Load flow in electrified railway

Probabilistic load flow techniques have been adopted in AC electrified railways to study the load demand under various train service conditions. This paper highlights the differences in probabilistic load flow analysis between the usual power systems and power supply systems in AC railways; discusses the possible difficulties in problem formulation and presents the link between train movement and the corresponding power demand for load flow calculation.

Note on similarity solutions for viscous flow over an impermeable and non-linearly (quadratic) stretching sheet

Similarity solutions for flow over an impermeable, non-linearly (quadratic) stretching sheet were studied recently by Raptis and Perdikis (Int. J. Non-linear Mech. 41 (2006) 527–529) using a stream function of the form ψ=αxf(η)+βx2g(η). A fundamental error in their problem formulation is pointed out. On correction, it is shown that similarity solutions do not exist for this choice of ψ

Omnidirectional bearing-only see-and-avoid for small aerial robots

Motivated by the growing interest in unmanned aerial system’s applications in indoor and outdoor settings and the standardisation of visual sensors as vehicle payload. This work presents a collision avoidance approach based on omnidirectional cameras that does not require the estimation of range between two platforms to resolve a collision encounter. It will achieve a minimum separation between the two vehicles involved by maximising the view-angle given by the omnidirectional sensor. Only visual information is used to achieve avoidance under a bearing-only visual servoing approach. We provide theoretical problem formulation, as well as results from real flight using small quadrotors.

Numerical Characterization of Nanowires

In this chapter, we will present a contemporary review of the hitherto numerical characterization of nanowires (NWs). The bulk of the research reported in the literatures concern metallic NWs including Al, Cu, Au, Ag, Ni, and their alloys NWs. Research has also been reported for the investigation of some nonmetallic NWs, such as ZnO, GaN, SiC, SiO2. A plenty of researches have been conducted regarding the numerical investigation of NWs. Issues analyzed include structural changes under different loading situations, the formation and propagation of dislocations, and the effect of the magnitude of applied loading on deformation mechanics. Efforts have also been made to correlate simulation results with experimental measurements. However, direct comparisons are difficult since most simulations are carried out under conditions of extremely high strain/loading rates and small simulation samples due to computational limitations. Despite of the immense numerical studies of NWs, a significant work still lies ahead in terms of problem formulation, interpretation of results, identification and delineation of deformation mechanisms, and constitutive characterization of behavior. In this chapter, we present an introduction of the commonly adopted experimental and numerical approaches in studies of the deformation of NWs in Section 1. An overview of findings concerning perfect NWs under different loading situations, such as tension, compression, torsion, and bending are presented in Section 2. In Section 3, we will detail some recent results from the authors’ own work with an emphasis on the study of influences from different pre-existing defect on NWs. Some thoughts on future directions of the computational mechanics of NWs together with Conclusions will be given in the last section.

Vision based control for micro aerial vehicles : Application to sense and avoid

This work presents a collision avoidance approach based on omnidirectional cameras that does not require the estimation of range between two platforms to resolve a collision encounter. Our method achieves minimum separation between the two vehicles involved by maximising the view-angle given by the omnidirectional sensor. Only visual information is used to achieve avoidance under a bearing- only visual servoing approach. We provide theoretical problem formulation, as well as results from real flights using small quadrotors

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Numerical characterization of the mechanical properties of metal nanowires

Nanowires (NWs) have attracted appealing and broad application owing to their remarkable mechanical, optical, electrical, thermal and other properties. To unlock the revolutionary characteristics of NWs, a considerable body of experimental and theoretical work has been conducted. However, due to the extremely small dimensions of NWs, the application and manipulation of the in situ experiments involve inherent complexities and huge challenges. For the same reason, the presence of defects appears as one of the most dominant factors in determining their properties. Hence, based on the experiments' deficiency and the necessity of investigating different defects' influence, the numerical simulation or modelling becomes increasingly important in the area of characterizing the properties of NWs. It has been noted that, despite the number of numerical studies of NWs, significant work still lies ahead in terms of problem formulation, interpretation of results, identification and delineation of deformation mechanisms, and constitutive characterization of behaviour. Therefore, the primary aim of this study was to characterize both perfect and defected metal NWs. Large-scale molecular dynamics (MD) simulations were utilized to assess the mechanical properties and deformation mechanisms of different NWs under diverse loading conditions including tension, compression, bending, vibration and torsion. The target samples include different FCC metal NWs (e.g., Cu, Ag, Au NWs), which were either in a perfect crystal structure or constructed with different defects (e.g. pre-existing surface/internal defects, grain/twin boundaries). It has been found from the tensile deformation that Young's modulus was insensitive to different styles of pre-existing defects, whereas the yield strength showed considerable reduction. The deformation mechanisms were found to be greatly influenced by the presence of defects, i.e., different defects acted in the role of dislocation sources, and many affluent deformation mechanisms had been triggered. Similar conclusions were also obtained from the compressive deformation, i.e., Young's modulus was insensitive to different defects, but the critical stress showed evident reduction. Results from the bending deformation revealed that the current modified beam models with the considerations of surface effect, or both surface effect and axial extension effect were still experiencing certain inaccuracy, especially for the NW with ultra small cross-sectional size. Additionally, the flexural rigidity of the NW was found to be insensitive to different pre-existing defects, while the yield strength showed an evident decrease. For the resonance study, the first-order natural frequency of the NW with pre-existing surface defects was almost the same as that from the perfect NW, whereas a lower first-order natural frequency and a significantly degraded quality factor was observed for NWs with grain boundaries. Most importantly, the <110> FCC NWs were found to exhibit a novel beat phenomenon driven by a single actuation, which was resulted from the asymmetry in the lattice spacing in the (110) plane of the NW cross-section, and expected to exert crucial impacts on the in situ nanomechanical measurements. In particular, <110> Ag NWs with rhombic, truncated rhombic, and triangular cross-sections were found to naturally possess two first-mode natural frequencies, which were envisioned with applications in NEMS that could operate in a non-planar regime. The torsion results revealed that the torsional rigidity of the NW was insensitive to the presence of pre-existing defects and twin boundaries, but received evident reduction due to grain boundaries. Meanwhile, the critical angle decreased considerably for defected NWs. This study has provided a comprehensive and deep investigation on the mechanical properties and deformation mechanisms of perfect and defected NWs, which will greatly extend and enhance the existing knowledge and understanding of the properties/performance of NWs, and eventually benefit the realization of their full potential applications. All delineated MD models and theoretical analysis techniques that were established for the target NWs in this research are also applicable to future studies on other kinds of NWs. It has been suggested that MD simulation is an effective and excellent tool, not only for the characterization of the properties of NWs, but also for the prediction of novel or unexpected properties.

Understanding the fabric of social interactions for ridesharing through mining social networking sites

The design of applications for dynamic ridesharing or carpooling is often formulated as a matching problem of connecting people with an aligned set of transport needs within a reasonable interval of time and space. This problem formulation relegates social connections to being secondary factors. Technology assisted ridesharing applications that put the matching problem first have revealed that they suffer from being unable to address the factor of social comfort, even after adding friend features or piggybacking on social networking sites. This research aims to understand the fabric of social interactions through which ridesharing happens. We take an online observation approach in order to understand the fabric of social interactions for ridesharing that is happening in highly subscribed online groups of local residents. This understanding will help researchers to identify design challenges and opportunities to support ridesharing in local communities. This paper contributes a fundamental understanding of how social interactions and social comfort precede rideshare requests in local communities.

Thrust allocation with linear constrained quadratic cost function

In this paper, a method of thrust allocation based on a linearly constrained quadratic cost function capable of handling rotating azimuths is presented. The problem formulation accounts for magnitude and rate constraints on both thruster forces and azimuth angles. The advantage of this formulation is that the solution can be found with a finite number of iterations for each time step. Experiments with a model ship are used to validate the thrust allocation system.

Mobile robot navigation as a planar visual servoing problem

This paper demonstrates some interesting connections between the hitherto disparate fields of mobile robot navigation and image-based visual servoing. A planar formulation of the well-known image-based visual servoing method leads to a bearing-only navigation system that requires no explicit localization and directly yields desired velocity. The well known benefits of image-based visual servoing such as robustness apply also to the planar case. Simulation results are presented.

Mobile robot navigation as a planar visual servoing problem

This paper demonstrates some interesting connections between the hitherto disparate fields of mobile robot navigation and image-based visual servoing. A planar formulation of the well-known image-based visual servoing method leads to a bearing-only navigation system that requires no explicit localization and directly yields desired velocity. The well known benefits of image-based visual servoing such as robustness apply also to the planar case. Simulation results are presented.

A fast tabu search algorithm for the group shop scheduling problem

Three types of shop scheduling problems, the flow shop, the job shop and the open shop scheduling problems, have been widely studied in the literature. However, very few articles address the group shop scheduling problem introduced in 1997, which is a general formulation that covers the three above mentioned shop scheduling problems and the mixed shop scheduling problem. In this paper, we apply tabu search to the group shop scheduling problem and evaluate the performance of the algorithm on a set of benchmark problems. The computational results show that our tabu search algorithm is typically more efficient and faster than the other methods proposed in the literature. Furthermore, the proposed tabu search method has found some new best solutions of the benchmark instances.

A flexible optimisation model for the railway crew scheduling problem

Addressing the Crew Scheduling Problem (CSP) in transportation systems can be too complex to capture all details. The designed models usually ignore or simplify features which are difficult to formulate. This paper proposes an alternative formulation using a Mixed Integer Programming (MIP) approach to the problem. The optimisation model integrates the two phases of pairing generation and pairing optimisation by simultaneously sequencing trips into feasible duties and minimising total elapsed time of any duty. Crew scheduling constraints in which the crew have to return to their home depot at the end of the shift are included in the model. The flexibility of this model comes in the inclusion of the time interval of relief opportunities, allowing the crew to be relieved during a finite time interval. This will enhance the robustness of the schedule and provide a better representation of real-world conditions.

A lagrangian formulation for statistical fluid registration

We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithmdeveloped in [4], and used a Lagrangian framework to incorporate 0 th and 1st order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensorbased distances to compare the power and the robustness of the algorithms.