System oriented neural networks - Problem formulation, methodology and application

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful

Consideration of problem formulation and option assessment (PFOA) for environmental risk assessment: Bt cotton in Vietnam.

2008

A heuristic algorithm for the Capacitated Vehicle Routing Problem with Synchronized Pick-ups and Drop-offs : a case study for medications delivery and supervision in DR Congo

Dans des contextes de post-urgence tels que le vit la partie occidentale de la République Démocratique du Congo (RDC), l’un des défis cruciaux auxquels font face les hôpitaux ruraux est de maintenir un niveau de médicaments essentiels dans la pharmacie. Sans ces médicaments pour traiter les maladies graves, l’impact sur la santé de la population est significatif. Les hôpitaux encourent également des pertes financières dues à la péremption lorsque trop de médicaments sont commandés. De plus, les coûts du transport des médicaments ainsi que du superviseur sont très élevés pour les hôpitaux isolés ; les coûts du transport peuvent à eux seuls dépasser ceux des médicaments. En utilisant la province du Bandundu, RDC pour une étude de cas, notre recherche tente de déterminer la faisabilité (en termes et de la complexité du problème et des économies potentielles) d’un problème de routage synchronisé pour la livraison de médicaments et pour les visites de supervision. Nous proposons une formulation du problème de tournées de véhicules avec capacité limitée qui gère plusieurs exigences nouvelles, soit la synchronisation des activités, la préséance et deux fréquences d’activités. Nous mettons en œuvre une heuristique « cluster first, route second » avec une base de données géospatiales qui permet de résoudre le problème. Nous présentons également un outil Internet qui permet de visualiser les solutions sur des cartes. Les résultats préliminaires de notre étude suggèrent qu’une solution synchronisée pourrait offrir la possibilité aux hôpitaux ruraux d’augmenter l’accessibilité des services médicaux aux populations rurales avec une augmentation modique du coût de transport actuel.

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: a THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.

An algorithm to median shortest path problem (MSPP) in the design of urban transportation networks

This paper proposes an efficient solution algorithm for realistic multi-objective median shortest path problems in the design of urban transportation networks. The proposed problem formulation and solution algorithm to median shortest path problem is based on three realistic objectives via route cost or investment cost, overall travel time of the entire network and total toll revenue. The proposed solution approach to the problem is based on the heuristic labeling and exhaustive search technique in criteria space and solution space of the algorithm respectively. The first labels each node in terms of route cost and deletes cyclic and infeasible paths in criteria space imposing cyclic break and route cost constraint respectively. The latter deletes dominated paths in terms of objectives vector in solution space in order to identify a set of Pareto optimal paths. The approach, thus, proposes a non-inferior solution set of Pareto optimal paths based on non-dominated objective vector and leaves the ultimate decision to decision-makers for purpose specific final decision during applications. A numerical experiment is conducted to test the proposed algorithm using artificial transportation network. Sensitivity analyses have shown that the proposed algorithm is advantageous and efficient over existing algorithms to find a set of Pareto optimal paths to median shortest paths problems.

A boundary value problem approach to too-short arc optical track association

Given a short-arc optical observation with estimated angle-rates, the admissible region is a compact region in the range / range-rate space defined such that all likely and relevant orbits are contained within it. An alternative boundary value problem formulation has recently been proposed where range / range hypotheses are generated with two angle measurements from two tracks as input. In this paper, angle-rate information is reintroduced as a means to eliminate hypotheses by bounding their constants of motion before a more computationally costly Lambert solver or differential correction algorithm is run.

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.