Buffer scheme with battery energy storage capability for enhancement of network transient stability and load ride-through

This paper examines a buffer scheme to mitigate the negative impacts of power-conditioned loads on network voltage and transient stabilities. The scheme is based on the use of battery energy-storage systems in the buffers. The storage systems ensure that protected loads downstream of the buffers can ride through upstream voltage sags and swells. Also, by controlling the buffers to operate in either constant impedance or constant power modes, power is absorbed or injected by the storage systems. The scheme thereby regulates the rotor-angle deviations of generators and enhances network transient stability. A computational method is described in which the capacity of the storage systems is determined to achieve simultaneously the above dual objectives of load ride-through and stability enhancement. The efficacy of the resulting scheme is demonstrated through numerical examples.

Network-targeted combination therapy : a new concept in cancer treatment

Toxicity is a major concern for anti-neoplastic drugs, with much of the existing pharmacopoeia being characterized by a very narrow therapeutic index. 'Network-targeted' combination therapy is a promising new concept in cancer therapy, whereby therapeutic index might be improved by targeting multiple nodes in a cell's signaling network, rather than a single node. Here, we examine the potential of this novel approach, illustrating how therapeutic benefit could be achieved with smaller doses of the necessary agents.

What I want for my Social Network privacy

Social Networks (SN) users have various privacy requirements to protect their information; to address this issue, a six-stage thematic analysis of scholarly articles related to SN user privacy concerns were synthesized. Then this research combines mixed methods research employing the strengths of quantitative and qualitative research to investigate general SN users, and thus construct a new set of ?ve primary and Twenty-?ve secondary SN user privacy requirements. Such an approach has been rarely used to examine the privacy requirements. Factor analysis results show superior agreement with theoretical predictions and signi?cant improvement over previous alternative models of SN user privacy requirements. This research presented here has the potential to provide for the development of more sophisticated privacy controls which will increase the ability of SN users to: specify their rights in SNs and to determine the protection of their own SN data.

Long-term exploration and tours for energy constrained robots with online proprioceptive traversability estimation

This paper is concerned with how a localised and energy-constrained robot can maximise its time in the field by taking paths and tours that minimise its energy expenditure. A significant component of a robot's energy is expended on mobility and is a function of terrain traversability. We estimate traversability online from data sensed by the robot as it moves, and use this to generate maps, explore and ultimately converge on minimum energy tours of the environment. We provide results of detailed simulations and parameter studies that show the efficacy of this approach for a robot moving over terrain with unknown traversability as well as a number of a priori unknown hard obstacles.

Development of gas sensing technology for ground and airborne applications powered by solar energy : methodology and experimental results

Monitoring gases for environmental, industrial and agricultural fields is a demanding task that requires long periods of observation, large quantity of sensors, data management, high temporal and spatial resolution, long term stability, recalibration procedures, computational resources, and energy availability. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) are currently representing the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialised gas sensing systems, and offer the possibility of geo-located and time stamp samples. However, these technologies are not fully functional for scientific and commercial applications as their development and availability is limited by a number of factors: the cost of sensors required to cover large areas, their stability over long periods, their power consumption, and the weight of the system to be used on small UAVs. Energy availability is a serious challenge when WSN are deployed in remote areas with difficult access to the grid, while small UAVs are limited by the energy in their reservoir tank or batteries. Another important challenge is the management of data produced by the sensor nodes, requiring large amount of resources to be stored, analysed and displayed after long periods of operation. In response to these challenges, this research proposes the following solutions aiming to improve the availability and development of these technologies for gas sensing monitoring: first, the integration of WSNs and UAVs for environmental gas sensing in order to monitor large volumes at ground and aerial levels with a minimum of sensor nodes for an effective 3D monitoring; second, the use of solar energy as a main power source to allow continuous monitoring; and lastly, the creation of a data management platform to store, analyse and share the information with operators and external users. The principal outcomes of this research are the creation of a gas sensing system suitable for monitoring any kind of gas, which has been installed and tested on CH4 and CO2 in a sensor network (WSN) and on a UAV. The use of the same gas sensing system in a WSN and a UAV reduces significantly the complexity and cost of the application as it allows: a) the standardisation of the signal acquisition and data processing, thereby reducing the required computational resources; b) the standardisation of calibration and operational procedures, reducing systematic errors and complexity; c) the reduction of the weight and energy consumption, leading to an improved power management and weight balance in the case of UAVs; d) the simplification of the sensor node architecture, which is easily replicated in all the nodes. I evaluated two different sensor modules by laboratory, bench, and field tests: a non-dispersive infrared module (NDIR) and a metal-oxide resistive nano-sensor module (MOX nano-sensor). The tests revealed advantages and disadvantages of the two modules when used for static nodes at the ground level and mobile nodes on-board a UAV. Commercial NDIR modules for CO2 have been successfully tested and evaluated in the WSN and on board of the UAV. Their advantage is the precision and stability, but their application is limited to a few gases. The advantages of the MOX nano-sensors are the small size, low weight, low power consumption and their sensitivity to a broad range of gases. However, selectivity is still a concern that needs to be addressed with further studies. An electronic board to interface sensors in a large range of resistivity was successfully designed, created and adapted to operate on ground nodes and on-board UAV. The WSN and UAV created were powered with solar energy in order to facilitate outdoor deployment, data collection and continuous monitoring over large and remote volumes. The gas sensing, solar power, transmission and data management systems of the WSN and UAV were fully evaluated by laboratory, bench and field testing. The methodology created to design, developed, integrate and test these systems was extensively described and experimentally validated. The sampling and transmission capabilities of the WSN and UAV were successfully tested in an emulated mission involving the detection and measurement of CO2 concentrations in a field coming from a contaminant source; the data collected during the mission was transmitted in real time to a central node for data analysis and 3D mapping of the target gas. The major outcome of this research is the accomplishment of the first flight mission, never reported before in the literature, of a solar powered UAV equipped with a CO2 sensing system in conjunction with a network of ground sensor nodes for an effective 3D monitoring of the target gas. A data management platform was created using an external internet server, which manages, stores, and shares the data collected in two web pages, showing statistics and static graph images for internal and external users as requested. The system was bench tested with real data produced by the sensor nodes and the architecture of the platform was widely described and illustrated in order to provide guidance and support on how to replicate the system. In conclusion, the overall results of the project provide guidance on how to create a gas sensing system integrating WSNs and UAVs, how to power the system with solar energy and manage the data produced by the sensor nodes. This system can be used in a wide range of outdoor applications, especially in agriculture, bushfires, mining studies, zoology, and botanical studies opening the way to an ubiquitous low cost environmental monitoring, which may help to decrease our carbon footprint and to improve the health of the planet.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Confucius Institutes and the network communication approach to public diplomacy

Discussions of public diplomacy in recent years have paid a growing amount of attention to networks. This network perspective is understood to provide insights into various issues of public diplomacy, such as its effects, credibility, reputation, identity and narratives. This paper applies the network idea to analyse China’s Confucius Institutes initiative. It understands Confucius Institutes as a global network and argues that this network structure has potential implications for the operation of public and cultural diplomacy that are perhaps underestimated in existing accounts of Chinese cultural diplomacy. In particular, it is noted that the specific setup of Confucius Institutes requires the engagement of local stakeholders, in a way that is less centralised and more networked than comparable cultural diplomacy institutions. At the same time, the development of a more networked for of public cultural diplomacy is challenged in practice by both practical issues and the configuration of China’s state-centric public diplomacy system informed by the political constitution of the Chinese state.

Non-invasive techniques for imaging in-vivo human corneal sub basal nerve mapping and migration rate in diabetic neuropathy

Purpose Corneal confocal microscopy (CCM) is a rapid non-invasive ophthalmic technique, which has been shown to diagnose and stratify the severity of diabetic neuropathy. Current morphometric techniques assess individual static images of the subbasal nerve plexus; this work explores the potential for non-invasive assessment of the wide-field morphology and dynamic changes of this plexus in vivo. Methods In this pilot study, laser scanning CCM was used to acquire maps (using a dynamic fixation target and semi-automated tiling software) of the central corneal sub-basal nerve plexus in 4 diabetic patients with and 6 without neuropathy and in 2 control subjects. Nerve migration was measured in an additional 7 diabetic patients with neuropathy, 4 without neuropathy and in 2 control subjects by repeating a modified version of the mapping procedure within 2-8 weeks, thus facilitating re-identification of distinctive nerve landmarks in the 2 montages. The rate of nerve movement was determined from these data and normalised to a weekly rate (µm/week), using customised software. Results Wide-field corneal nerve fibre length correlated significantly with the Neuropathy Disability Score (r = -0.58, p < 0.05), vibration perception (r = -0.66, p < 0.05) and peroneal conduction velocity (r = 0.67, p < 0.05). Central corneal nerve fibre length did not correlate with any of these measures of neuropathy (p > 0.05 for all). The rate of corneal nerve migration was 14.3 ± 1.1 µm/week in diabetic patients with neuropathy, 19.7 ± 13.3µm/week in diabetic patients without neuropathy, and 24.4 ± 9.8µm/week in control subjects; however, these differences were not significantly different (p = 0.543). Conclusions Our data demonstrate that it is possible to capture wide-field images of the corneal nerve plexus, and to quantify the rate of corneal nerve migration by repeating this procedure over a number of weeks. Further studies on larger sample sizes are required to determine the utility of this approach for the diagnosis and monitoring of diabetic neuropathy.

Exploring the properties of macroscopic fundamental diagram : analysing Brisbane urban network

This thesis explored traffic characteristics at the aggregate level for area-wide traffic monitoring of large urban area. It focused on three aspects: understanding a macroscopic network performance under real-time traffic information provision, measuring traffic performance of a signalised arterial network using available data sets, and discussing network zoning for monitoring purposes in the case of Brisbane, Australia. This work presented the use of probe vehicle data for estimating traffic state variables, and illustrated dynamic features of regional traffic performance of Brisbane. The results confirmed the viability and effectiveness of area-wide traffic monitoring.

Antisocial behaviour orders : Unanticipated directions in social network site development

Abstract: Social network technologies, as we know them today have become a popular feature of everyday life for many people. As their name suggests, their underlying premise is to enable people to connect with each other for a variety of purposes. These purposes however, are generally thought of in a positive fashion. Based on a multi-method study of two online environments, Habbo Hotel and Second Life, which incorporate social networking functionality, we she light on forms of what can be conceptualized as antisocial behaviours and the rationales for these. Such behaviours included: scamming, racist/homophobic attacks, sim attacks, avatar attacks, non-conformance to contextual norms, counterfeiting and unneighbourly behaviour. The rationales for sub behaviours included: profit, fun, status building, network disruption, accidental acts and prejudice. Through our analysis we are able to comment upon the difficulties of defining antisocial behaviour in such environments, particularly when such environments are subject to interpretation vis their use and expected norms. We also point to the problems we face in conducting our public and private lives given the role ICTs are playing in the convergence of these two spaces and also the convergence of ICTs themselves.

Vision-based simultaneous localization and mapping in changing outdoor environments

For robots operating in outdoor environments, a number of factors, including weather, time of day, rough terrain, high speeds, and hardware limitations, make performing vision-based simultaneous localization and mapping with current techniques infeasible due to factors such as image blur and/or underexposure, especially on smaller platforms and low-cost hardware. In this paper, we present novel visual place-recognition and odometry techniques that address the challenges posed by low lighting, perceptual change, and low-cost cameras. Our primary contribution is a novel two-step algorithm that combines fast low-resolution whole image matching with a higher-resolution patch-verification step, as well as image saliency methods that simultaneously improve performance and decrease computing time. The algorithms are demonstrated using consumer cameras mounted on a small vehicle in a mixed urban and vegetated environment and a car traversing highway and suburban streets, at different times of day and night and in various weather conditions. The algorithms achieve reliable mapping over the course of a day, both when incrementally incorporating new visual scenes from different times of day into an existing map, and when using a static map comprising visual scenes captured at only one point in time. Using the two-step place-recognition process, we demonstrate for the first time single-image, error-free place recognition at recall rates above 50% across a day-night dataset without prior training or utilization of image sequences. This place-recognition performance enables topologically correct mapping across day-night cycles.

Efficiently capturing large, complex cultural heritage sites with a handheld mobile 3D laser mapping system

Accurate three-dimensional representations of cultural heritage sites are highly valuable for scientific study, conservation, and educational purposes. In addition to their use for archival purposes, 3D models enable efficient and precise measurement of relevant natural and architectural features. Many cultural heritage sites are large and complex, consisting of multiple structures spatially distributed over tens of thousands of square metres. The process of effectively digitising such geometrically complex locations requires measurements to be acquired from a variety of viewpoints. While several technologies exist for capturing the 3D structure of objects and environments, none are ideally suited to complex, large-scale sites, mainly due to their limited coverage or acquisition efficiency. We explore the use of a recently developed handheld mobile mapping system called Zebedee in cultural heritage applications. The Zebedee system is capable of efficiently mapping an environment in three dimensions by continually acquiring data as an operator holding the device traverses through the site. The system was deployed at the former Peel Island Lazaret, a culturally significant site in Queensland, Australia, consisting of dozens of buildings of various sizes spread across an area of approximately 400 × 250 m. With the Zebedee system, the site was scanned in half a day, and a detailed 3D point cloud model (with over 520 million points) was generated from the 3.6 hours of acquired data in 2.6 hours. We present results demonstrating that Zebedee was able to accurately capture both site context and building detail comparable in accuracy to manual measurement techniques, and at a greatly increased level of efficiency and scope. The scan allowed us to record derelict buildings that previously could not be measured because of the scale and complexity of the site. The resulting 3D model captures both interior and exterior features of buildings, including structure, materials, and the contents of rooms.

Pipeline renewal decision support : a case study using LinEAR in a water pipeline network

This paper presents a case study for the application of a Linear Engineering Asset Renewal decision support software tool (LinEAR) at a water distribution network in Australia. This case study examines how the LinEAR can assist water utilities to minimise their total pipeline management cost, to make a long-term budget based on mathematically predicted expenditure, and to present calculated evidence for supporting their expenditure requirements. The outcomes from the study on pipeline renewal decision support demonstrate that LinEAR can help water utilities to improve the decision process and save renewal costs over a long-term by providing an optimum renewal schedules. This software can help organisation to accumulate technical knowledge and prediction future impact of the decision using what-if analysis.

Demand Response for Residential Appliances in a Smart Electricity Distribution Network: Utility and Customer Perspectives

This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

Mismatch negativity in recent-onset and chronic schizophrenia : a current source density analysis

Mismatch negativity (MMN) is a component of the event-related potential elicited by deviant auditory stimuli. It is presumed to index pre-attentive monitoring of changes in the auditory environment. MMN amplitude is smaller in groups of individuals with schizophrenia compared to healthy controls. We compared duration-deviant MMN in 16 recent-onset and 19 chronic schizophrenia patients versus age- and sex-matched controls. Reduced frontal MMN was found in both patient groups, involved reduced hemispheric asymmetry, and was correlated with Global Assessment of Functioning (GAF) and negative symptom ratings. A cortically-constrained LORETA analysis, incorporating anatomical data from each individual's MRI, was performed to generate a current source density model of the MMN response over time. This model suggested MMN generation within a temporal, parietal and frontal network, which was right hemisphere dominant only in controls. An exploratory analysis revealed reduced CSD in patients in superior and middle temporal cortex, inferior and superior parietal cortex, precuneus, anterior cingulate, and superior and middle frontal cortex. A region of interest (ROI) analysis was performed. For the early phase of the MMN, patients had reduced bilateral temporal and parietal response and no lateralisation in frontal ROIs. For late MMN, patients had reduced bilateral parietal response and no lateralisation in temporal ROIs. In patients, correlations revealed a link between GAF and the MMN response in parietal cortex. In controls, the frontal response onset was 17 ms later than the temporal and parietal response. In patients, onset latency of the MMN response was delayed in secondary, but not primary, auditory cortex. However amplitude reductions were observed in both primary and secondary auditory cortex. These latency delays may indicate relatively intact information processing upstream of the primary auditory cortex, but impaired primary auditory cortex or cortico-cortical or thalamo-cortical communication with higher auditory cortices as a core deficit in schizophrenia.