A comparison between randomly alternating imaging, normal laparoscopic imaging, and virtual reality training in laparoscopic psychomotor skill acquisition

Objectives: To evaluate virtual reality as a laparoscopic training device in helping surgeons to automate to the “fulcrum effect” by comparing it to time-matched training programs using randomly alternating images (ie, y-axis inverted and normal laparoscopic) and normal laparoscopic viewing conditions.

Methods: Twenty-four participants (16 females and 8 males), were randomly assigned to minimally invasive surgery virtual reality (MIST VR), randomly alternating (between y-axis inverted and normal laparoscopic images), and normal laparoscopic imaging condition. Participants were requested to perform a 2-minute laparoscopic cutting task before and after training.

Results: In the test trial participants who trained on the MIST VR performed significantly better than those in the normal laparoscopic and randomly alternating imaging conditions.

Conclusion: The results show that virtual reality training may provide faster skill acquisition with particular reference to automation of the fulcrum effect. MIST VR provides a new way of training laparoscopic psychomotor surgical skills.

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.

An Integration Methodology for Automated Recurring Cost Prediction using Digital Manufacturing Technology

The need to account for the effect of design decisions on manufacture and the impact of manufacturing cost on the life cycle cost of any product are well established. In this context, digital design and manufacturing solutions have to be further developed to facilitate and automate the integration of cost as one of the major driver in the product life cycle management. This article is to present an integration methodology for implementing cost estimation capability within a digital manufacturing environment. A digital manufacturing structure of knowledge databases are set out and the ontology of assembly and part costing that is consistent with the structure is provided. Although the methodology is currently used for recurring cost prediction, it can be well applied to other functional developments, such as process planning. A prototype tool is developed to integrate both assembly time cost and parts manufacturing costs within the same digital environment. An industrial example is used to validate this approach.

Hierarchical demand response using dantzig-wolfe decomposition

Demand Side Management (DSM) programmes are designed to shift electrical loads from peak times. Demand Response (DR) algorithms automate this process for controllable loads. DR can be implemented explicitly in terms of Peak to Average Ratio Reduction (PARR), in which case the maximum peak load is minimised over a prediction horizon by manipulating the amount of energy given to controllable loads at different times. A hierarchical predictive PARR algorithm is presented here based on Dantzig-Wolfe decomposition. © 2013 IEEE.

Time Efficient Assessment and Feedback Methods for Large Computer-Aided-Design Cohorts

Timely and individualized feedback on coursework is desirable from a student perspective as it facilitates formative development and encourages reflective learning practice. Faculty however are faced with a significant and potentially time consuming challenge when teaching larger cohorts if they are to provide feedback which is timely, individualized and detailed. Additionally, for subjects which assess non-traditional submissions, such as Computer-Aided-Design (CAD), the methods for assessment and feedback tend not to be so well developed or optimized. Issues can also arise over the consistency of the feedback provided. Evaluations of Computer-Assisted feedback in other disciplines (Denton et al, 2008), (Croft et al, 2001) have shown students prefer this method of feedback to traditional “red pen” marking and also that such methods can be more time efficient for faculty.
Herein, approaches are described which make use of technology and additional software tools to speed up, simplify and automate assessment and the provision of feedback for large cohorts of first and second year engineering students studying modules where CAD files are submitted electronically. A range of automated methods are described and compared with more “manual” approaches. Specifically one method uses an application programming interface (API) to interrogate SolidWorks models and extract information into an Excel spreadsheet, which is then used to automatically send feedback emails. Another method describes the use of audio recordings made during model interrogation which reduces the amount of time while increasing the level of detail provided as feedback.
Limitations found with these methods and problems encountered are discussed along with a quantified assessment of time saving efficiencies made.

Optimising postbuckling composite panels for damage resistance

The design of current composite primary aerostructures, such as fuselage or wing stiffened panels, tends to be conservative due to the susceptibility of the relatively weak skin-stiffener interface. This weakness is due to through-thickness stresses which are exacerbated by deformations due to buckling. This paper presents a finite-elementbased optimization strategy, utilizing a global-local modelling approach, for postbuckling stiffened panels which takes into account damage mechanisms which may lead to delamination and subsequent failure of the panel due to stiffener debonding. A genetic algorithm was linked to a finite element package to automate the iterative procedure and maximize the damage resistance of the panel in postbuckling. For a given loading condition, the procedure optimized the panel’s skin layup leading to a design displaying superior damage resistance compared to non-optimized designs

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.