42 research outputs found
Efficient execution of continuous incoherency bounded queries over multi-source streaming data
No full textOn-line decision making often involves query processing over time-varying data which arrives in the form of data streams from distributed locations. In such environments typically, a user application is interested in the value of some function defined over the data items. For example, the traffic management system can make control decisions based on the observed traffic at major intersections; stock investors can manage their investments based on the value of their portfolios. In this paper we present a system that supports pull based data refresh and query processing techniques where such queries access data from multiple distributed sources. Key challenges in supporting such Continuous Multi-Data Incoherency Bounded Queries lie in minimizing network and source overheads, without loss of fidelity in the query responses provided to users. We address these challenges by using mathematically sound approaches based on Gradient Descent and Constraint Optimization which allow us to adapt the refresh frequencies of the dynamically changing data and adjust the quality of service provided to different users. 1
Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy
No full textPurposeCurrent normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue–sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation.Methods and MaterialsFDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares–logistic regression [FPLS-LR] and functional principal component–logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate–response associations, assessed using bootstrapping.ResultsThe area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models.ConclusionsFPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling
Reconfigurable modular battery pack for electric aircraft
No full textAviation is a growing industry responsible for over 2% of the energy-related CO2 emissions in 2021. To achieve the 'Net Zero Emission by 2050 scenario', the aviation industry is turning towards modern propulsion technologies that reduce carbon and NOx emissions. Research is taking place on many prospective aircraft designs, such as hybrid/turbo-electric powertrains, fuel cell/liquid hydrogen-powered aircraft and fully electric aircraft. Although fully electric aircraft offer the cleanest possible air travel, these aircraft are considered the solution for short-haul flights due to the range extension issue caused by the deficient specific energy of batteries compared to currently used aviation fuel. The electric aircraft concept designers rely on the potential development in battery technology while proposing their designs, and sporadic state-of-the-art battery designs are present concerning electric aviation. The concept of reconfigurable battery packs involves using power switches to modify the arrangement of connected battery cells based on specific requirements. This innovative technique can potentially significantly reduce the weight of battery packs. The primary objective of this thesis was to conduct a comprehensive analysis and comparison between fixed configuration and reconfigurable battery packs in the context of electric aviation. It was imperative first to design these battery packs to facilitate this comparison. Given the limited availability of open data on electric aircraft designs, the power profile was estimated using available reference aircraft specifications and reasonable assumptions. The literature review on power systems in aircraft revealed a significant correlation between system-level voltage and the weight of power cables. This discovery led to estimating an optimal system-level voltage, a critical constraint in battery sizing. For the fixed configuration battery pack, sizing was conducted using both a high-specific energy cell and a high-specific power cell. The design of a reconfigurable battery pack involved strategically leveraging both cell types. This innovative approach created a reconfigurable battery pack capable of dynamically connecting and disconnecting an internal high-specific energy battery pack called the 'primary battery pack' and a high-specific power battery pack known as the 'secondary battery pack' through power switches, allowing them to complement each other during high-power demand phases of flight, such as take-off and climbing.Software simulations were conducted for the validation of this technique. These simulations revealed that the reconfigurable battery pack experienced higher C-rates than the fixed configuration battery pack. Given that higher C-rates can impact battery health by inducing capacity loss over multiple cycles, a preliminary ageing analysis was performed to quantitatively assess the adverse effects of higher C-rates on the reconfigurable battery pack.The results quantified that around 400 kg of potential weight savings is possible by employing reconfigurable battery packs over fixed configuration battery packs at only 0.4% more capacity loss over 500 charging-discharging cycles. The weight savings can be translated into three different scenarios. Firstly, payload weight capacity can be enhanced. Secondly, flying with lesser weight will offset the power profile, saving energy. Lastly, an additional number of cells equivalent to the mass saved can realise the range extension of the electric aircraft.Electrical Engineering | Sustainable Energy Technolog
Active Inference Control for Vehicle Platooning
No full textDue to the increase in traffic, road congestion has gone up. Vehicle platooning is a possible way to increase the capacity of a given road, by decreasing the distance between the vehicles in the platoon. At the moment, the control of vehicle platoons is commonly done using PID controllers. The advantage of this is that it requires little computational resources. With improvements in computing technology in recent years, the possibility of using a more computationally costly method has opened up. But, parallel with that, dealing with inherently unmodeled dynamics and large parameter variations or faults, is a challenging task whilecontrolling any system. Classical control techniques do not provide satisfactory responses in most of the settings, and often external supervision systems have to be designed to handle the faults. Recent research has shown that active inference, a unifying neuroscientific theory of the brain, bares the potential of intrinsically coping with strong uncertainties in the system, mimicking the adaptability capabilities of humans. However, the current state-of-the-art regarding active inference in vehicle platooning is non-existent. This thesis presents a novel active inference controller for adaptive cruise control systems and as a general adaptive fault tolerant solution for control of vehicle platoon. First, we demonstrate the applicability of active inference in classical control scheme in order to control a platoon of vehicles. Second, we verify that the proposed active inference framework is computationally efficient and with high performance against a benchmark model. Third, we access the adaptive properties of the designed framework in presence of large parameter variations and actuator faults. This work reveals that not only active inference is applicable in vehicle platooning, but it also outperforms the benchmark model in some characteristics, and it allows to deal efficiently with parameter variations and actuator faults. This thesis represents a first step towards the implementation of the current state-of-the-art of active inference for vehicle platooning, and it lays the foundations for further research in this direction.Mechanical Engineering | Systems and Contro
Efficient Execution of Continuous Incoherency Bounded Queries over Multi-Source Streaming Data
No full textOn-line decision making often involves query processing over time-varying data which arrives in the form of data streams from distributed locations. In such environments typically, a user application is interested in the value of some function defined over the data items. For example, the traffic management system can make control decisions based on the observed traffic at major intersections; stock investors can manage their investments based on the value of their portfolios. In this paper we present a system that supports pull based data refresh and query processing techniques where such queries access data from multiple distributed sources. Key challenges in supporting such Continuous Multi-Data Incoherency Bounded Queries lie in minimizing network and source overheads, without loss of fidelity in the query responses provided to users. We address these challenges by using mathematically sound approaches based on Gradient Descent and Constraint Optimization which allow us to adapt the refresh frequencies of the dynamically changing data and adjust the quality of service provided to different users
Efficient Execution of Continuous Threshold Queries over Dynamic Web Data
No full textOn-line decision making often involves processing significant amount of time-varying data. Examples of timevarying data available on the Web include financial information such as stock prices and currency exchange rates, real-time traffic, weather information and data from process control applications. In such environments, typically a decision is made whenever some function of the current value of a set of data items satisfies a threshold criterion
