96 research outputs found
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Occupancy monitoring and prediction in ambient intelligent environment
Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent
Smart Monitoring and Control in the Future Internet of Things
The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things
Transitioning power distribution grid into nanostructured ecosystem : prosumer-centric sovereignty
PhD ThesisGrowing acceptance for in-house Distributed Energy Resource (DER) installations at lowvoltage
level have gained much significance in recent years due to electricity market liberalisations
and opportunities in reduced energy billings through personalised utilisation
management for targeted business model. In consequence, modelling of passive customers’
electric power system are progressively transitioned into Prosumer-based settings where presidency
for Transactive Energy (TE) system framework is favoured. It amplifies Prosumers’
commitments into annexing TE values during market participations and optimised energy
management to earn larger rebates and incentives from TE programs. However, when dealing
with mass Behind-The-Meter DER administrations, Utility foresee managerial challenges
when dealing with distribution network analysis, planning, protection, and power quality
security based on Prosumers’ flexibility in optimising their energy needs.
This dissertation contributes prepositions into modelling Distributed Energy Resources
Management System (DERMS) as an aggregator designed for Prosumer-centered cooperation,
interoperating TE control and coordination as key parameters to market for both
optimised energy trading and ancillary services in a Community setting. However, Prosumers
are primarily driven to create a profitable business model when modelling their
DERMS aggregator. Greedy-optimisation exploitations are negative concerns when decisions
made resulted in detrimental-uncoordinated outcomes on Demand-Side Response (DSR)
and capacity market engagements. This calls for policy decision makers to contract safe (i.e.
cooperative yet competitive tendency) business models for Prosumers to maximise TE values
while enhancing network’s power quality metrics and reliability performances.
Firstly, digitalisation and nanostructuring of distribution network is suggested to identify
Prosumer as a sole energy citizen while extending bilateral trading between Prosumer-to-
Prosumer (PtP) with the involvements of other grid operators−TE system. Modelling of
Nanogrid environment for DER integrations and establishment of local area network infrastructure
for IoT security (i.e. personal computing solutions and data protection) are committed
for communal engagements in a decentralise setting. Secondly, a multi-layered Distributed
Control Framework (DCF) is proposed using Microsoft Azure cloud-edge platform that cascades energy actors into respective layers of TE control and coordination. Furthermore,
modelling of flexi-edge computing architecture is proposed, comprising of Contract-Oriented
Sensor-based Application Platform (COSAP) employing Multi-Agent System (MAS) to
enhance data-sharing privacy and contract coalition agreements during PtP engagements.
Lastly, the Agents of MAS are programmed with cooperative yet competitive intelligences
attributed to Reinforcement Learning (RL) and Neural Networks (NN) algorithms to solve
multimodal socio-economical and uncertainty problems that corresponded to Prosumers’
dynamic energy priorities within the TE framework. To verify the DERMS aggregator
operations, three business models were proposed (i.e. greedy-profit margin, collegial-peak
demand, reserved-standalone) to analyse comparative technical/physical and economic/social
dimensions. Results showed that the proposed TE-valued DERMS aggregator provides
participation versatility in the electricity market that enables competitive edginess when utilising
Behind-The-Meter DERs in view of Prosumer’s asset scheduling, bidding strategy, and
corroborative ancillary services. Performance metrics were evaluated on both domestic and
industrial NG environments against IEEE Standard 2030.7-2017 & 2030.8-2018 compliances
to ensure deployment practicability.
Subsequently, proposed in-house protection system for DER installation serves as an
add-on monitoring service which can be incorporated into existing Advance Distribution
Management System (ADMS) for Distribution Service Operator (DSO) and field engineers
use, ADMS aggregator. It provides early fault detections and isolation processes from allowing
fault current to propagate upstream causing cascading power quality issues across
the feeder line. In addition, ADMS aggregator also serves as islanding indicator that distinguishes
Nanogrid’s islanding state from unintentional or intentional operations. Therefore, a
Overcurrent Current Relay (OCR) is proposed using Fuzzy Logic (FL) algorithm to detect,
profile, and provide decisional isolation processes using specified OCRs. Moreover, the
proposed expert knowledge in FL is programmed to detect fault crises despite insufficient
fault current level contributed by DER (i.e. solar PV system) which conventional OCR fails
to trigger
Safety-critical scenarios and virtual testing procedures for automated cars at road intersections
This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios.
Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions.
Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions.
As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system.
Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads
iCity. Transformative Research for the Livable, Intelligent, and Sustainable City
This open access book presents the exciting research results of the BMBF funded project iCity carried out at University of Applied Science Stuttgart to help cities to become more liveable, intelligent and sustainable, to become a LIScity. The research has been pursued with industry partners and NGOs from 2017 to 2020. A LIScity is increasingly digitally networked, uses resources efficiently, and implements intelligent mobility concepts. It guarantees the supply of its grid-bound infrastructure with a high proportion of renewable energy. Intelligent cities are increasingly human-centered, integrative, and flexible, thus placing the well-being of the citizens at the center of developments to increase the quality of life. The articles in this book cover research aimed to meet these criteria. The book covers research in the fields of energy (i.e. algorithms for heating and energy storage systems, simulation programs for thermal local heating supply, runtime optimization of combined heat and power (CHP), natural ventilation), mobility (i.e. charging distribution and deep learning, innovative emission-friendly mobility, routing apps, zero-emission urban logistics, augmented reality, artificial intelligence for individual route planning, mobility behavior), information platforms (i.e. 3DCity models in city planning: sunny places visualization, augmented reality for windy cities, internet of things (IoT) monitoring to visualize device performance, storing and visualizing dynamic energy data of smart cities), and buildings and city planning (i.e. sound insulation of sustainable facades and balconies, multi-camera mobile systems for inspection of tunnels, building-integrated photovoltaics (BIPV) as active façade elements, common space, the building envelopes potential in smart sustainable cities)
Measuring knowledge sharing processes through social network analysis within construction organisations
The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable
to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes
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