Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system

For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

The clinical effectiveness of individual behaviour change interventions to reduce risky sexual behaviour after a negative human immunodeficiency virus test in men who have sex with men: systematic and realist reviews and intervention development

Background: Men who have sex with men (MSM) experience significant inequalities in health and well-being. They are the group in the UK at the highest risk of acquiring a human immunodeficiency virus (HIV) infection. Guidance relating to both HIV infection prevention, in general, and individual-level behaviour change interventions, in particular, is very limited. Objectives: To conduct an evidence synthesis of the clinical effectiveness of behaviour change interventions to reduce risky sexual behaviour among MSM after a negative HIV infection test. To identify effective components within interventions in reducing HIV risk-related behaviours and develop a candidate intervention. To host expert events addressing the implementation and optimisation of a candidate intervention. Data sources: All major electronic databases (British Education Index, BioMed Central, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Educational Resource Index and Abstracts, Health and Medical Complete, MEDLINE, PsycARTICLES, PsycINFO, PubMed and Social Science Citation Index) were searched between January 2000 and December 2014. Review methods: A systematic review of the clinical effectiveness of individual behaviour change interventions was conducted. Interventions were examined using the behaviour change technique (BCT) taxonomy, theory coding assessment, mode of delivery and proximity to HIV infection testing. Data were summarised in narrative review and, when appropriate, meta-analysis was carried out. Supplemental analyses for the development of the candidate intervention focused on post hoc realist review method, the assessment of the sequential delivery and content of intervention components, and the social and historical context of primary studies. Expert panels reviewed the candidate intervention for issues of implementation and optimisation. Results: Overall, trials included in this review (n = 10) demonstrated that individual-level behaviour change interventions are effective in reducing key HIV infection risk-related behaviours. However, there was considerable clinical and methodological heterogeneity among the trials. Exploratory meta-analysis showed a statistically significant reduction in behaviours associated with high risk of HIV transmission (risk ratio 0.75, 95% confidence interval 0.62 to 0.91). Additional stratified analyses suggested that effectiveness may be enhanced through face-to-face contact immediately after testing, and that theory-based content and BCTs drawn from ‘goals and planning’ and ‘identity’ groups are important. All evidence collated in the review was synthesised to develop a candidate intervention. Experts highlighted overall acceptability of the intervention and outlined key ways that the candidate intervention could be optimised to enhance UK implementation. Limitations: There was a limited number of primary studies. All were from outside the UK and were subject to considerable clinical, methodological and statistical heterogeneity. The findings of the meta-analysis must therefore be treated with caution. The lack of detailed intervention manuals limited the assessment of intervention content, delivery and fidelity. Conclusions: Evidence regarding the effectiveness of behaviour change interventions suggests that they are effective in changing behaviour associated with HIV transmission. Exploratory stratified meta-analyses suggested that interventions should be delivered face to face and immediately after testing. There are uncertainties around the generalisability of these findings to the UK setting. However, UK experts found the intervention acceptable and provided ways of optimising the candidate intervention. Future work: There is a need for well-designed, UK-based trials of individual behaviour change interventions that clearly articulate intervention content and demonstrate intervention fidelity

Optimal coordination of electric vehicle charging and photovoltaic power curtailment in unbalanced low voltage networks: An experimental case

This study introduces a quadratic programming-based optimisation method to coordinate electric vehicle (EV) charging and photovoltaic (PV) curtailment in unbalanced low voltage (LV) networks. The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non-linear models and surpassing the limitations of local solutions derived from meta-heuristics algorithms reported in the literature. The coordination is carried out through a centralised controller installed at the header of the LV feeder. The objective of the proposed strategy is to minimise the power curtailment of all PV systems and maximise the power delivered to all EVs by optimising at every time step a suitable setpoint for the PV units and the charging rate of each EV connected without surpassing network constraints. A new energy-boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival-and-desired energy states of the vehicle to compute its charging trajectory optimally. The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment, making use of two commercial EVs and a PV inverter in a Power Hardware-in-the-Loop setup.This work was supported by TECNALIA funding through the 2017 PhD scholarship programme. TECNALIA is a "CERVERA Technology Centre of Excellence" recognised by the Ministry of Science and Innovation. The authors also would like to thank the Basque Government (GISEL research group IT1191‐19) and the UPV/EHU (GISEL research group 18/181) for their support in this work, as well as the TU Dortmund University for allowing the use of its facilities to obtain the results described in this paper. Dr. Kalle Rauma would like to thank the support of the German Federal Ministry of Transport and Digital Infrastructure through the project Parken und Laden in der Stadt (03EMF0203). The work of Kalle Rauma was also supported by the European Union's Horizon 2020 Research and Innovation Programme through SENDER project under grant agreement no. 95775

The wave energy converter control competition (WECCCOMP): Wave energy control algorithms compared in both simulation and tank testing

The wave energy control competition established a benchmark problem which was offered as an open challenge to the wave energy system control community. The competition had two stages: In the first stage, competitors used a standard wave energy simulation platform (WEC-Sim) to evaluate their controllers while, in the second stage, competitors were invited to test their controllers in a real-time implementation on a prototype system in a wave tank. The performance function used was based on converted energy across a range of standard sea states, but also included aspects related to economic performance, such as peak/average power, peak force, etc. This paper compares simulated and experimental results and, in particular, examines if the results obtained in a linear system simulation are borne out in reality. Overall, within the scope of the device tested, the range of sea states employed, and the performance metric used, the conclusion is that high-performance WEC controllers work well in practice, with good carry-over from simulation to experimentation. However, the availability of a good WEC mathematical model is deemed to be crucial

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Improvement of existing coal fired thermal power plants performance by control systems modifications

This paper presents possibilities of implementation of advanced combustion control concepts in selected Western Balkan thermal power plant, and particularly those based on artificial intelligence as part of primary measures for nitrogen oxide reduction in order to optimise combustion and to increase plant efficiency. Both considered goals comply with environmental quality standards prescribed in large combustion plant directive. Due to specific characterisation of Western Balkan power sector these goals should be reached by low cost and easily implementable solution. Advanced self-learning controller has been developed and the effects of advanced control concept on combustion process have been analysed using artificial neural-network based parameter prediction model. (c) 2013 Elsevier Ltd. All rights reserved