Monitoring technology

A process for infrared spectroscopic monitoring of insitu compositional changes in a polymeric material comprises the steps of providing an elongated infrared radiation transmitting fiber that has a transmission portion and a sensor portion, embedding the sensor portion in the polymeric material to be monitored, subjecting the polymeric material to a processing sequence, applying a beam of infrared radiation to the fiber for transmission through the transmitting portion to the sensor portion for modification as a function of properties of the polymeric material, monitoring the modified infrared radiation spectra as the polymeric material is being subjected to the processing sequence to obtain kinetic data on changes in the polymeric material during the processing sequence, and adjusting the processing sequence as a function of the kinetic data provided by the modified infrared radiation spectra information

Monitoring technology

A process for infrared spectroscopic monitoring of insitu compositional changes in a polymeric material comprises the steps of providing an elongated infrared radiation transmitting fiber that has a transmission portion and a sensor portion, embedding the sensor portion in the polymeric material to be monitored, subjecting the polymeric material to a processing sequence, applying a beam of infrared radiation to the fiber for transmission through the transmitting portion to the sensor portion for modification as a function of properties of the polymeric material, monitoring the modified infrared radiation spectra as the polymeric material is being subjected to the processing sequence to obtain kinetic data on changes in the polymeric material during the processing sequence, and adjusting the processing sequence as a function of the kinetic data provided by the modified infrared radiation spectra information

Data processing system for the Sneg-2MP experiment

The data processing system for scientific experiments on stations of the "Prognoz" type provides for the processing sequence to be broken down into a number of consecutive stages: preliminary processing, primary processing, secondary processing. The tasks of each data processing stage are examined for an experiment designed to study gamma flashes of galactic origin and solar flares lasting from several minutes to seconds in the 20 kev to 1000 kev energy range

Single stream parallelization of generalized LSTM-like RNNs on a GPU

Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data. However, they suffer from long training time, which demands parallel implementations of the training procedure. Parallelization of the training algorithms for RNNs are very challenging because internal recurrent paths form dependencies between two different time frames. In this paper, we first propose a generalized graph-based RNN structure that covers the most popular long short-term memory (LSTM) network. Then, we present a parallelization approach that automatically explores parallelisms of arbitrary RNNs by analyzing the graph structure. The experimental results show that the proposed approach shows great speed-up even with a single training stream, and further accelerates the training when combined with multiple parallel training streams.Comment: Accepted by the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 201

Fingermark Detection on Thermal Papers: Proposition of an Updated Processing Sequence

The detection of latent fingermarks on thermal papers proves to be particularly challenging because the application of conventional detection techniques may turn the sample dark grey or black, thus preventing the observation of fingermarks. Various approaches aiming at avoiding or solving this problem have been suggested. However, in view of the many propositions available in the literature, it gets difficult to choose the most advantageous method and to decide which processing sequence should be followed when dealing with a thermal paper. In this study, 19 detection techniques adapted to the processing of thermal papers were assessed individually and then were compared to each other. An updated processing sequence, assessed through a pseudo-operational test, is suggested

Simple Processing Sequence to VSP-Seismic data matching in Sindbad oil field, south of Iraq

The purpose of this research is to get the batter matching between VSP and seismic data after preforming a simple processing sequence on Zero-offset VSP survey. Sindbad oil field is chosen to study goals and it\u27s containing only one well with VSP survey (Snd2) that covering depth from Zubair to Sulaiy Formations and 2D seismic lines of Basrah Survey (2Br & 5Br). In order to get the mentioned information from VSP the main steps of processing sequence (Velocity calculation, Amplitude recovery, wave separation, deconvolution and stacking) has been used to measure (RMS and AVG) velocity and make the corridor stack image of VSP p-waves in Promax landmark software. The principle of VSP and seismic data matching is depended on phase and time shift. The matching filter in Omega software depends on frequency content, phase, locations of the two data and amplitude difference which gives us batter correlation for matching. The final test of these filters shows good matching between Snd-2 VSP and 2Br2 Seismic line

Code Prediction by Feeding Trees to Transformers

We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems. First, we report that using the recently proposed Transformer architecture even out-of-the-box outperforms previous neural and non-neural systems for code prediction. We then show that by making the Transformer architecture aware of the syntactic structure of code, we further increase the margin by which a Transformer-based system outperforms previous systems. With this, it outperforms the accuracy of an RNN-based system (similar to Hellendoorn et al. 2018) by 18.3\%, the Deep3 system (Raychev et al 2016) by 14.1\%, and an adaptation of Code2Seq (Alon et al., 2018) for code prediction by 14.4\%. We present in the paper several ways of communicating the code structure to the Transformer, which is fundamentally built for processing sequence data. We provide a comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Facebook internal Python corpus. Our code and data preparation pipeline will be available in open source