Practice based learning approaches in collaborative design and engineering education:a case study investigation into the benefits of a crossdisciplinary practice based learning strategy

This study explores the ongoing pedagogical development of a number of undergraduate design and engineering programmes in the United Kingdom. Observations and data have been collected over several cohorts to bring a valuable perspective to the approaches piloted across two similar university departments while trialling a number of innovative learning strategies. In addition to the concurrent institutional studies the work explores curriculum design that applies the principles of Co-Design, multidisciplinary and trans disciplinary learning, with both engineering and product design students working alongside each other through a practical problem solving learning approach known as the CDIO learning initiative (Conceive, Design Implement and Operate) [1]. The study builds on previous work presented at the 2010 EPDE conference: The Effect of Personality on the Design Team: Lessons from Industry for Design Education [2]. The subsequent work presented in this paper applies the findings to mixed design and engineering team based learning, building on the insight gained through a number of industrial process case studies carried out in current design practice. Developments in delivery also aligning the CDIO principles of learning through doing into a practice based, collaborative learning experience and include elements of the TRIZ creative problem solving technique [3]. The paper will outline case studies involving a number of mixed engineering and design student projects that highlight the CDIO principles, combined with an external industrial design brief. It will compare and contrast the learning experience with that of a KTP derived student project, to examine an industry based model for student projects. In addition key areas of best practice will be presented, and student work from each mode will be discussed at the conference.

The design and validation of an optical coherence tomography-based classification system for focal vitreomacular traction

PurposeTo develop and validate a classification system for focal vitreomacular traction (VMT) with and without macular hole based on spectral domain optical coherence tomography (SD-OCT), intended to aid in decision-making and prognostication.MethodsA panel of retinal specialists convened to develop this system. A literature review followed by discussion on a wide range of cases formed the basis for the proposed classification. Key features on OCT were identified and analysed for their utility in clinical practice. A final classification was devised based on two sequential, independent validation exercises to improve interobserver variability.ResultsThis classification tool pertains to idiopathic focal VMT assessed by a horizontal line scan using SD-OCT. The system uses width (W), interface features (I), foveal shape (S), retinal pigment epithelial changes (P), elevation of vitreous attachment (E), and inner and outer retinal changes (R) to give the acronym WISPERR. Each category is scored hierarchically. Results from the second independent validation exercise indicated a high level of agreement between graders: intraclass correlation ranged from 0.84 to 0.99 for continuous variables and Fleiss' kappa values ranged from 0.76 to 0.95 for categorical variables.ConclusionsWe present an OCT-based classification system for focal VMT that allows anatomical detail to be scrutinised and scored qualitatively and quantitatively using a simple, pragmatic algorithm, which may be of value in clinical practice as well as in future research studies.

Practice based learning for automotive engineering design students

The automotive industry combines a multitude of professionals to develop a modern car successfully. Within the design and development teams the collaboration and interface between Engineers and Designers is critical to ensure design intent is communicated and maintained throughout the development process. This study highlights recent industry practice with the emergence of Concept Engineers in design teams at Jaguar Land Rover Automotive group. The role of the Concept Engineer emphasises the importance of the Engineering and Design/Styling interface with the Concept engineer able to interact and understand the challenges and specific languages of each specialist area, hence improving efficiency and communication within the design team. Automotive education tends to approach design from two distinct directions, that of engineering design through BSc courses or a more styling design approach through BA and BDes routes. The educational challenge for both types of course is to develop engineers and stylist's who have greater understanding and experience of each other's specialist perspective of design and development. The study gives examples of two such courses in the UK who are developing programmes to help students widen their understanding of the engineering and design spectrum. Initial results suggest the practical approach has been well received by students and encouraged by industry as they seek graduates with specialist knowledge but also a wider appreciation of their role within the design process.

DFIG machine design for maximizing power output based on surrogate optimization algorithm

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are used in conjunction with the finite element method to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Surrogate-based analysis and optimization for the design of heat sinks with jet impingement

Heat sinks are widely used for cooling electronic devices and systems. Their thermal performance is usually determined by the material, shape, and size of the heat sink. With the assistance of computational fluid dynamics (CFD) and surrogate-based optimization, heat sinks can be designed and optimized to achieve a high level of performance. In this paper, the design and optimization of a plate-fin-type heat sink cooled by impingement jet is presented. The flow and thermal fields are simulated using the CFD simulation; the thermal resistance of the heat sink is then estimated. A Kriging surrogate model is developed to approximate the objective function (thermal resistance) as a function of design variables. Surrogate-based optimization is implemented by adaptively adding infill points based on an integrated strategy of the minimum value, the maximum mean square error approach, and the expected improvement approaches. The results show the influence of design variables on the thermal resistance and give the optimal heat sink with lowest thermal resistance for given jet impingement conditions.

Quantum-dot based ultrafast photoconductive antennae for efficient THz radiation

Here we overview our work on quantum dot based THz photoconductive antennae, capable of being pumped at very high optical intensities of higher than 1W optical mean power, i.e. about 50 times higher than the conventional LT-GaAs based antennae. Apart from high thermal tolerance, defect-free GaAs crystal layers in an InAs:GaAs quantum dot structure allow high carrier mobility and ultra-short photo carrier lifetimes simultaneously. Thus, they combine the advantages and lacking the disadvantages of GaAs and LT-GaAs, which are the most popular materials so far, and thus can be used for both CW and pulsed THz generation. By changing quantum dot size, composition, density of dots and number of quantum dot layers, the optoelectronic properties of the overall structure can be set over a reasonable range-compact semiconductor pump lasers that operate at wavelengths in the region of 1.0 μm to 1.3 μm can be used. InAs:GaAs quantum dot-based antennae samples show no saturation in pulsed THz generation for all average pump powers up to 1W focused into 30 μm spot. Generated THz power is super-linearly proportional to laser pump power. The generated THz spectrum depends on antenna design and can cover from 150 GHz up to 1.5 THz.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.

Reliability-based calibration for a mechanics-based fatigue cracking design procedure

Most pavement design procedures incorporate reliability to account for design inputs-associated uncertainty and variability effect on predicted performance. The load and resistance factor design (LRFD) procedure, which delivers economical section while considering design inputs variability separately, has been recognised as an effective tool to incorporate reliability into design procedures. This paper presents a new reliability-based calibration in LRFD format for a mechanics-based fatigue cracking analysis framework. This paper employs a two-component reliability analysis methodology that utilises a central composite design-based response surface approach and a first-order reliability method. The reliability calibration was achieved based on a number of field pavement sections that have well-documented performance history and high-quality field and laboratory data. The effectiveness of the developed LRFD procedure was evaluated by performing pavement designs of various target reliabilities and design conditions. The result shows an excellent agreement between the target and actual reliabilities. Furthermore, it is clear from the results that more design features need to be included in the reliability calibration to minimise the deviation of the actual reliability from the target reliability.

Design principles of semantic binary database management systems

The Semantic Binary Data Model (SBM) is a viable alternative to the now-dominant relational data model. SBM would be especially advantageous for applications dealing with complex interrelated networks of objects provided that a robust efficient implementation can be achieved. This dissertation presents an implementation design method for SBM, algorithms, and their analytical and empirical evaluation. Our method allows building a robust and flexible database engine with a wider applicability range and improved performance. ^ Extensions to SBM are introduced and an implementation of these extensions is proposed that allows the database engine to efficiently support applications with a predefined set of queries. A New Record data structure is proposed. Trade-offs of employing Fact, Record and Bitmap Data structures for storing information in a semantic database are analyzed. ^ A clustering ID distribution algorithm and an efficient algorithm for object ID encoding are proposed. Mapping to an XML data model is analyzed and a new XML-based XSDL language facilitating interoperability of the system is defined. Solutions to issues associated with making the database engine multi-platform are presented. An improvement to the atomic update algorithm suitable for certain scenarios of database recovery is proposed. ^ Specific guidelines are devised for implementing a robust and well-performing database engine based on the extended Semantic Data Model. ^

Refinement methods for software architecture design using the software architecture model

Modern software systems are often large and complicated. To better understand, develop, and manage large software systems, researchers have studied software architectures that provide the top level overall structural design of software systems for the last decade. One major research focus on software architectures is formal architecture description languages, but most existing research focuses primarily on the descriptive capability and puts less emphasis on software architecture design methods and formal analysis techniques, which are necessary to develop correct software architecture design. ^ Refinement is a general approach of adding details to a software design. A formal refinement method can further ensure certain design properties. This dissertation proposes refinement methods, including a set of formal refinement patterns and complementary verification techniques, for software architecture design using Software Architecture Model (SAM), which was developed at Florida International University. First, a general guideline for software architecture design in SAM is proposed. Second, specification construction through property-preserving refinement patterns is discussed. The refinement patterns are categorized into connector refinement, component refinement and high-level Petri nets refinement. These three levels of refinement patterns are applicable to overall system interaction, architectural components, and underlying formal language, respectively. Third, verification after modeling as a complementary technique to specification refinement is discussed. Two formal verification tools, the Stanford Temporal Prover (STeP) and the Simple Promela Interpreter (SPIN), are adopted into SAM to develop the initial models. Fourth, formalization and refinement of security issues are studied. A method for security enforcement in SAM is proposed. The Role-Based Access Control model is formalized using predicate transition nets and Z notation. The patterns of enforcing access control and auditing are proposed. Finally, modeling and refining a life insurance system is used to demonstrate how to apply the refinement patterns for software architecture design using SAM and how to integrate the access control model. ^ The results of this dissertation demonstrate that a refinement method is an effective way to develop a high assurance system. The method developed in this dissertation extends existing work on modeling software architectures using SAM and makes SAM a more usable and valuable formal tool for software architecture design. ^

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Risk based post closure care analysis for Florida landfills

Subtitle D of the Resource Conservation and Recovery Act (RCRA) requires a post closure period of 30 years for non hazardous wastes in landfills. Post closure care (PCC) activities under Subtitle D include leachate collection and treatment, groundwater monitoring, inspection and maintenance of the final cover, and monitoring to ensure that landfill gas does not migrate off site or into on site buildings. The decision to reduce PCC duration requires exploration of a performance based methodology to Florida landfills. PCC should be based on whether the landfill is a threat to human health or the environment. Historically no risk based procedure has been available to establish an early end to PCC. Landfill stability depends on a number of factors that include variables that relate to operations both before and after the closure of a landfill cell. Therefore, PCC decisions should be based on location specific factors, operational factors, design factors, post closure performance, end use, and risk analysis. The question of appropriate PCC period for Florida’s landfills requires in depth case studies focusing on the analysis of the performance data from closed landfills in Florida. Based on data availability, Davie Landfill was identified as case study site for a case by case analysis of landfill stability. The performance based PCC decision system developed by Geosyntec Consultants was used for the assessment of site conditions to project PCC needs. The available data for leachate and gas quantity and quality, ground water quality, and cap conditions were evaluated. The quality and quantity data for leachate and gas were analyzed to project the levels of pollutants in leachate and groundwater in reference to maximum contaminant level (MCL). In addition, the projected amount of gas quantity was estimated. A set of contaminants (including metals and organics) were identified as contaminants detected in groundwater for health risk assessment. These contaminants were selected based on their detection frequency and levels in leachate and ground water; and their historical and projected trends. During the evaluations a range of discrepancies and problems that related to the collection and documentation were encountered and possible solutions made. Based on the results of PCC performance integrated with risk assessment, projection of future PCC monitoring needs and sustainable waste management options were identified. According to these results, landfill gas monitoring can be terminated, leachate and groundwater monitoring for parameters above MCL and surveying of the cap integrity should be continued. The parameters which cause longer monitoring periods can be eliminated for the future sustainable landfills. As a conclusion, 30 year PCC period can be reduced for some of the landfill components based on their potential impacts to human health and environment (HH&E).

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

A quantitative approach to the organizational design problem

The span of control is the most discussed single concept in classical and modern management theory. In specifying conditions for organizational effectiveness, the span of control has generally been regarded as a critical factor. Existing research work has focused mainly on qualitative methods to analyze this concept, for example heuristic rules based on experiences and/or intuition. This research takes a quantitative approach to this problem and formulates it as a binary integer model, which is used as a tool to study the organizational design issue. This model considers a range of requirements affecting management and supervision of a given set of jobs in a company. These decision variables include allocation of jobs to workers, considering complexity and compatibility of each job with respect to workers, and the requirement of management for planning, execution, training, and control activities in a hierarchical organization. The objective of the model is minimal operations cost, which is the sum of supervision costs at each level of the hierarchy, and the costs of workers assigned to jobs. The model is intended for application in the make-to-order industries as a design tool. It could also be applied to make-to-stock companies as an evaluation tool, to assess the optimality of their current organizational structure. Extensive experiments were conducted to validate the model, to study its behavior, and to evaluate the impact of changing parameters with practical problems. This research proposes a meta-heuristic approach to solving large-size problems, based on the concept of greedy algorithms and the Meta-RaPS algorithm. The proposed heuristic was evaluated with two measures of performance: solution quality and computational speed. The quality is assessed by comparing the obtained objective function value to the one achieved by the optimal solution. The computational efficiency is assessed by comparing the computer time used by the proposed heuristic to the time taken by a commercial software system. Test results show the proposed heuristic procedure generates good solutions in a time-efficient manner.