Modelling and Developing an Intelligent Road Lighting System Using Power-Line Communication

The development of a suitable system which will control street lighting ballasts depending on traffic flow, communicate data between each street light along the Power-Line and sense passing traffic. This paper offers the methodology of the system, environmental benefits, commercial benefits and safety benefits of such a unique system. It also shows topics that have been researched to date and potential future development paths this research could take

Tensile Response of Adhesively Bonded Composite-to-composite Single-lap Joints in the Presence of Bond Deficiency

This paper studies the quasi-static tensile response of adhesively bonded composite-to-composite single-lap joints in the presence of weak and kissing bonds, as an attempt for characterisation of bond deficiencies likely to occur in polymer composite bonded repair. Cytec FM®94 adhesive film (0.25 mm nominal thickness) was used for all joints to bond two 2mm-thickness carbon fibre polymer composite laminates manufactured from unidirectional Hexcel M21/T800S pre-pregs. Peel-ply surface treatment was used for all joints. The bonds were deteriorated via five methods: pre-curing the centre of bond area prior to the cure of the bond edges, increasing the curing temperature rate, reducing the curing time, and embedding PTFE films over the centre of the bond. For the last method, the studies were carried out by embedding PTFE films on one and two sides of the adhesive film. The bond deterioration was followed by non-destructive inspections using ultrasound C-scanning. The ultimate failure load of the joints with defected bonds (i.e. weak and kissing bonds) was measured and compared to that of the joints with no defect (i.e. good bonds). It was found that rapid curing and short-time curing reduces more than 50% of the load carrying capacity of the single-lap joins in tension while the joints with weak bonds introduced by pre-curing of a large area of the bond (>60%) can take up more than 65% of the ultimate load of the joint with good bond. Also, optical microscopy of the bond surfaces after failure showed changes in failure type for the rapid and short-time cure, strongly correlated with their significant failure load reduction

Mechanical performance of composite bonded joints in the presence of localised process-induced zero-thickness defects

Processing parameters and environmental conditions can introduce variation into the performance of adhesively bonded joints. The effect of such variation on the mechanical performance of the joints is not well understood. Moreover, there is no validated nondestructive inspection (NDI) available to ensure bond integrity post-process and in-service so as to guarantee initial and continued airworthiness in aerospace sector. This research studies polymer bond defects produced in the laboratory scale single-lap composite-to-composite joints that may represent the process-induced defects occurring in actual processing scenarios such as composite joining and repair in composite aircrafts. The effect of such defects on the degradation of a joint's mechanical performance is then investigated via quasi-static testing in conjunction with NDI ultrasonic C-scanning and pulsed thermography. This research is divided into three main sections: 1- manufacturing carbon fibre-reinforced composite joints containing representative nearly zero-thickness bond defects, 2- mechanical testing of the composite joints, and 3- assessment of the NDI capability for detection of the bond defects in such joints

Artificial immune systems can find arbitrarily good approximations for the NP-hard number partitioning problem

Typical artificial immune system (AIS) operators such as hypermutations with mutation potential and ageing allow to efficiently overcome local optima from which evolutionary algorithms (EAs) struggle to escape. Such behaviour has been shown for artificial example functions constructed especially to show difficulties that EAs may encounter during the optimisation process. However, no evidence is available indicating that these two operators have similar behaviour also in more realistic problems. In this paper we perform an analysis for the standard NP-hard Partition problem from combinatorial optimisation and rigorously show that hypermutations and ageing allow AISs to efficiently escape from local optima where standard EAs require exponential time. As a result we prove that while EAs and random local search (RLS) may get trapped on 4/3 approximations, AISs find arbitrarily good approximate solutions of ratio (1+) within n(−(2/)−1)(1 − )−2e322/ + 2n322/ + 2n3 function evaluations in expectation. This expectation is polynomial in the problem size and exponential only in 1/

When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms

We present a time complexity analysis of the Opt-IA artificial immune system (AIS). We first highlight the power and limitations of its distinguishing operators (i.e., hypermutations with mutation potential and ageing) by analysing them in isolation. Recent work has shown that ageing combined with local mutations can help escape local optima on a dynamic optimisation benchmark function. We generalise this result by rigorously proving that, compared to evolutionary algorithms (EAs), ageing leads to impressive speed-ups on the standard Image 1 benchmark function both when using local and global mutations. Unless the stop at first constructive mutation (FCM) mechanism is applied, we show that hypermutations require exponential expected runtime to optimise any function with a polynomial number of optima. If instead FCM is used, the expected runtime is at most a linear factor larger than the upper bound achieved for any random local search algorithm using the artificial fitness levels method. Nevertheless, we prove that algorithms using hypermutations can be considerably faster than EAs at escaping local optima. An analysis of the complete Opt-IA reveals that it is efficient on the previously considered functions and highlights problems where the use of the full algorithm is crucial. We complete the picture by presenting a class of functions for which Opt-IA fails with overwhelming probability while standard EAs are efficient

Robust optimization over time by learning problem space characteristics

Robust optimization over time is a new way to tackle dynamic optimization problems where the goal is to find solutions that remain acceptable over an extended period of time. The state-of-the-art methods in this domain try to identify robust solutions based on their future predicted fitness values. However, predicting future fitness values is difficult and error prone. In this paper, we propose a new framework based on a multi-population method in which sub-populations are responsible for tracking peaks and also gathering characteristic information about them. When the quality of the current robust solution falls below the acceptance threshold, the algorithm chooses the next robust solution based on the collected information. We propose four different strategies to select the next solution. The experimental results on benchmark problems show that our newly proposed methods perform significantly better than existing algorithms

M-atom conductance oscillations of a metallic quantum wire

The electron transport through a monoatomic metallic wire connected to leads is investigated using the tight-binding Hamiltonian and Green's function technique. Analytical formulas for the transmittance are derived and M-atom oscillations of the conductance versus the length of the wire are found. Maxima of the transmittance function versus the energy, for the wire consisted of N atoms, determine the (N+1) period of the conductance. The periods of conductance oscillations are discussed and the local and average quantum wire charges are presented. The average charge of the wire is linked with the period of the conductance oscillations and it tends to the constant value as the length of the wire increases. For M-atom periodicity there are possible (M-1) average occupations of the wire states.Comment: 8 pages, 5 figures. J.Phys.: Condens. matter (2005) accepte

Adaptive control of sub-populations in evolutionary dynamic optimization

Multi-population methods are highly effective in solving dynamic optimization problems. Three factors affect this significantly: the exclusion mechanisms to avoid the convergence to the same peak by multiple sub-populations, the resource allocation mechanism which assigns the computational resources to the sub-populations, and the control mechanisms to adaptively adjust the number of sub-populations by considering the number of optima and available computational resources. In the existing exclusion mechanisms, when the distance (i.e. the distance between their best found positions) between two sub-populations becomes less than a predefined threshold, the inferior one will be removed/reinitialized. However, this leads to incapability of algorithms in covering peaks/optima that are closer than the threshold. Moreover, despite the importance of resource allocation due to the limited available computational resources between environmental changes, it has not been well studied in the literature. Finally, the number of sub-populations should be adapted to the number of optima. However, in most existing adaptive multi-population methods, there is no predefined upper bound for generating sub-populations. Consequently, in problems with large numbers of peaks, they can generate too many subpopulations sharing limited computational resources. In this paper, a multi-population framework is proposed to address the aforementioned issues by using three adaptive approaches: subpopulation generation, double-layer exclusion, and computational resource allocation. The experimental results demonstrate the superiority of the proposed framework over several peer approaches in solving various benchmark problems