Machine Learning approaches for Anomaly Detection in Industrial IoT scenarios

openMachine learning has become a part of our daily life and is commonly used across a wide range of industries, these methodologies have been applied in countless areas of application and their use is in continuous expansion. In particular, these approaches play a key role in enabling Industry 4.0 and IoT scenarios. Many of the algorithm results cannot be understood and explained in terms of how and why a specific decision was made. With the advancement of machine learning research, several techniques and approaches have emerged in recent years, but only a few studies have been produced regarding the end-user perspective and understanding the results of the algorithms, making these algorithms into higher beings in which their credibility depends on how much faith one has. Therefore, the lack of interpretability in this technology is the biggest obstacle to the spread of these applications. Anomaly detection is a large subdivision of machine learning technology that has enormous applicability in industrial scenarios. In fact, it is extremely relevant for the purposes of quality monitoring, predictive prevention and much more. Furthermore, the strength of this type of approach is that it can be implemented without the need for tagged data and obviously in this type of frameworks where the data is "dirty", is very peculiar not to have labeled data. Obviously, this last application is also infected with the same problem that the whole family suffers from. This thesis describes the development of an anomaly detection system that is interpretable, which therefore aims at alleviate the problems introduced above by trying to focus as much as possible on the perspective of the end-user. The two main topics are anomaly detection on the one side and the interpretability of the models on the other.Machine learning has become a part of our daily life and is commonly used across a wide range of industries, these methodologies have been applied in countless areas of application and their use is in continuous expansion. In particular, these approaches play a key role in enabling Industry 4.0 and IoT scenarios. Many of the algorithm results cannot be understood and explained in terms of how and why a specific decision was made. With the advancement of machine learning research, several techniques and approaches have emerged in recent years, but only a few studies have been produced regarding the end-user perspective and understanding the results of the algorithms, making these algorithms into higher beings in which their credibility depends on how much faith one has. Therefore, the lack of interpretability in this technology is the biggest obstacle to the spread of these applications. Anomaly detection is a large subdivision of machine learning technology that has enormous applicability in industrial scenarios. In fact, it is extremely relevant for the purposes of quality monitoring, predictive prevention and much more. Furthermore, the strength of this type of approach is that it can be implemented without the need for tagged data and obviously in this type of frameworks where the data is "dirty", is very peculiar not to have labeled data. Obviously, this last application is also infected with the same problem that the whole family suffers from. This thesis describes the development of an anomaly detection system that is interpretable, which therefore aims at alleviate the problems introduced above by trying to focus as much as possible on the perspective of the end-user. The two main topics are anomaly detection on the one side and the interpretability of the models on the other

nanotechnologies in the agri-food chain

openQuesta tesi esplora le applicazioni e le potenzialità delle nanotecnologie nel set-tore agroalimentare, con particolare attenzione ai seguenti ambiti: agricoltura di precisione, proprietà e applicazioni antimicrobiche delle nanoparticelle, nanoparticelle magnetiche per l’estrazione di molecole da matrici alimentari, nanotecnologia nel food-packaging, aspetti tossicologici e normativi. Attraverso un’analisi dettagliata di diversi esempi di nanomateriali e nanosistemi, si evidenzia come le nanotecnologie possano offrire soluzioni innovative e altamente competitive per migliorare l’efficienza, la sicurezza e la sostenibilità della filiera agroalimentare. Tuttavia, si sottolinea anche la necessità di affrontare con responsabilità e cautela le questioni etiche, normative e ambientali connesse all’utilizzo delle nanotecnologie, al fine di garantire un impatto positivo sul settore agroalimentare e sulla salute pubblica

Packaging Industry Anomaly DEtection (PIADE) Dataset

PIADE dataset contains data from five industrial packaging machines: Machine s_1: from 2020-01-01 14:00:00 to 2021-12-31 13:00:00 Machine s_2: from 2020-06-17 08:00:00 to 2021-12-31 07:00:00 Machine s_3: from 2020-10-07 12:00:00 to 2022-01-01 23:00:00 Machine s_4: from 2020-01-01 01:00:00 to 2022-01-01 23:00:00 Machine s_5: from 2020-01-20 08:00:00 to 2022-01-01 12:00:00 ## Raw Data Each row represents a production interval, with the following schema: interval_start: start of the production interval equipment_ID: equipment identifier alarm: alarm code of the active stop reason, if it occurred type: idle, production, downtime, performance_loss or scheduled_downtime start: start of the production interval end: end of the production interval elapsed: duration of the production interval pi: input packages po: output packages speed: speed (packages per hour) There are 133 different types of alerts, and 429394 rows. ## Sequences (1h) data For each piece of equipment, we define sequences of length = 1 hour and we aggregate raw interval data as follows: 'equipment_ID': machine identifier '#changes': changes in machine state '%downtime': time spent in 'downtime' state '%idle': time spent in 'idle' state '%performance_loss': time spent in 'performance loss' state '%production': time spent in production '%scheduled_downtime': time spent in scheduled downtime 'count_sum': sum of all alarm occurrences 'A_': counter of alarm occurrences '/': number of transitions from to The collection of this dataset has been partially supported by the Regione Veneto project VIR2EM (VIrtualization and Remotization for Resilient and Efficient Manufacturing, Virtualizzazione e remotizzazione per una manifattura efficiente e resiliente

Repeatability and Reproducibility of Foveal Avascular Zone Area Measurement on Normal Eyes by Different Optical Coherence Tomography Angiography Instruments

Abstract PURPOSE: To compare the foveal avascular zone (FAZ) area measurements produced by different optical coherence tomography angiography (OCTA). METHODS: Healthy enrolled volunteers underwent OCTA using 2 different devices: Spectralis HRA+OCTA (Heidelberg Engineering, Heidelberg, Germany) and RS-3000 Advance (Nidek, Gamagori, Japan). Two graders measured FAZ in both superficial (SCP) and deep (DCP) retinal capillary plexuses. The SCP and DCP en face images were visualized automatically segmenting 2 separate slabs defined by the arbitrary segmentation lines created by the software of each OCT device. One grader repeated each measure twice. RESULTS: Fifty-nine eyes were included. The mean FAZ was 0.33 \ub1 0.09 mm2 at the SCP and 0.57 \ub1 0.17 mm2 at the DCP measured with RS-3000 versus 0.30 \ub1 0.08 and 0.35 \ub1 0.08 mm2, respectively, measured with Spectralis. The measurements of the 2 devices were significantly different (p < 0.0001). The intraoperator agreement was excellent at the SCP (intraclass correlation coefficient, ICC: 0.97 with Spectralis and 0.96 with RS-3000). At the DCP, it was good with Spectralis and fair with RS-3000 (ICC: 0.85 and 0.64, respectively). The interoperator agreement was excellent for Spectralis and good for RS-3000 at the SCP (ICC: 0.97 and 0.93, respectively). It was good at the DCP with both devices (ICC: 0.74 with RS-3000 and 0.81 with Spectralis). CONCLUSIONS: FAZ measurements obtained with different OCTA devices differ. These findings should be considered in follow-up studies of patients with retinal vascular diseases