A Modelling Approach To Human Navigation in Constrained Spaces

In this thesis, we consider algorithms and systems which dynamically guide evacuees towards exits during an emergency to minimise building evacuation time. We observe that the "shortest safe path" routing approach is inadequate when congestion is a predominant factor, and therefore focus on systems which manage congestion. We first implement a "Reactive" metric which compares paths based on real-time transit times. We find that regular route corrections must be issued to address the constant changes in path delays, and that routes oscillate. We also implement a model-based "Proactive" metric which forecasts the increase in future congestion that results from every routing decision, allowing the routing algorithm to operate offline. We combine both metrics with the Cognitive Packet Network (CPN), a distributed self-aware routing algorithm which uses neural networks to efficiently explore the building graph. We also present the first thorough sensitivity analysis on CPN's parameters, and use this to tune CPN for optimal performance. We then compare the proactive and reactive approaches through simulation and find both approaches reduce building evacuation times -- especially when evacuees are not evenly distributed in the building. We also find major differences between the Proactive and Reactive approach, in terms of stability, flexibility, sensory requirements, etc. Finally, we consider guiding evacuees using dynamic exit signs, whose pointing direction can be controlled. Dynamic signs can readily be used with Reactive routing, but since Proactive routing issues routes on an individual basis, one display is required for each evacuee. This is incompatible with dynamic signs; therefore we propose a novel algorithm which controls the dynamic signs according to the Proactive algorithm's output. We simulate both systems, compare their performance, and review their practical limitations. For both approaches, we find that updating the sign's display more often improves performance, but this may reduce evacuee compliance and make the system inefficient in real-life conditions.Open Acces

Decision makers\u27 experience of participatory dynamic simulation modelling: Methods for public health policy

Background: Systems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context. Methods: Semi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development. Results: The ‘co-production’ aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening. Conclusion: These findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings

Design and performance of an integrated envelope/lighting system

Dynamic envelope/lighting systems offer the potential to achieve a near optimum energy-efficient environment meeting occupant needs throughout the year by adapting to dynamic meteorological conditions and changing occupant preferences in real time. With the dramatic increased functionality of the microprocessor, there is an untapped potential to make dynamic envelop/lighting systems easier to use, diagnose, and monitor, and to integrate them as part of a sophisticated building-wide control system. This study addresses the complex relationship between this energy-efficiency technology and many of the non-energy issues related to its potential acceptance by the building industry, architects, owners, and users. The authors demonstrate the concept of integrated dynamic systems with a prototype motorized venetian blind operated in synchronization with electric lighting and daylighting controls via an intelligent control system. Research work conducted with simulation software and reduced-scale and full-scale field tests is summarized. Much of this work is directly relevant to other active shading and daylighting systems on the market today and to state-of-the-art window systems yet to come (i.e., electrochromics)

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Getting a Better Performing Building: Commissioning and Real Time Data Analysis

Commissioning of new construction is becoming increasingly accepted as a quality assurance tool to deliver performance, reliability, and efficiency in building systems. But what about existing building stock? Does successful commissioning of the construction process ensure design performance throughout the life of building systems? If you believe that commissioning is the key to acquiring a system that performs as intended, what about building systems that were never commissioned? Short of a substantial system failure, can we assume optimal performance? The answer is no. Performance verification requires a method of measurement. For those practitioners who have explored the commissioning of existing facilities, the consensus persists that existing buildings present energy saving opportunities upwards of 25%1, while reconciling mechanical system performance shortfalls, occupant comfort issues, and potential Indoor Air Quality (IAQ) issues. The key to identifying these lost savings is a measurement plan based on real time system operational data. Because of the dynamic nature and complexity of commercial building HVAC systems, they are the perfect target for periodic performance assessments, or recommissioning. Today's buildings are expected to supply designed conditioning and ventilation requirements as well as modulate to the loads and schedules of a variety of end use requirements throughout the building. Evaluating that the HVAC system is performing to load conditions is critical to the operating needs of the facility, and documenting that it meets the designed performance standards and the resulting energy use, and no more, ensures efficient system performance. The key to documenting system performance is the ability to access and analyze reliable system performance data. This paper will utilize EH&E's commissioning experience and methods to explore the relationship between HVAC system performance verification and good Measurement and Verification practice in both the commissioning of new construction projects as well as trouble shooting existing building problems

Optimization of Load Allocation Strategy of a Multi-source Energy System by Means of Dynamic Programming

AbstractMulti-source systems for the fulfillment of electric, thermal and cooling demand of a building can be based on different technologies (e.g. solar photovoltaic, solar heating, cogeneration, heat pump, absorption chiller) which use renewable, partially renewable and fossil energy sources. The main issues of these kinds of multi-source systems are (i) the allocation strategy which allows the division of the energy demands among the various technologies and (ii) the proper sizing of each technology.Furthermore, these two issues proves to be deeply interrelated because, while a wiser energy demand allocation strategy can lead to significant reductions in primary energy consumption, the definition itself of an optimal allocation strategy strongly depends on the actual sizing of the employed technologies. Thus the problem of optimizing the sizing of each technology cannot be separated from the definition of an optimal control strategy. For this purpose a model of a multi-source energy system, previously developed and implemented in the Matlab® environment, has been considered. The model takes account of the load profiles for electricity, heating and cooling for a whole year and the performance of the energy systems are modelled through a systemic approach. A dynamic programming algorithm is therefore employed in order to obtain an optimal control strategy for the energy demand allocation during the winter period. While the resulting control strategy is non-causal and therefore not suitable for the implementation on a real-time application, it allows the definition of a benchmark on the maximum primary energy savings achievable with a specific sizing solution. This result is therefore very helpful both in comparing different solutions and in subsequently define a proper causal control strategy. Finally, the model is applied to the case of a thirteen-floors tower composed of a two-floor shopping mall at ground level and eleven floors used as offices

Occupant behaviour pattern modeling and detection in buildings based on environmental sensing

Occupant presence and behaviour have a signi�cant impact on building energy performance. An occupant present in a building generates pollutants like CO2, odour, heat, which can directly change the indoor environment. Because of this change, the occupant may interact with the building environment to maintain the comfort level, for example, he or she may turn on air conditioning systems. Today's Building Energy Management Systems (BEMS) are usually operated based on a �xed seasonal schedule and maximum design occupancy assumption but fail to capture dynamic information. This is both costly and ine�cient. Recent e�orts on exploitation of environmental sensors and data-driven approaches to monitor occupant behaviour patterns, have shown the potential for dynamically adapt BEMS according to real user needs. Furthermore, this occupant information can also be used for other applications such as home security, healthcare or smart environments. However, most of existing models su�er from inaccuracy and imprecision for occupant state classi�cation, could not adaptively learn from real-time sensor input and they mainly focused on single occupant scenarios only. To address these issues, we present a novel data-driven approach to model occupant behaviour patterns accurately, for both single occupant and multiple occupants with real-time sensor information. The contributions can be summarised as follows: Firstly, we have conducted a thorough benchmark evaluation of classi�cation performance of state-of-the-art Machine Learning (ML) methods and occupant related publicly available datasets. Secondly, based on the �ndings in literature and our own experimental evaluations, we have developed a novel dynamic hidden semi-Markov model (DHSMM), which can accurately detect occupant behaviour patterns from sensor data streams in real-time. Thirdly, built upon the online DHSMM model, we have developed a novel incremental learning approach to allow dynamically learning over streaming data. Finally, we have conducted an experimental evaluation of our proposed model Online DHSMM Multi-Occupant for occupancy detection for both single and multiple occupants. We have validated our approach using real datasets and the experimental results show our proposed approach outperforms existing methods in terms of classi�cation accuracy and processing time/scalability. To the best of our knowledge, we have �rst developed a HSMM-based incremental online learning approach to fast and accurate learn building occupant patterns over streaming data for both single and multiple occupants in a holistic way. Additionally, our approach signi�cantly improves the classi�cation accuracies of traditional Markov models (over 10% accuracy increase, while maintaining the model complexity and performing multioccupant detection)

An Analysis of issues against the adoption of Dynamic Carpooling

Using a private car is a transportation system very common in industrialized countries. However, it causes different problems such as overuse of oil, traffic jams causing earth pollution, health problems and an inefficient use of personal time. One possible solution to these problems is carpooling, i.e. sharing a trip on a private car of a driver with one or more passengers. Carpooling would reduce the number of cars on streets hence providing worldwide environmental, economical and social benefits. The matching of drivers and passengers can be facilitated by information and communication technologies. Typically, a driver inserts on a web-site the availability of empty seats on his/her car for a planned trip and potential passengers can search for trips and contact the drivers. This process is slow and can be appropriate for long trips planned days in advance. We call this static carpooling and we note it is not used frequently by people even if there are already many web-sites offering this service and in fact the only real open challenge is widespread adoption. Dynamic carpooling, on the other hand, takes advantage of the recent and increasing adoption of Internet-connected geo-aware mobile devices for enabling impromptu trip opportunities. Passengers request trips directly on the street and can find a suitable ride in just few minutes. Currently there are no dynamic carpooling systems widely used. Every attempt to create and organize such systems failed. This paper reviews the state of the art of dynamic carpooling. It identifies the most important issues against the adoption of dynamic carpooling systems and the proposed solutions for such issues. It proposes a first input on solving the problem of mass-adopting dynamic carpooling systems.Comment: 10 pages, whitepaper, extracted from B.Sc. thesis "Dycapo: On the creation of an open-source Server and a Protocol for Dynamic Carpooling" (Daniel Graziotin, 2010

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions