Origins of production errors and significance of employee empowerment in reducing production error amount in sheet metal fabricating industry

The market place of the twenty-first century will demand that manufacturing assumes a crucial role in a new competitive field. Two potential resources in the area of manufacturing are advanced manufacturing technology (AMT) and empowered employees. Surveys in Finland have shown the need to invest in the new AMT in the Finnish sheet metal industry in the 1990's. In this run the focus has been on hard technology and less attention is paid to the utilization of human resources. In manymanufacturing companies an appreciable portion of the profit within reach is wasted due to poor quality of planning and workmanship. The production flow production error distribution of the sheet metal part based constructions is inspectedin this thesis. The objective of the thesis is to analyze the origins of production errors in the production flow of sheet metal based constructions. Also the employee empowerment is investigated in theory and the meaning of the employee empowerment in reducing the overall production error amount is discussed in this thesis. This study is most relevant to the sheet metal part fabricating industrywhich produces sheet metal part based constructions for electronics and telecommunication industry. This study concentrates on the manufacturing function of a company and is based on a field study carried out in five Finnish case factories. In each studied case factory the most delicate work phases for production errors were detected. It can be assumed that most of the production errors are caused in manually operated work phases and in mass production work phases. However, no common theme in collected production error data for production error distribution in the production flow can be found. Most important finding was still that most of the production errors in each case factory studied belong to the 'human activity based errors-category'. This result indicates that most of the problemsin the production flow are related to employees or work organization. Development activities must therefore be focused to the development of employee skills orto the development of work organization. Employee empowerment gives the right tools and methods to achieve this.

Estudio de la distribución espacial de Cydia pomonella (L.) y Pandemis heparana (Denis & Schiffermüller) en Torregrossa (Lleida) mediante métodos geoestadísticos

El estudio de la distribución espacial de una especie por me'todos geoestadísticos se realiza mediante el conocimiento de la función semivariograma. Después de calcular el semivariograma se procede a la estimación de la variable regionalizada en cualquier punto de la zona de estudio. Esta estimación se realiza mediante técnicas de interpolación lineal llamadas «krigeado», en honor a Krige y Matheron, fundadores de la geoestadística. El «krigeado» se basa en la minimización de la varianza del error en cada punto de estudio, previamente localizado en el espacio por sus coordenadas de situación. Cydia pomonella (L.) y Pandemis heparana (Denis & Schiffermüller) son dos tortrícidos plaga del manzano y del peral. La estimación de sus poblaciones se realiza mediante trampas de feromona y es posible disponer de una amplia base de datos. El objetivo de este trabajo fue analizar la idoneidad de los métodos geoestadísticos para el estudio de poblaciones de insectos y aplicarlas al caso concreto de C. pomonella y P. heparana. Se utilizaron las capturas en 55 estaciones con trampas de feromonas (difusor de origen Wageningen) colocadas en parcelas comerciales de manzano y peral en 1996 y 1997 en el término municipal de Torregrossa (Lleida). La idoneidad de los métodos geoestadísticos quedó demostrada por el hecho de que la variable número acumulado de machos por trampa fue regionalizable. Una vez calculadas las funciones semivariograma para cada especie y año, se han dibujado los mapas de distribución mediante el uso de isolíneas. En el futuro, se plantea la posibilidad de ampliar la zona de estudio a toda la zona frutera de Lleida y analizar la influencia de variables independientes (climáticas ...) sobre la distribución espacial mediante métodos de «co-krigeado».

Parametrización de kB-1 para estimar el flujo de calor sensible con imágenes AVHRR

La necesidad de evaluar la evapotranspiración a escala regional para la gestión de regadíos ha hecho que sean innumerables los intentos por aplicar imágenes AVHRR-NOAA en la determinación el flujo de calor sensible. La principal limitación de estos métodos es la estimación de la resistencia aerodinámica. El parámetro crítico en la expresión de la resistencia aerodinámica es kB-1. La parametrización de kB-1 ha sido infructuosa a escala regional por no disponer hasta ahora de medidas de flujo de calor sensible a escala del píxel AVHRR en superficies heterogéneas y durante toda una temporada de riegos. Para resolver esta medida de flujo se ha desarrollado el cintilómetro. En la primera parte de este trabajo se estudia la representatividad espacial de las medidas del cintilómetro. El núcleo de esta aportación consiste en la correlación entre el parámetro kB-1, el NDVI y la altura solar. Los buenos resultados obtenidos (r2=0.81) ofrecen una nueva metodología para determinar el flujo de calor sensible. La estimación de kB-1, las imágenes AVHRR y los datos meteorológicos permiten calcular el flujo de calor sensible durante toda la temporada de riegos con errores inferiores al 20%.

Spatial variation in the decline of European birds as shown by the barn owl Tyto alba diet

Capsule The analysis of 635 papers about the diet of the European Barn Owl Tyto alba showed that 83 751 birds were captured out of 3.44 million prey items (2.4%). Birds were more frequently captured on islands than mainland, in southern than northern Europe and in eastern than western Europe. Between 1860 and 2012, the consumption of birds decreased in northern and eastern Europe. Among avian prey, the House Sparrow Passer domesticus, the most frequently captured bird (65.7%), decreased in frequency during the last 150 years in eastern Europe.

The interplay of spatial and climatic landscapes in the genetic distribution of a South American parrot

AimOur aim was to understand the interplay of heterogeneous climatic and spatial landscapes in shaping the distribution of nuclear microsatellite variation in burrowing parrots, Cyanoliseus patagonus. Given the marked phenotypic differences between populations of burrowing parrots we hypothesized an important role of geographical as well climatic heterogeneity in the population structure of this species. LocationSouthern South America. MethodsWe applied a landscape genetics approach to investigate the explicit patterns of genetic spatial autocorrelation based on both geography and climate using spatial principal component analysis (sPCA). This necessitated a novel statistical estimation of the species climatic landscape, considering temperature- and precipitation-based variables separately to evaluate their weight in shaping the distribution of genetic variation in our model system. ResultsGeographical and climatic heterogeneity successfully explained molecular variance in burrowing parrots. sPCA divided the species distribution into two main areas, Patagonia and the pre-Andes, which were connected by an area of geographical and climatic transition. Moreover, sPCA revealed cryptic and conservation-relevant genetic structure: the pre-Andean populations and the transition localities were each divided into two groups, each management units for conservation. Main conclusionssPCA, a method originally developed for spatial genetics, allowed us to unravel the genetic structure related to spatial and climatic landscapes and to visualize these patterns in landscape space. These novel climatic inferences underscore the importance of our modified sPCA approach in revealing how climatic variables can drive cryptic patterns of genetic structure, making the approach potentially useful in the study of any species distributed over a climatically heterogeneous landscape.

Error en el posicionamiento indoor en dispositivos móviles

Location information is becoming increasingly necessary as every new smartphone incorporates a GPS (Global Positioning System) which allows the development of various applications based on it. However, it is not possible to properly receive the GPS signal in indoor environments. For this reason, new indoor positioning systems are being developed. As indoors is a very challenging scenario, it is necessary to study the precision of the obtained location information in order to determine if these new positioning techniques are suitable for indoor positioning.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine

Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance.

Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps

The role of competition for light among plants has long been recognized at local scales, but its potential importance for plant species' distribution at larger spatial scales has largely been ignored. Tree cover acts as a modulator of local abiotic conditions, notably by reducing light availability below the canopy and thus the performance of species that are not adapted to low-light conditions. However, this local effect may propagate to coarser spatial grains. Using 6,935 vegetation plots located across the European Alps, we fit Generalized Linear Models (GLM) for the distribution of 960 herbs and shrubs species to assess the effect of tree cover at both plot and landscape grain sizes (~ 10-m and 1-km, respectively). We ran four models with different combinations of variables (climate, soil and tree cover) for each species at both spatial grains. We used partial regressions to evaluate the independent effects of plot- and landscape-scale tree cover on plant communities. Finally, the effects on species' elevational range limits were assessed by simulating a removal experiment comparing the species' distribution under high and low tree cover. Accounting for tree cover improved model performance, with shade-tolerant species increasing their probability of presence at high tree cover whereas shade-intolerant species showed the opposite pattern. The tree cover effect occurred consistently at both plot and landscape spatial grains, albeit strongest at the former. Importantly, tree cover at the two grain sizes had partially independent effects on plot-scale plant communities, suggesting that the effects may be transmitted to coarser grains through meta-community dynamics. At high tree cover, shade-intolerant species exhibited elevational range contractions, especially at their upper limit, whereas shade-tolerant species showed elevational range expansions at both limits. Our findings suggest that the range shifts for herb and shrub species may be modulated by tree cover dynamics.

Aortic blood pressure measured via EIT: investigation of different measurement settings.

Electrical impedance tomography (EIT) allows the measurement of intra-thoracic impedance changes related to cardiovascular activity. As a safe and low-cost imaging modality, EIT is an appealing candidate for non-invasive and continuous haemodynamic monitoring. EIT has recently been shown to allow the assessment of aortic blood pressure via the estimation of the aortic pulse arrival time (PAT). However, finding the aortic signal within EIT image sequences is a challenging task: the signal has a small amplitude and is difficult to locate due to the small size of the aorta and the inherent low spatial resolution of EIT. In order to most reliably detect the aortic signal, our objective was to understand the effect of EIT measurement settings (electrode belt placement, reconstruction algorithm). This paper investigates the influence of three transversal belt placements and two commonly-used difference reconstruction algorithms (Gauss-Newton and GREIT) on the measurement of aortic signals in view of aortic blood pressure estimation via EIT. A magnetic resonance imaging based three-dimensional finite element model of the haemodynamic bio-impedance properties of the human thorax was created. Two simulation experiments were performed with the aim to (1) evaluate the timing error in aortic PAT estimation and (2) quantify the strength of the aortic signal in each pixel of the EIT image sequences. Both experiments reveal better performance for images reconstructed with Gauss-Newton (with a noise figure of 0.5 or above) and a belt placement at the height of the heart or higher. According to the noise-free scenarios simulated, the uncertainty in the analysis of the aortic EIT signal is expected to induce blood pressure errors of at least ± 1.4 mmHg.

Conditioning of multiple-point statistics facies simulations to tomographic images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.