FRAM for systemic accident analysis: a matrix representation of functional resonance

Due to the inherent complexity of nowadays Air Traffic Management (ATM) system, standard methods looking at an event as a linear sequence of failures might become inappropriate. For this purpose, adopting a systemic perspective, the Functional Resonance Analysis Method (FRAM) originally developed by Hollnagel, helps identifying non-linear combinations of events and interrelationships. This paper aims to enhance the strength of FRAM-based accident analyses, discussing the Resilience Analysis Matrix (RAM), a user-friendly tool that supports the analyst during the analysis, in order to reduce the complexity of representation of FRAM. The RAM offers a two dimensional representation which highlights systematically connections among couplings, and thus even highly connected group of couplings. As an illustrative case study, this paper develops a systemic accident analysis for the runway incursion happened in February 1991 at LAX airport, involving SkyWest Flight 5569 and USAir Flight 1493. FRAM confirms itself a powerful method to characterize the variability of the operational scenario, identifying the dynamic couplings with a critical role during the event and helping discussing the systemic effects of variability at different level of analysis

Error resilient video transcoding for robust inter-network communications using GPRS

A novel fully comprehensive mobile video communications system is proposed in this paper. This system exploits the useful rate management features of the video transcoders and combines them with error resilience for transmissions of coded video streams over general packet radio service (GPRS) mobileaccess networks. The error-resilient video transcoding operation takes place at a centralized point, referred to as a video proxy, which provides the necessary output transmission rates with the required amount of robustness. With the use of this proposed algorithm, error resilience can be added to an already compressed video stream at an intermediate stage at the edge of two or more different networks through two resilience schemes, namely the adaptive intra refresh (AIR) and feedback control signaling (FCS) methods. Both resilience tools impose an output rate increase which can also be prevented with the proposed novel technique in this paper. Thus, an error-resilient video transcoding scheme is presented to give robust video outputs at near target transmission rates that only require the same number of GPRS timeslots as the nonresilient schemes. Moreover, an ultimate robustness is also accomplished with the combination of the two resilience algorithms at the video proxy. Extensive computer simulations demonstrate the effectiveness of the proposed system

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Municipal Energy Management: Best Practices from DVRPC's Direct Technical Assistance Program

This guide highlights best practices and lessons learned from municipal energy management projects in southeastern Pennsylvania. In 2013 and 2014, DVRPC worked with nine municipalities in southeastern Pennsylvania to provide direct technical assistance with measuring, analyzing, and developing implementation strategies for energy management in municipal buildings. The goal of energy management is to identify opportunities for improving how energy is being used at a facility and to develop analyses that support decision making on how best to prioritize and implement these improvements. These improvements can remedy various problems -- high energy and maintenance costs due to malfunctioning, poorly installed or aging equipment, poor occupant comfort due to a lack of weatherization, or poorly controlled equipment. This guide will illustrate several best practices for identifying and implementing energy management opportunities that save money and improve building comfort

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

Leveraging Edge Computing through Collaborative Machine Learning

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Internal report cluster 1: Urban freight innovations and solutions for sustainable deliveries (2/4)

Technical report about sustainable urban freight solutions, part 2 of

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively