Improving Human Reliability Analysis for Railway Systems Using Fuzzy Logic

The International Union of Railway provides an annually safety report highlighting that human factor is one of the main causes of railway accidents every year. Consequently, the study of human reliability is fundamental, and it must be included within a complete reliability assessment for every railway-related system. However, currently RARA (Railway Action Reliability Assessment) is the only approach available in literature that considers human task specifically customized for railway applications. The main disadvantages of RARA are the impact of expert’s subjectivity and the difficulty of a numerical assessment for the model parameters in absence of an exhaustive error and accident database. This manuscript introduces an innovative fuzzy method for the assessment of human factor in safety-critical systems for railway applications to address the problems highlighted above. Fuzzy logic allows to simplify the assessment of the model parameters by means of linguistic variables more resemblant to human cognitive process. Moreover, it deals with uncertain and incomplete data much better than classical deterministic approach and it minimizes the subjectivity of the analyst evaluation. The output of the proposed algorithm is the result of a fuzzy interval arithmetic, $\alpha$ -cut theory and centroid defuzzification procedure. The proposed method has been applied to the human operations carried out on a railway signaling system. Four human tasks and two scenarios have been simulated to analyze the performance of the proposed algorithm. Finally, the results of the method are compared with the classical RARA procedure underline compliant results obtain with a simpler, less complex and more intuitive approach

Data augmentation using background replacement for automated sorting of littered waste

The introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, which remains unsolved, is how to deal with the large amount of waste that is littered in the environment instead of being collected properly. In this paper, we introduce BackRep: a method for building waste recognizers that can be used for identifying and sorting littered waste directly where it is found. BackRep consists of a data-augmentation procedure, which expands existing datasets by cropping solid waste in images taken on a uniform (white) background and superimposing it on more realistic backgrounds. For our purpose, realistic backgrounds are those representing places where solid waste is usually littered. To experiment with our data-augmentation procedure, we produced a new dataset in realistic settings. We observed that waste recognizers trained on augmented data actually outperform those trained on existing datasets. Hence, our data-augmentation procedure seems a viable approach to support the development of waste recognizers for urban and wild environments

Mimicking Behaviors in Separated Domains

Devising a strategy to make a system mimic behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of LTLf , a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separated dynamic domains, DA and DB , and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of DA into properties on behaviors of DB . The goal is to synthesize a strategy that step-by-step maps every behavior of DA into a behavior of DB so that the specification is met. We consider several forms of mapping specifications, ranging from simple ones to full LTLf , and for each, we study synthesis algorithms and computational properties

Reliability analysis of wireless sensor network for smart farming applications

Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network

Perceptions and attitudes toward blue energy and technologies in the Mediterranean area: ASKYOURCITIZENSONBE

An energy transition is needed in order to meet the European pledge of reaching climate neutrality by 2050. This transition cannot ignore the renewable resources available from 70% of the Earth (namely, the oceans and seas). This concept is fundamental for the planet, especially for the Mediterranean area. Marine renewable energies are still under-deployed in the Mediterranean area for many reasons, including legislative constraints, lower energy availability, and technological readiness. An appropriate participatory process including all actors (e.g., policymakers, firms, citizens, and researchers) is necessary for a correct path toward decarbonization. The BLUE DEAL project was conceived and implemented by 12 Mediterranean partners to tackle these issues and set the route for blue energy deployment in the Mediterranean area. Activities already conducted include a survey to probe the perceptions and attitudes of citizens toward blue energy. The survey targeted about 3,000 persons in 12 Mediterranean sites with the aim of bringing citizens into the discussion on future technologies. The results showed that although blue energy is still relatively unknown to the general public (only 42% of respondents were aware of these technologies), there was a general willingness (70%) to host one or more such installations in their areas. Here, we describe our survey method and some empirical results with suggestions for replicability and recommendations on how to use it for policymaking purposes