Lifetime measurements of lowest states in the π g7/2 ⊗ νh11/2 rotational band in 112I

A differential-plunger device was used to measure the lifetimes of the lowest states in the πg7/2 ⊗ νh11/2 rotational band in doubly odd 112I with the 58Ni(58Ni, 3pn) reaction. A differential decay curve method was performed using the fully shifted and degraded γ -ray intensity measurements as a function of target-to-degrader distance. The lifetimes of the lowest three states in the πg7/2 ⊗ νh11/2 band in 112I were measured to be 124(30), 130(25), and 6.5(5) ps, respectively. As the lifetimes of successive excited states in a rotational band are expected to decrease with increasing excitation energy, these measurements suggest that the order of the transitions in the established band in 112…

Anonymization as homeomorphic data space transformation for privacy-preserving deep learning

Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation),…

Semantic publishing as a tool in smart, self-protective critical infrastructure

Semantic publishing as a semantic representation of publications makes articles machine-readable. It involves linking an article with another articles and external sources. Also, data provided along with the publication can be made machine-readable, which allows visualizing the data in different ways according to a reader’s needs. This kind of semantic technology deployed on physics domain can serve for improving interoperability in critical infrastructure as far as it includes such facilities as atomic power stations, atomic plants and local experimental atomic devices, experimental fusion reactors and demonstration fusion reactors among others. Having shared terminology and data represent…

Lifetime measurements of lowest states in the πg7/2⊗νh11/2 rotational band in I112

The detection efficiency of the clover Ge-detectors array at the RITU-GREAT facility

Absolute detection efficiency depending upon its position along the beam line is determined for three germanium detectors, that are part of the GREAT spectrometer at the RITU separator focal plane. The efficiency is determined for an energy range from 80 keV to 1400 keV for all three detectors as an array and also individually using digital electronics. The optimal position for the detector array has been defined for two configurations of the GREAT spectrometer: with planar detector and without it. The obtained results have been compared with three references: a simulation prepared especially for this setup by another student, a previous study that was carried out by applying analogue elect…

Cloning and training collective intelligence with generative adversarial networks

Industry 4.0 and highly automated critical infrastructure can be seen as cyber‐physical‐social systems controlled by the Collective Intelligence. Such systems are essential for the functioning of the society and economy. On one hand, they have flexible infrastructure of heterogeneous systems and assets. On the other hand, they are social systems, which include collaborating humans and artificial decision makers. Such (human plus machine) resources must be pre‐trained to perform their mission with high efficiency. Both human and machine learning approaches must be bridged to enable such training. The importance of these systems requires the anticipation of the potential and previously unknow…

Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…