Anomaly Prediction with Temporal Convolutional Networks for HPC Systems

Per far fronte a esigenze computazionali elevate, necessarie per la risoluzione di problemi complessi, la scienza e le industrie fanno spesso uso di sistemi di calcolo di enormi dimensioni e potenza. I sistemi HPC (High Performance Computing) sono identificabili come un insieme di tanti computer cooperanti e connessi tra loro, chiamati singolarmente “nodi”. I costi da sostenere per l’acquisto o la costruzione di questi sistemi ammontano a svariate decine di milioni di euro. Per questo motivo viene spesso affittata la potenza di calcolo di questi sistemi in modalità on-demand, grazie alla tecnologia del Cloud Computing. In queste circostanze risulta necessario garantire quella che viene definita “qualità del servizio”(QoS) , la quale comprende la disponibilità del sistema (availability). Questo significa che un cluster HPC non deve mai (o quasi) essere inaccessibile a chi ha pagato per usufruirne. L’oggetto di questa tesi di laurea magistrale nasce da questa necessità e si propone di trovare una soluzione costruendo un modello, basato sull’utilizzo di tecniche facenti riferimento all’Intelligenza Artificiale (ed in particolare al Machine Learning), che sia in grado di prevedere in anticipo il sorgere di nuove anomalie nei nodi del sistema, di modo che un amministratore possa intervenire tempestivamente, evitando cosı̀ che questo debba essere reso inaccessibile all’utenza. Il sistema HPC su cui è stato testato il modello è di proprietà di CINECA, un consorzio universitario formato da 67 università italiane e 13 istituzioni, prende il nome di MARCONI e, grazie alla sua potenza di calcolo di 20 Pflops/s, si posiziona al 19esimo posto su scala mondiale in termini di prestazioni

Acoustic impedance inversion in coal strata using the priori constraint-based TCN-BiGRU method

Acoustic impedance inversion is a key technique for the seismic exploration of coalfield, which can determine subsurface lithological changes and coal seam distribution. The traditional method is highly subjective, has poor generalizability, and interpretation can be time and labor consuming. Due to the powerful nonlinear interpretation and feature extraction capabilities of neural networks, deep learning technology has demonstrated potential for geophysical exploration. To predict acoustic impedance accurately and efficiently, this study proposes the use of the initial geological model as the priori constraint for training. The low-frequency feature extraction capability of a bidirectional gated recurrent unit network and the high-frequency feature extraction capability of a temporal convolutional network are used to establish a new acoustic impedance inversion method in coal strata with a priori constraint data. The temporal convolutional network-bidirectional gated recurrent unit method was applied to data from the Xinjing Mining Area in Shanxi province, northern China. The results displayed good precision by accurately predicting the distribution and thickness variation of local coal seams. Compared with the traditional model-based method and the method using temporal convolutional network-bidirectional gated recurrent unit network, the proposed priori constraint-based temporal convolutional network-bidirectional gated recurrent unit network has better feature expression capability and provides more detailed coal seam information. In conclusion, the new method can improve the accuracy of acoustic impedance inversion, which is of great significance for coalfield seismic exploration.Document Type: Original articleCited as: Shi, S., Qi, Y., Chang, W., Li, L., Yao, X., Shi, J. Acoustic impedance inversion in coal strata using the priori constraint-based TCN-BiGRU method. Advances in Geo-Energy Research, 2023, 9(1): 13-24. https://doi.org/10.46690/ager.2023.07.0

Adaptive Spatio-Temporal Convolutional Network for Traffic Prediction

Traffic prediction is a crucial task in many real-world applications. The task is challenging due to the implicit and dynamic spatio-temporal dependencies among traffic data. On the one hand, the spatial dependencies among traffic flows are latent and fluctuate with environmental conditions. On the other hand, the temporal dependencies among traffic flows also vary significantly over time and locations. In this paper, we propose Adaptive Spatio-Temporal Convolutional Network (ASTCN) to tackle these challenges. First, we propose a spatial graph learning module that learns the dynamic spatial relations among traffic data based on multiple influential factors. Furthermore, we design an adaptive temporal convolution module that captures complex temporal traffic dependencies with environment-aware dynamic filters. We conduct extensive experiments on three real-world traffic datasets. The results demonstrate that the proposed ASTCN consistently outperforms state-of-the-arts.Peer reviewe

A temporal Convolutional Network for EMG compressed sensing reconstruction

Electromyography (EMG) plays a vital role in detecting medical abnormalities and analyzing the biomechanics of human or animal movements. However, long-term EMG signal monitoring will increase the bandwidth requirements and transmission system burden. Compressed sensing (CS) is attractive for resource-limited EMG signal monitoring. However, traditional CS reconstruction algorithms require prior knowledge of the signal, and the reconstruction process is inefficient. To solve this problem, this paper proposed a reconstruction algorithm based on deep learning, which combines the Temporal Convolutional Network (TCN) and the fully connected layer to learn the mapping relationship between the compressed measurement value and the original signal, and it has been verified in the Ninapro database. The results show that, for the same subject, compared with the traditional reconstruction algorithms orthogonal matching pursuit (OMP), basis pursuit (BP), and Modified Compressive Sampling Matching Pursuit (MCo), the reconstruction quality and efficiency of the proposed method is significantly improved under various compression ratios (CR)

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc