Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results

Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance

An incremental learning framework to enhance teaching by demonstration based on multimodal sensor fusion

Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental learning framework based on a teleoperation strategy that can enable the robot to reproduce the demonstration task accurately. The multimodal demonstration data are collected from two different kinds of sensors in the demonstration phase. Then, the Kalman filter (KF) and dynamic time warping (DTW) algorithms are used to preprocessing the data for the multiple sensor signals. The KF algorithm is mainly used to fuse sensor data of different modalities, and the DTW algorithm is used to align the data in the same timeline. The preprocessed demonstration data are further trained and learned by the incremental learning network and sent to a Baxter robot for reproducing the task demonstrated by the human. Comparative experiments have been performed to verify the effectiveness of the proposed framework

Constraint Programming for Wireless Sensor Networks

In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that monitor physical and environmental conditions, such as temperature, humidity, pollution, etc. If required, nodes in a WSN can perform actions to affect the environment. WSNs present an interesting and challenging field of research due to the distributed nature of the network and the limited resources of the nodes. It is necessary for a node in a WSN to be small to enable easy deployment in an environment and consume as little energy as possible to prolong its battery lifetime. There are many challenges in WSNs, such as programming a large number of nodes, designing communication protocols, achieving energy efficiency, respecting limited bandwidth, and operating with limited memory. WSNs are further constrained due to the deployment of the nodes in indoor and outdoor environments and obstacles in the environment. In this dissertation, we study some of the fundamental optimisation problems related to the programming, coverage, mobility, data collection, and data loss of WSNs, modelled as standalone optimisation problems or as optimisation problems integrated with protocol design. Our proposed solution methods come from various fields of research including constraint programming, integer linear programming, heuristic-based algorithms, and data inference techniques.ProFu

Improved Nano-structures in hydrolysis-derived titanium dioxide particles for dye-sensitized solar cell applications

This thesis presents a study on the modification of titanium dioxide (TiO2) nanoparticle preparation through two hydrolysis routines: sol – gel and hydrothermal and the use of dopants for dye-sensitized solar cell (DSSC) application. Certain TiO2 characteristics, such as particle size, morphology, surface area, and phase structure, are crucial for obtaining superior power conversion efficiency in dye sensitized solar cells. Due to the agglomeration problem of the sol – gel process, this method has been found to make it difficult to control particle sizes with high surface area. In this work, we report a simple approach to improve the DSSC by controlling the degree of aggregation through zeta potential analysis. We found that different aqueous colloidal conditions, i.e., potential of hydrogen (pH), water/titanium alkoxide (titanium isopropoxide) ratio, and surface charge, obviously led to different particle sizes in the range of 10 to 500 nm. The stable sol solution was used for the blocking layer as well. Power conversion efficiency of 7.15% was obtained by using anatase TiO2 optimised to 10-20 nm in particle size with a compact blocking layer and a scattering layer. The scattering layer was made of particles with an average size of 100-200 nm, which were obtained through the sol – gel method by controlling the reaction parameters. Using the stable sol solution, one-dimensional (1D) TiO2 nanostructures were prepared using the electrospinning method to verify their potential for use as the photoelectrode of DSSCs. The achieved 1D mesoporous nanofibers were 100 nm in diameter and 10-20 μm in length, and were composed of aggregated anatase nanoparticles 20-30 nm in size. The employment of these novel 1D mesoporous nanofibers significantly improved the dye loading and light scattering of the DSSC photoanode, and resulted in conversion cell efficiency of 6.64%, corresponding to a ~65% enhancement over the Degussa P25 reference photoanode. Electrochemical impedance spectroscopy was used to investigate the electron transfer through the photoanode, and it showed improved charge transport and electron diffusion through the electrospun TiO2 nanofibers. The effects of phase structure on the photovoltaic performance were also investigated. To investigate the phase structure effects, it is very important to have the different phase structures in the same particle sizes. In this work, we controlled the phase transfer rate by controlling the synthesis parameters. The obtained nanoparticles were of the same size, but with different phase structures. The nanoparticles containing 75% anatase phase with 25% brookite phase showed the best photovoltaic performance, with power conversion efficiency of 6.8 % for operation with a TiO2 blocking layer. The hydrothermal synthesis method was used to synthesize oriented, single crystalline, one-dimensional TiO2 nanostructures. In this study, a precisely controlled procedure was used to synthesize 3D dendritic and 1D TiO2 nanostructures by tuning the hydrolysis rate of the titanium precursor. Based on this innovation, oriented 1D rutile TiO2 nanostructure arrays with continually adjustable morphologies, from nanorods (NRODs) to nanoribbons (NRIBs), and then nanowires (NWs), as well as transition state morphologies, were successfully synthesized. The photovoltaic performance tests showed that the photoanode constructed from the oriented NRIB arrays possessed not only a high surface area for sufficient dye loading and better light scattering in the visible light range than for the other morphologies, but also a wider band-gap and a higher conduction band edge, with more than 200% improvement in power conversion efficiency in dye-sensitized solar cells (DSSCs) compared with the NROD morphology. Titanium dioxide nanoparticles doped with nitrogen were prepared through the sol – gel method. Compared to undoped pure anatase titanium dioxide nanoparticles with the same particle size, the nitrogen doped titanium dioxide showed improved photovoltaic performance. Electrochemical impedance spectroscopy of cells with N doped TiO2 and pure TiO2 indicated that the charge transport of the photoelectrode was improved after doping with nitrogen. As a result, a photoelectric conversion efficiency of 6.8% was obtained for N doped TiO2 photoanode. In summary, the results show the systematic influence that the synthesis conditions have on the crystalline structure of titanium dioxide in such aspects as particle size, phase structure, surface area, and morphology. Greater attention to the synthesis of TiO2 for DSSCs showed how significantly the synthesis conditions can improve the photovoltaic performances

Dynamic Demand-Capacity Balancing for Air Traffic Management : Using Constraint-Based Local Search

Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance

Energy-Efficient Sensor Selection for Data Quality and Load Balancing in Wireless Sensor Networks

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