Integrating IoT technologies for an "intelligent" safety management in the process industry

Abstract IoT (Internet of Things) technologies are wide spreading in several industrial sector due to a combination of increasing technical performance together with decreasing purchase prices: thus, new tools are been evaluated for adoption in new fields of application, like safety at work. In recent years, several projects and prototypes as well as industrial solutions have been developed using IOT technologies especially to dynamically managing safety levels at complex workplaces. The aim of this study is to describe a prototype system where the so called Smart Objects (SOs) - integrating different IoT technologies- interact in a working environment through a digital platform for managing different type of hazards – e.g. involving safety of plants as well as workers - usually influencing safety levels especially in process industry. The fields of application of the proposed system vary from tracking periodic mandatory maintenance and analyzing aging of equipment, processing or containing hazardous materials, to remote tracking of hazardous conditions of workers

Strategies to improve Critical Infrastructures Robustness against the IEMI threat : a Review of relevant Standards and Guidelines on the topic

This paper aims to provide a brief overview of relevant standards, procedures and guidelines to standard bodies to manage the impact of the Intentional ElectroMagnetic Interference (IEMI) threat. It also provides guidelines for CI operators on how to reduce their exposure on IEMI hazards

Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification.

To compare diagnostic performance of contrast-enhanced dual-energy digital mammography (CEDM) and digital breast tomosynthesis (DBT) alone and in combination compared to 2D digital mammography (MX) and dynamic contrast-enhanced MRI (DCE-MRI) in women with breast lesions. We enrolled 100 consecutive patients with breast lesions (BIRADS 3-5 at imaging or clinically suspicious). CEDM, DBT, and DCE-MRI 2D were acquired. Synthetized MX was obtained by DBT. A total of 134 lesions were investigated on 111 breasts of 100 enrolled patients: 53 were histopathologically proven as benign and 81 as malignant. Nonparametric statistics and receiver operating characteristic (ROC) curve were performed. Two-dimensional synthetized MX showed an area under ROC curve (AUC) of 0.764 (sensitivity 65%, specificity 80%), while AUC was of 0.845 (sensitivity 80%, specificity 82%) for DBT, of 0.879 (sensitivity 82%, specificity 80%) for CEDM, and of 0.892 (sensitivity 91%, specificity 84%) for CE-MRI. DCE-MRI determined an AUC of 0.934 (sensitivity 96%, specificity 88%). Combined CEDM with DBT findings, we obtained an AUC of 0.890 (sensitivity 89%, specificity 74%). A difference statistically significant was observed only between DCE-MRI and CEDM (P = .03). DBT, CEDM, CEDM combined to tomosynthesis, and DCE-MRI had a high ability to identify multifocal and bilateral lesions with a detection rate of 77%, 85%, 91%, and 95% respectively, while 2D synthetized MX had a detection rate for multifocal lesions of 56%. DBT and CEDM have superior diagnostic accuracy of 2D synthetized MX to identify and classify breast lesions, and CEDM combined with DBT has better diagnostic performance compared with DBT alone. The best results in terms of diagnostic performance were obtained by DCE-MRI. Dynamic information obtained by time-intensity curve including entire phase of contrast agent uptake allows a better detection and classification of breast lesions

Indoor positioning system using walking pattern classification

In the age of automation the ability to navigate persons and devices in indoor environments has become increasingly important for a rising number of applications. While Global Positioning System can be considered a mature technology for outdoor localization, there is no off-the-shelf solution for indoor tracking. In this contribution, an infrastructure-less Indoor Positioning System based on walking feature detection is presented. The proposed system relies on the differences characterizing different human actions (e.g., walking, ascending or descending stairs, taking the elevator). The motion features are extracted in time domain by exploiting data provided by a 9DoF Inertial Measurement Unit. The positioning algorithm is based on walking distance and heading estimation. Step count and step length are used to determine the walking distance, while the heading is computed by quaternions. An experimental setup has been developed. The collected results show that system guarantee room level accuracy during long trials

Improving situational awareness for first responders

This paper aims at exploring a novel approach for indoor localisation by exploiting data fusion. Specifically, personnel localisation in rescue scenarios is addressed: the key idea is to increase the situation awareness of rescuers. A pedestrian dead reckoning algorithm based on waist mounted inertial sensors is designed to cope with different human activities. The drifting estimate is re-calibrated by using information gathered from the environment. The outcomes of experimental trials performed in a real scenario are reported

Precordial Vibrations: A Review of Wearable Systems, Signal Processing Techniques, and Main Applications

Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects

Improving the safety and the operational efficiency of emergency operators via on field situational awareness

In rescue missions, the situational awareness represents an essential tool in supporting rescue team operating in unknown and complex indoor environments. In case of fire in highly congested industrial scenarios (e.g., refineries, oil depots, petrochemical plants, etc.), the smoke may reduce the awareness of the rescuer about potential local resources/hazards, affecting both operational efficiency and personal safety. The mitigation of potential consequences arising from major accidents can be limited providing the emergency staff with tools able to foster their role on field. In this paper, we present the RISING (indooR localization and building maintenance using radio frequency Identification and inertial NaviGation) project that is devoted to support on field operators supplying them with a system for situational awareness and personal indoor positioning. The RISING solution is based on the integration of the RFID technology with the inertial navigation. A set of RFID tags, conveniently preinstalled in the working environment, can store information about their absolute position and the site of local items. This information can be easily retrieved on-the-fly using RFID readers and displayed on smart devices with which the user is equipped (e.g., tablet and/or smartphone) to allow on field situational awareness

Optimization Models in a Smart Tool for the Railway Infrastructure Protection

In this paper we describe a smart tool, developed for the European project METRIP (MEthodological Tool for Railway Infrastructure Protection) based on optimal covering integer programming models to be used in designing the security system for a Railway Infrastructure. Two models are presented and tested on a railway station scheme. The results highlight the role that the optimization models can fulfill in the design of an effective security system

Railway station surveillance system design: A real application of an optimal coverage approach

The design of an effective and efficient surveillance system is fundamental for the protection of the Critical Infrastructures. In a railway station, this requirement turns on as an urgent prerequisite: for its intrinsic nature, a station represents a complex environment to be monitored for both safety and security reasons. In this work, we show how the video surveillance system of a real terminal railway station can be effectively designed in terms of sensor placement problem using an optimal coverage approach. The obtained results confirm the effectiveness of the proposed method in supporting security experts in both the design and reconfiguration of a surveillance system, in order to increase the asset security level