A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.

Audit of mathematics learning support in Ireland in 2015 – the key findings

In 2015 the Irish Mathematics Learning Support Network (IMLSN) commissioned a comprehensive audit of the extent and nature of mathematics learning support (MLS) provision on the island of Ireland. An online survey was sent to 32 institutions, including universities, institutes of technology, further education and teacher training colleges, and a 97% response rate was achieved. While the headline figure – 84% of institutions that responded to the survey provide MLS – sounds good, deeper analysis reveals that the true state of MLS is not so solid. For example, in 25% of institutions offering MLS, only five hours per week (at most) of physical MLS are available, while in 20% of institutions the service is provided by only one or two staff members. Furthermore, training of tutors is minimal or non-existent in at least half of the institutions offering MLS. The results provide an illuminating picture, however, identifying the true state of MLS in Ireland is beneficial only if it informs developments in the years ahead. This talk will present some of the findings of the survey in more depth along with conclusions and recommendations. Key among these is the need for institutions to recognise MLS as a vital element of mathematics teaching and learning strategy at third level and devote the necessary resources to facilitate the provision of a service which can grow and adapt to meet student requirements.

Service user and carer participation in an endoscopy nursing programme

This article reports an initiative to improve students' insight into service user and carer experience of endoscopy, particularly those with severe disability, such as spinal cord injury. This insight has the potential to improve the information provided and level of person-centred care in an endoscopy service. It was evident in the feedback from the classroom encounter that the teaching and learning strategy had a positive outcome, which will allow us to integrate the approach into future curriculum development and delivery, bringing the lived experience from the service user and carer perspective into the classroom. Students engaged in discussion and used their reflective skills to develop sensitivity to those with physical disability and complex needs requiring endoscopy procedures.

On Theft: Teaching Poetry Composition to Undergraduates

This chapter discusses the potential uses of both found poetry and imitiation poetry as teaching material for undergraduate creative poetry students.

Building capacity in palliative care for personal support workers in long-term care through experiential learning

Background

Providing palliative care in long-term care (LTC) homes is an area of growing importance. As a result, attention is being given to exploring effective palliative care learning strategies for personal support workers (PSWs) who provide the most hands-on care to LTC residents.

The purpose of this intervention was to explore hospice visits as an experiential learning strategy to increase the capacity of PSWs in palliative care, specifically related to their new learning, and how they anticipated this experience changed their practices in LTC.

This study utilised a qualitative descriptive design.

Eleven PSWs from four Ontario LTC homes were sent to their local hospice to shadow staff for one to two days. After the visit, PSWs completed a questionnaire with open-ended questions based on critical reflection. Data were analysed using thematic content analysis.

PSWs commented on the extent of resident-focused care at the hospice and how palliative care interventions were tailored to meet the needs of residents. PSWs were surprised with the lack of routine at the hospice but felt that hospice staff prioritised their time effectively in order to meet family and client care needs. Some PSWs were pleased to see how well integrated the PSW role is on the community hospice team without any hierarchical relationships. Finally, PSWs felt that other LTC staff would benefit from palliative care education and becoming more comfortable with talking about death and dying with other staff, residents and family members.

This study highlighted the benefits of PSWs attending a hospice as an experiential learning strategy. Future work is needed to evaluate this strategy using more rigorous designs as a way to build capacity within PSWs to provide optimal palliative care for LTC residents and their family members.

PSWs need to be recognised as important members within the interdisciplinary team. PSWs who shadow staff at hospices view this experience as a positive strategy to meet their learning needs related to palliative care.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Transcending Boundaries of Creativity: Active learning in the Design Studio

This essay investigates the changing dynamics of interaction and paradigm of communication in the design studio. It analyses the process of practical implementation of interactive tools in architectural education which placed the
diversity of students’ cultural experiences, contextual awareness and individual interests as crucial resource for design innovation and inquiry. Building on Brian Lawson’s thesis on creativity in design thinking, this research project undertook
comprehensive investigation of students’ satisfaction of their roles in the studio and the room for liberal thought they are given to elaborate on genuine approach to architectural matters. The cyclical development of interactive learning strategy is explored through two different settings: first, it analyses architectural students’ position as passive/active in the studio, considering their relationships with tutors’ ideals; second, it reports on empirical strategy of students-led workshops at British schools of architecture, during which students have taken the lead of their creative design agenda. The practical implementation of interactive learning tools proved influential in helping students to personalize their design direction and to build a sense of confidence and independence.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

The determinants of knowledge transfer within collaborative new product development projects

Purpose – This paper explores the factors which determine the degree of knowledge transfer in inter-firm new product development projects. We test a theoretical model exploring how inter-firm knowledge transfer is enabled or hindered by a buyer’s learning intent, the degree of supplier protectiveness, inter-firm knowledge ambiguity, and absorptive capacity. Design/methodology/approach – A sample of 153 R&D intensive manufacturing firms in the UK automotive, aerospace, pharmaceutical, electrical, chemical, and general manufacturing industries were used to test the framework. Two-step structural equation modeling in AMOS 7.0 was used to analyse the data. Findings – Our results indicate that a buyer’s learning intent increases inter-firm knowledge transfer, but also acts as an incentive for suppliers to protect their knowledge. Such defensive measures increase the degree of inter-firm knowledge ambiguity, encouraging buyer firms to invest in absorptive capacity as a means to interpret supplier knowledge, but also increase the degree of knowledge transfer. Practical implications – Our paper illustrates the effects of focusing on acquisition, rather than accessing, supplier technological knowledge. We show that an overt learning strategy can be detrimental to knowledge transfer between buyer-supplier, as supplier’s react by restricting the flow of information. Organisations are encouraged to consider this dynamic when engaging in multi-organisational new product development projects. Originality/value – This paper examines the dynamics of knowledge transfer within inter-firm NPD projects, showing how transfer is influenced by the buyer firm’s learning intention, supplier’s response, characteristics of the relationship and knowledge to be transferred.

SPHERE: Stormont Parliamentary Hansards: Embedded in Research and Education

SPHERE (Stormont Parliamentary Hansards: Embedded in Research and Education) was a JISC-funded project based at King’s College, London and Queen’s University, Belfast, working in Partnership with the Northern Ireland Assembly Library, and the NIA Official Report (Hansard). Its purpose was to assess the use, value and impact of The Stormont Papers digital resource, and to use the results of this assessment to make recommendations for a series of practical approaches to embed the resource within teaching, learning and research among the wider user community. The project began in November 2010 and was concluded in April 2010.

A series of formal reports on the project are published by JISC online at http://www.jisc.ac.uk/whatwedo/programmes/digitisation/impactembedding/sphere.aspx

SPHERE Impact analysis summary
Portable Document Format
SPHERE interviews report
SPHERE Outreach use case
SPHERE research use case
SPHERE teaching use_case
SPHERE web survey report
SPHERE web analysis

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.