the use of SSA-H can enhance the ML models such as ensemble learning (EL), feed-forward neural networks (FNNs), support vector machines (SVMs), Naive Bayes (NB) in conjunction with autocorrelation function coefficients (ACCs) as features.
This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of ...radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as ‘recognition of degradation patterns in the database’ and ‘degradation prediction of new samples without any kind of irradiation’. The technique can be used under two different approaches called ‘pure data driven’ and ‘model based’. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component.
Abstract
We present a catalog of about 25,000 images of massive (
M
⋆
≥ 10
9
M
⊙
) galaxies at redshifts 3 ≤
z
≤ 6 from the TNG50 cosmological simulation, tailored for observations at multiple ...wavelengths carried out with JWST. The synthetic images were created with the SKIRT radiative transfer code, including the effects of dust attenuation and scattering. The noiseless images were processed with the
mirage
simulator to mimic the Near Infrared Camera (NIRCam) observational strategy (e.g., noise, dithering pattern, etc.) of the Cosmic Evolution Early Release Science (CEERS) survey. In this paper, we analyse the predictions of the TNG50 simulation for the size evolution of galaxies at 3 ≤
z
≤ 6 and the expectations for CEERS to probe that evolution. In particular, we investigate how sizes depend on the wavelength, redshift, mass, and angular resolution of the images. We find that the effective radius accurately describes the three-dimensional half-mass–radius of the TNG50 galaxies. Sizes observed at 2
μ
m are consistent with those measured at 3.56
μ
m at all redshifts and masses. At all masses, the population of higher-
z
galaxies is more compact than their lower-
z
counterparts. However, the intrinsic sizes are smaller than the mock observed sizes for the most massive galaxies, especially at
z
≲ 4. This discrepancy between the mass and light distributions may point to a transition in the galaxy morphology at
z
= 4–5, where massive compact systems start to develop more extended stellar structures.
22
22
Data publicly released at
https://www.tng-project.org/costantin22
.
This article presents a 3-D anthropometric vision system, free of ionizing radiation for measuring the medial longitudinal arch (MLA) and longitudinal arch angle (LAA) of the foot, essential for ...evaluating foot arch disorders that can affect a person's lifestyle. Existing 2-D techniques for determining MLA based on anthropometric tests have limitations because they exclude important information about foot such as pronation, dorsiflexion, and rotation. To overcome these limitations, the proposed system uses stereo vision and a landmark detection algorithm to measure the 3-D MLA and LAA, considering the <inline-formula> <tex-math notation="LaTeX">z </tex-math></inline-formula>-axis perpendicular to the sagittal plane. The proposed system is compared with a commercial LIDAR using a tridimensional grid (ground truth), obtaining a root mean square error (RMSE) of 1.2° for the proposed system and an RMSE of 4.8° for LIDAR. The proposed system offers several advantages over traditional techniques, including low error, 3-D measurement capabilities, and free of ionizing radiation. In addition, the system's application in human subjects showed a variability range between 3-D and 2-D measurements for MLA, which is consistent with previous research. Overall, the proposed system has the potential to improve the evaluation and treatment of foot arch disorders, thereby enhancing a person's quality of life.
The present paper describes the experimentation in a controlled environment and a real environment using different photosensors, such as infrared light emitting diode (IRLED-as receiver), photodiode, ...light dependent resistor (LDR), and blue LED for the purpose of selecting those devices, which can be employed in adverse conditions, such as sunlight or artificial sources. The experiments that are described in this paper confirmed that the blue LED and phototransistor could be used as a photosensor of an Optical Scanning System (OSS), because they were less sensitive to sunlight radiation. Moreover, they are appropriate as reference sources that are selected for the experiment (blue LED flashlight and light bulb). The best experimental results that were obtained contained a digital filter that was applied to the output of the photosensor, which reduced the standard deviation for the best case for the phototransistor LED from 100.26 to 0.15. For the best case, using the blue LED, the standard deviation was reduced from 86.08 to 0.11. Using these types of devices the cost of the Optical Scanning System can be reduced and a considerable increase in resolution and accuracy.
Nuclear fusion is a potential source of energy that could supply the growing needs of the world population for millions of years. Several experimental thermonuclear fusion devices try to understand ...and control the nuclear fusion process. A very interesting diagnostic called Thomson scattering (TS) is performed in the Spanish fusion device TJ-II. This diagnostic takes images to measure the temperature and density profiles of the plasma, which is heated to very high temperatures to produce fusion plasma. Each image captures spectra of laser light scattered by the plasma under different conditions. Unfortunately, some images are corrupted by noise called stray light that affects the measurement of the profiles. In this work, we propose the use of deep learning models to reduce the stray light that appears in the diagnostic. The proposed approach utilizes a Pix2Pix neural network, which is an image-to-image translation based on a generative adversarial network (GAN). This network learns to translateimages affected by stray light to images without stray light. This allows for the effective removal of the noise that affects the measurements of the TS diagnostic, avoiding the need for manual image processing adjustments. The proposed method shows a better performance, reducing the noise up to 98% inimages, which surpassesprevious works that obtained 85% for the validation dataset.
•JET real-time disruption predictor with metallic wall 991 discharges analyzed.•Predictor training has been carried out with JET C wall data.•Success, false alarm and missed alarm rates are 98.4%, ...0.9% and 1.6%, respectively.•Alarms are triggered in average 426ms before the disruption.
The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 real-time signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 32ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%.
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