51 research outputs found

    Advances in Information Security and Privacy

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    With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue

    Agents and Robots for Reliable Engineered Autonomy

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    This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

    Ramping Up for Cost and Performance Improvements

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    The objective of this Research Project was to study the possible benefits of developing an improved Metering Control System. This study identified potential cost savings and reduced emissions through the greater use of a Metering Control System. The results of this research have indicated an annual cost reduction of $720,000 in fuel burn and a significant delay time reduction caused by accumulated ground traffic. In addition, during peak hours, it was concluded that savings could be potentially higher. Therefore, by optimizing the ground movement, it is also expected a lower level of CO2 emissions, an increased safety level due to more organized apron movements, and finally, an improved customer experience. The data presented in this research is in line with current Brazilian regulations, focused on reducing or minimizing airline ground delays. The selected airport sample is a main hub of an airline, concentrating 95% of its operation. Also, the airport is classified as the fourth busiest in terms of operation in Brazil. An inefficient apron management is responsible for causing a variety of disruptions in terms of on-time performance, fuel consumption and customer satisfaction. Some events may be reduced or even eliminated by applying simple practices, therefore, increasing efficiency without compromising safety

    Air Traffic Management Abbreviation Compendium

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    As in all fields of work, an unmanageable number of abbreviations are used today in aviation for terms, definitions, commands, standards and technical descriptions. This applies in general to the areas of aeronautical communication, navigation and surveillance, cockpit and air traffic control working positions, passenger and cargo transport, and all other areas of flight planning, organization and guidance. In addition, many abbreviations are used more than once or have different meanings in different languages. In order to obtain an overview of the most common abbreviations used in air traffic management, organizations like EUROCONTROL, FAA, DWD and DLR have published lists of abbreviations in the past, which have also been enclosed in this document. In addition, abbreviations from some larger international projects related to aviation have been included to provide users with a directory as complete as possible. This means that the second edition of the Air Traffic Management Abbreviation Compendium includes now around 16,500 abbreviations and acronyms from the field of aviation

    Hybrid verification technique for decision-making of self-driving vehicles

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    The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed

    Numerical Simulations

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    This book will interest researchers, scientists, engineers and graduate students in many disciplines, who make use of mathematical modeling and computer simulation. Although it represents only a small sample of the research activity on numerical simulations, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. It will be useful to encourage further experimental and theoretical researches in the above mentioned areas of numerical simulation

    Modelling and optimisation of resource usage in an IoT enabled smart campus

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    University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

    SEGMENTACIÓN DE LUGARES DISPONIBLES EN ESTACIONAMIENTOS HACIENDO USO DE REDES NEURONALES PULSO-ACOPLADAS (PARKING SLOTS SEGMENTATION USING PULSE-COUPLED NEURAL NETWORKS)

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    Resumen Algunos de los principales retos en los sistemas de asistencia a estacionamientos basados en visión artificial que están dedicados a la segmentación de lugares disponibles, son las diferentes afectaciones que se pueden presentar, como por ejemplo las variaciones de luz, generación de sombras, así como las diferentes tonalidades de color que presentan los automóviles; los cuales pueden afectar la detección. En este trabajo se propone un algoritmo de identificación basado en el análisis y procesamiento de imágenes en el espacio de color HSV, haciendo uso de un algoritmo de redes neuronales pulso-acopladas (PCNN) en su forma simplificada a través del modelo de intersección cortical (ICM). El algoritmo propuesto está dividido en tres partes, análisis de la imagen en HSV, segmentación y detección, el cual se evaluó haciendo uso de diferentes imágenes capturadas en un estacionamiento. Se obtuvieron los valores de los parámetros de la red ICM para el proceso de segmentación. Los resultados obtenidos muestran que el algoritmo propuesto permite reducir la susceptibilidad a los efectos de tonalidad que presentan los automóviles, así como los cambios ligeros de iluminación, consiguiendo así la detección de automóviles con diferentes colores bajo las condiciones del día. Palabra(s) Clave: Estacionamiento, reconocimiento, redes neuronales pulso-acopladas, segmentación. Abstract The main challenges in parking lots assistant systems based on artificial vision, which are dedicated to the segmentation of available places into parking lots, are the different effects that can occur such as, changes in luminosity, shadows produced by cars, as well as different color hues that can affect detection. In this work, an identification algorithm based on the analysis and processing of images in the HSV color space is proposed, using pulse-coupled neural networks (PCNN) algorithm in its simplified form, the intersection cortical model (ICM). The proposed algorithm is divided in three parts, HSV image analysis, segmentation, and detection, which was evaluated using different images captured in parking lot. The ICM network parameter values were obtained for the segmentation process. The results show that proposed algorithm allows to reduce the susceptibility presented by cars, as well as slight changes in lighting, thus achieving the detection of cars with different colors under daytime conditions. Keywords: Parking lot, pulse-coupled neural networks, survey, segmentation

    From Smart Parking Towards Autonomous Valet Parking: A Survey, Challenges and Future Works

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    Recently, we see an increasing number of vehicles coming into our lives, which makes finding car parks a difficult task. To overcome this challenge, efficient and advanced parking techniques are required, such as finding the proper parking slot, increasing users’ experience, dynamic path planning and congestion avoidance. To this end, this survey provides a detailed overview starting from Smart Parking (SP) towards the emerging Autonomous Valet Parking (AVP) techniques. Specially, the SP includes digitally enhanced parking, smart routing, high density parking and vacant slot detection solutions. Moreover, the AVP involves Short-range Autonomous Valet Parking (SAVP) and Long-range Autonomous Valet Parking (LAVP). Finally, open issues and future work are provided
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