Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state-of-the-art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for modeling of voltage time series of the magnets. In order to address this challenging task different network architectures and hyper-parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.Comment: Related to arXiv:1702.0083

Effect of selected factors on the serum 25(OH)D concentration in women treated for breast cancer

Maintaining an optimal vitamin D concentration reduces the risk of recurrence and extends survival time in patients after breast cancer treatment. Data on vitamin D deficiency among Polish women after breast cancer therapy are limited. Thus, the aim of the study was the analysis of vitamin D status in post-mastectomy patients, considering such factors as seasons, social habits, vitamin D supplementation and its measurements. The study involved 94 women after breast cancer treatment. Serum vitamin D concentration was measured, and a questionnaire, gathering demographic and clinical data regarding cancer, diet, exposure to sun radiation, and knowledge of recommendations on vitamin D supplementation, was delivered twice, in both winter and in summer. The control group consisted of 94 age-matched women with no oncological history. In women after breast cancer treatment, 25-hydroxyvitamin D (25(OH)D) deficiency was much more frequent than in the general population. Only about half of the patients supplemented vitamin D at the beginning of the study. After the first test and the issuing of recommendations on vitamin D supplementation, the percentage of vitamin D supplemented patients increased by about 30% in study groups. The average dose of supplement also increased. None of the women that were not supplementing vitamin D and were tested again in winter had optimal 25(OH)D concentration. It was concluded that vitamin deficiency is common in women treated for breast cancer. Medical advising about vitamin D supplementation and monitoring of 25(OH)D concentration should be improved

Using Neural Networks with data Quantization for time Series Analysis in LHC Superconducting Magnets

The aim of this paper is to present a model based on the recurrent neural network (RNN) architecture, the long short-term memory (LSTM) in particular, for modeling the work parameters of Large Hadron Collider (LHC) super-conducting magnets. High-resolution data available in the post mortem database were used to train a set of models and compare their performance for various hyper-parameters such as input data quantization and the number of cells. A novel approach to signal level quantization allowed reducing the size of the model, simplifying the tuning of the magnet monitoring system and making the process scalable. The paper shows that an RNN such as the LSTM or a gated recurrent unit (GRU) can be used for modeling high-resolution signals with the accuracy of over 0.95 and a small number of parameters, ranging from 800 to 1200. This makes the solution suitable for hardware implementation, which is essential in the case of monitoring the performance critical and high-speed signal of LHC superconducting magnets

Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high resolution data available in Post Mortem database to train a set of models and chose the best possible set of their hyper-parameters. Using Deep Learning approach allowed to examine a vast body of data and extract the fragments which require further experts examination and are regarded as anomalies. The presented method does not require tedious manual threshold setting and operator attention at the stage of the system setup. Instead, the automatic approach is proposed, which achieves according to our experiments accuracy of 99%. This is reached for the largest dataset of 302 MB and the following architecture of the network: single layer LSTM, 128 cells, 20 epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam. All the experiments were run on GPU Nvidia Tesla K8

Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor

Sensing the voltage developed over a superconducting object is very important in order to make superconducting installation safe. An increase in the resistive part of this voltage (quench) can lead to significant deterioration or even to the destruction of the superconducting device. Therefore, detection of anomalies in time series of this voltage is mandatory for reliable operation of superconducting machines. The largest superconducting installation in the world is the main subsystem of the Large Hadron Collider (LHC) accelerator. Therefore a protection system was built around superconducting magnets. Currently, the solutions used in protection equipment at the LHC are based on a set of hand-crafted custom rules. They were proved to work effectively in a range of applications such as quench detection. However, these approaches lack scalability and require laborious manual adjustment of working parameters. The presented work explores the possibility of using the embedded Recurrent Neural Network as a part of a protection device. Such an approach can scale with the number of devices and signals in the system, and potentially can be automatically configured to given superconducting magnet working conditions and available data. In the course of the experiments, it was shown that the model using Gated Recurrent Units (GRU) comprising of two layers with 64 and 32 cells achieves 0.93 accuracy for anomaly/non-anomaly classification, when employing custom data compression scheme. Furthermore, the compression of proposed module was tested, and showed that the memory footprint can be reduced four times with almost no performance loss, making it suitable for hardware implementation

Prebiotics, probiotics, synbiotics, paraprobiotics and postbiotic compounds in IBD

The increasing incidence of inflammatory bowel diseases (IBD) and the increasing severity of the course of these diseases create the need for developing new methods of therapy. The gut microbiome is extensively studied as a factor influencing the development and course of IBD. The composition of intestinal microbiota can be relatively easily modified by diet (i.e., prebiotics, mainly dietary fibers) and bacterial supplementation using beneficial bacteria strains called probiotics. Additionally, the effects of the improved microbiome could be enhanced or gained by using paraprobiotics (non-viable, inactivated bacteria or their components) and/or postbiotics (products of bacterial metabolism or equal synthetic products that beneficially modulate immunological response and inflammation). This study summarizes the recent works on prebiotics, probiotics, synbiotics (products merging pre- and probiotics), paraprobiotics and postbiotics in IBD

The upgraded quench protection system for main quadrupoles in the LHC

The protection of superconducting magnets is a very important issue and demanding challenge in the LHC and other superconducting accelerating facilities. The quench phenomenon can destroy components of the accelerator, and therefore this digital system was designed, implemented, tested, and installed near each superconducting magnet in the LHC tunnel. The quench detection principle relies on the extraction of resistive voltage by compensation of the inductive part of the voltage. This article presents briefly the architecture applied to the design and the validation of the FPGA-based quench detector for the main quadrupoles of the LHC. The article focusses on digital design with the use of FPGA by VHDL coding and on the verification by simulation. The design is a replacement for the old detection system

Prebiotics, Probiotics, and Postbiotics in the Prevention and Treatment of Anemia

Iron deficiency anemia (IDA) is very common and affects approximately 1/3 of the world’s human population. There are strong research data that some probiotics, such as Lactobacillus acidophilus and Bifidobacterium longum improve iron absorption and influence the course of anemia. Furthermore, prebiotics, including galactooligosaccharides (GOS) and fructooligosaccharides (FOS), increase iron bioavailability and decrease its destructive effect on the intestinal microbiota. In addition, multiple postbiotics, which are probiotic metabolites, including vitamins, short-chain fatty acids (SCFA), and tryptophan, are involved in the regulation of intestinal absorption and may influence iron status in humans. This review presents the actual data from research studies on the influence of probiotics, prebiotics, and postbiotics on the prevention and therapy of IDA and the latest findings regarding their mechanisms of action. A comparison of the latest research data and theories regarding the role of pre-, post-, and probiotics and the mechanism of their action in anemias is also presented and discussed