Insight Extraction for Semiconductor Manufacturing Processes

In the semiconductor manufacturing environment it is very important to understand which factors have the most impact on process outcomes and to control them accordingly. This is usually achieved through design of experiments at process start-up and long term observation of production. As such it relies heavily on the expertise of the process engineer. In this work, we present an automatic approach to extracting useful insights about production processes and equipment based on state-of-the-art Machine Learning techniques. The main goal of this activity is to provide tools to process engineers to accelerate the learning-by-observation phase of process analysis. Using a Metal Deposition process as an example, we highlight various ways in which the extracted information can be employed.

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 (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) 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.

A Soft Sensor for Viscosity Control of Polymer Extrusion

Viscosity represents a key indicator of product quality in polymer extrusion but has traditionally been difficult to measure in-process in real-time. An innovative, yet simple, solution to this problem is proposed by a Prediction-Feedback observer mechanism. A `Prediction' model based on the operating conditions generates an open-loop estimate of the melt viscosity; this estimate is used as an input to a second, `Feedback' model to predict the pressure of the system. The pressure value is compared to the actual measured melt pressure and the error used to correct the viscosity estimate. The Prediction model captures the relationship between the operating conditions and the resulting melt viscosity and as such describes the specific material behavior. The Feedback model on the other hand describes the fundamental physical relationship between viscosity and extruder pressure and is a function of the machine geometry. The resulting system yields viscosity estimates within 1% error, shows excellent disturbance rejection properties and can be directly applied to model-based control. This is of major significance to achieving higher quality and reducing waste and set-up times in the polymer extrusion industry.

Monitoring of saliva pH using miniaturized sensing tools as an indicator of oral health

The monitoring of oral disease is important, not alone for oral health, but for the detection and prevention of
systemic disease. The link between oral health and systemic disease is the focus of many studies, with
indications emerging of a causal link [1]. For disease diagnostics, blood has typically been the fluid of choice
for analysis, the retrieval of which is invasive and therefore unsuitable for wearable technology. Analysis of
saliva, however, is less invasive than that of blood, requires little or no pre-treatment and is abundantly
available. A strong correlation has been found between the analytes of blood and saliva [2] with saliva
containing biomarkers for diseases such as diabetes, oral cancer and cardiovascular disease. The development of
an implantable multi-parametric wireless sensor, to monitor both salivary analytes and changes in gingival
temperature, is the aim of this research project.
The aim of our current study is to detect changes in salivary pH, using a gold electrode with a pHsensitive
iridium oxide layer, and an Ion Sensitive Field Effect Transistor probe. Characterisation studies were
carried out in artificial saliva (AS). A salivary pH of between 4.5pH-7.5pH [3], and gingival temperature
between 35°C-38°C [4], were identified as the target range of interest for the human oral environment. Sensor
measurements were recorded in solutions of varying pH and temperature. An ISFET probe was then implanted
into a prototype denture and characterised in AS. This study demonstrates the suitability of ISFET and gold
electrode pH sensors for incorporation into implantable oral sensors.
[1] G. Taylor and W. Borgnakke, “Periodontal disease: associations with diabetes, glycemic control and
complications,” Oral Dis., vol. 14, no. 3, pp. 191–203, Apr. 2008.
[2] E. Tékus, M. Kaj, E. Szabó, N. L. Szénási, I. Kerepesi, M. Figler, R. Gábriel, and M. Wilhelm,
“Comparison of blood and saliva lactate level after maximum intensity exercise,” Acta Biol. Hung., vol. 63
Suppl 1, pp. 89–98, 2012.
[3] S. Naveen, M. L. Asha, G. Shubha, A. Bajoria, and A. Jose, “Salivary Flow Rate, pH and Buffering
Capacity in Pregnant and Non Pregnant Women - A Comparative Study,” JMED Res., pp. 1–8, Feb. 2014.
[4] A. F. Holthuis and F. S. Chebib, “Observations on temperature and temperature patterns of the gingiva. I.
The effect of arch, region and health,” J. Periodontol., vol. 54, no. 10, pp. 624–628, Oct. 1983

Failure of a numerical quality assessment scale to identify potential risk of bias in a systematic review: a comparison study

BACKGROUND: Assessing methodological quality of primary studies is an essential component of systematic reviews. Following a systematic review which used a domain based system [United States Preventative Services Task Force (USPSTF)] to assess methodological quality, a commonly used numerical rating scale (Downs and Black) was also used to evaluate the included studies and comparisons were made between quality ratings assigned using the two different methods. Both tools were used to assess the 20 randomized and quasi-randomized controlled trials examining an exercise intervention for chronic musculoskeletal pain which were included in the review. Inter-rater reliability and levels of agreement were determined using intraclass correlation coefficients (ICC). Influence of quality on pooled effect size was examined by calculating the between group standardized mean difference (SMD).

RESULTS: Inter-rater reliability indicated at least substantial levels of agreement for the USPSTF system (ICC 0.85; 95% CI 0.66, 0.94) and Downs and Black scale (ICC 0.94; 95% CI 0.84, 0.97). Overall level of agreement between tools (ICC 0.80; 95% CI 0.57, 0.92) was also good. However, the USPSTF system identified a number of studies (n = 3/20) as "poor" due to potential risks of bias. Analysis revealed substantially greater pooled effect sizes in these studies (SMD -2.51; 95% CI -4.21, -0.82) compared to those rated as "fair" (SMD -0.45; 95% CI -0.65, -0.25) or "good" (SMD -0.38; 95% CI -0.69, -0.08).

CONCLUSIONS: In this example, use of a numerical rating scale failed to identify studies at increased risk of bias, and could have potentially led to imprecise estimates of treatment effect. Although based on a small number of included studies within an existing systematic review, we found the domain based system provided a more structured framework by which qualitative decisions concerning overall quality could be made, and was useful for detecting potential sources of bias in the available evidence.

A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells.

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.

Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering

This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones.

Gene-associated markers provide tools for tackling illegal fishing and false eco-certification

Illegal, Unreported and Unregulated fishing has had a major role in the overexploitation of global fish populations. In response, international regulations have been imposed and many fisheries have been 'eco-certified' by consumer organizations, but methods for independent control of catch certificates and eco-labels are urgently needed. Here we show that, by using gene-associated single nucleotide polymorphisms, individual marine fish can be assigned back to population of origin with unprecedented high levels of precision. By applying high differentiation single nucleotide polymorphism assays, in four commercial marine fish, on a pan-European scale, we find 93-100% of individuals could be correctly assigned to origin in policy-driven case studies. We show how case-targeted single nucleotide polymorphism assays can be created and forensically validated, using a centrally maintained and publicly available database. Our results demonstrate how application of gene-associated markers will likely revolutionize origin assignment and become highly valuable tools for fighting illegal fishing and mislabelling worldwide.

Tools and Environments for Networked Electronic Performance

A collection of software and hardware tools and environments that facilitate collective networked performance between electronic musicians. Tools include 'Chat Monkey', a live chat tool for performance, 'DMA Sequencing', a step sequencer using open sound control messaging and multi nodal control, 'tutti, duet, trio, solo, quartet', an ensemble management environment, and 'Por Larrañaga', a cigar box based electro-acoustic instrument with embedded sensors and controllers. Notable performances: w/BLISS, NCAD, Dublin, 1 March 2015; w/BLISS, NI Science Festival, Belfast, 21 Feb 2015

Control of ionization and dissociation of H2+ by elliptically polarized ultra-short VUV laser pulses

Resonance-enhanced multiphoton ionization of H₂ ⁺ exposed to elliptically polarized VUV laser pulses is investigated. Differential cross sections for nuclei and electron are obtained using numerical solutions of the time-dependent Schrödinger equation. In this work in progress, we explore the dependence of the dissociative ionization observables with the polarization of the light.

Investigating Tools used to Analyse the Impact of a Wave Farm on the surrounding Wave Field

For wave energy to become commercially viable, it is predicted that wave energy converters (WECs) will need to be installed in large wave farms. This will required an extensive environmental impact study. Assessments of impacts of these sites requires prior numerical modelling however the available tools have not been fully validated.
This project investigates the area surrounding an array of five scaled WEC models using experimental techniques. It then assesses the suitability of numerical tools to be validated with this experimental data. Validated numerical tools could then be used to predict parameters relating to the models such as reflection and transmission coefficients.
The physical aspect of this project was conducted in the Portaferry wave basin owned by Queen’s University Belfast. The device studied was a bottom hinged oscillating wave surge converter (OWSC) which penetrates the surface (similar to the Oyster device). The models were tested at 40th scale.

Numerical modelling of radionuclide migration in near-surface systems

Em todo o mundo são usados, hoje em dia, modelos numéricos hidrogeoquímicos para simular fenómenos naturais e fenómenos decorrentes de actividades antrópicas. Estes modelos ajudam-nos a compreender o ambiente envolvente, a sua variabilidade espacial e evolução temporal. No presente trabalho apresenta-se o desenvolvimento de modelos numéricos hidrogeoquímicos aplicados no contexto do repositório geológico profundo para resíduos nucleares de elevada actividade. A avaliação da performance de um repositório geológico profundo inclui o estudo da evolução geoquímica do repositório, bem como a análise dos cenários de mau funcionamento do repositório, e respectivas consequências ambientais. Se se escaparem acidentalmente radionuclídeos de um repositório, estes poderão atravessar as barreiras de engenharia e barreiras naturais que constituem o repositório, atingindo eventualmente, os ecosistemas superficiais. Neste caso, os sedimentos subsuperficiais constituem a última barreira natural antes dos ecosistemas superficiais. No presente trabalho foram desenvolvidos modelos numéricos que integram processos biogeoquímicos, geoquímicos, hidrodinâmicos e de transporte de solutos, para entender e quantificar a influência destes processos na mobilidade de radionuclídeos em sistemas subsuperficiais. Os resultados alcançados reflectem a robustez dos instrumentos numéricos utilizados para desenvolver simulações descritivas e predictivas de processos hidrogeoquímicos que influenciam a mobilidade de radionuclídeos. A simulação (descritiva) de uma experiência laboratorial revela que a actividade microbiana induz a diminuição do potencial redox da água subterrânea que, por sua vez, favorece a retenção de radionuclídeos sensíveis ao potencial redox, como o urânio. As simulações predictivas indicam que processos de co-precipitação com minerais de elementos maioritários, precipitação de fases puras, intercâmbio catiónico e adsorção à superfície de minerais favorecem a retenção de U, Cs, Sr e Ra na fase sólida de uma argila glaciar e uma moreia rica em calcite. A etiquetagem dos radionuclídeos nas simulações numéricas permitiu concluir que a diluição isotópica joga um papel importante no potencial impacte dos radionuclídeos nos sistemas subsuperficiais. A partir dos resultados das simulações numéricas é possivel calcular coeficientes de distribuição efectivos. Esta metodologia proporciona a simulação de ensaios de traçadores de longa duração que não seriam exequíveis à escala da vida humana. A partir destas simulações podem ser obtidos coeficientes de retardamento que são úteis no contexto da avaliação da performance de repositórios geológicos profundos.

Computational methods in the fractional calculus of variations and optimal control

The fractional calculus of variations and fractional optimal control are generalizations of the corresponding classical theories, that allow problem modeling and formulations with arbitrary order derivatives and integrals. Because of the lack of analytic methods to solve such fractional problems, numerical techniques are developed. Here, we mainly investigate the approximation of fractional operators by means of series of integer-order derivatives and generalized finite differences. We give upper bounds for the error of proposed approximations and study their efficiency. Direct and indirect methods in solving fractional variational problems are studied in detail. Furthermore, optimality conditions are discussed for different types of unconstrained and constrained variational problems and for fractional optimal control problems. The introduced numerical methods are employed to solve some illustrative examples.

Development of open models and tools for seismic risk assessment: application to Portugal

The exponential growth of the world population has led to an increase of settlements often located in areas prone to natural disasters, including earthquakes. Consequently, despite the important advances in the field of natural catastrophes modelling and risk mitigation actions, the overall human losses have continued to increase and unprecedented economic losses have been registered. In the research work presented herein, various areas of earthquake engineering and seismology are thoroughly investigated, and a case study application for mainland Portugal is performed. Seismic risk assessment is a critical link in the reduction of casualties and damages due to earthquakes. Recognition of this relation has led to a rapid rise in demand for accurate, reliable and flexible numerical tools and software. In the present work, an open-source platform for seismic hazard and risk assessment is developed. This software is capable of computing the distribution of losses or damage for an earthquake scenario (deterministic event-based) or earthquake losses due to all the possible seismic events that might occur within a region for a given interval of time (probabilistic event-based). This effort has been developed following an open and transparent philosophy and therefore, it is available to any individual or institution. The estimation of the seismic risk depends mainly on three components: seismic hazard, exposure and vulnerability. The latter component assumes special importance, as by intervening with appropriate retrofitting solutions, it may be possible to decrease directly the seismic risk. The employment of analytical methodologies is fundamental in the assessment of structural vulnerability, particularly in regions where post-earthquake building damage might not be available. Several common methodologies are investigated, and conclusions are yielded regarding the method that can provide an optimal balance between accuracy and computational effort. In addition, a simplified approach based on the displacement-based earthquake loss assessment (DBELA) is proposed, which allows for the rapid estimation of fragility curves, considering a wide spectrum of uncertainties. A novel vulnerability model for the reinforced concrete building stock in Portugal is proposed in this work, using statistical information collected from hundreds of real buildings. An analytical approach based on nonlinear time history analysis is adopted and the impact of a set of key parameters investigated, including the damage state criteria and the chosen intensity measure type. A comprehensive review of previous studies that contributed to the understanding of the seismic hazard and risk for Portugal is presented. An existing seismic source model was employed with recently proposed attenuation models to calculate probabilistic seismic hazard throughout the territory. The latter results are combined with information from the 2011 Building Census and the aforementioned vulnerability model to estimate economic loss maps for a return period of 475 years. These losses are disaggregated across the different building typologies and conclusions are yielded regarding the type of construction more vulnerable to seismic activity.