Impact of object extraction methods on classification performance in surface inspection systems

In surface inspection applications, the main goal is to detect all areas which might contain defects or unacceptable imperfections, and to classify either every single 'suspicious' region or the investigated part as a whole. After an image is acquired by the machine vision hardware, all pixels that deviate from a pre-defined 'ideal' master image are set to a non-zero value, depending on the magnitude of deviation. This procedure leads to so-called "contrast images", in which accumulations of bright pixels may appear, representing potentially defective areas. In this paper, various methods are presented for grouping these bright pixels together into meaningful objects, ranging from classical image processing techniques to machine-learning-based clustering approaches. One important issue here is to find reasonable groupings even for non-connected and widespread objects. In general, these objects correspond either to real faults or to pseudo-errors that do not affect the surface quality at all. The impact of different extraction methods on the accuracy of image classifiers will be studied. The classifiers are trained with feature vectors calculated for the extracted objects found in images labeled by the user and showing surfaces of production items. In our investigation artificially created contrast images will be considered as well as real ones recorded on-line at a CD imprint production and at an egg inspection system. © Springer-Verlag 2009

CSA calculator Manual: Assessing the climate-smartness of technical options at farm level

The CSA-Farm Calculator tool was developed as part of the CSA Multilevel Monitoring Framework implemented in the context of the Learning Platform Participatory evaluation of Climate-Smart Agricultural (CSA) practices and technologies across the AR4D Climate-Smart Villages (CSVs) network. It builds on a farm model included in the GeoFarmer app allowing the prospective assessment of farm performance in response to the implementation of different CSA practices/packages and other farming management activities in terms of productivity, adaptive capacity, and mitigation potential (in other words the “climate-smartness” of the farm) and specifically looking at synergies and trade-offs

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models.

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on (i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and (ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi–Sugeno fuzzy systems (termed as PLS-fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, (i) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and (ii) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (self-adaptive forecast models with increased flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic) chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-fuzzy forecast models (showing mostly lower errors than achieved by other model architectures) as well as in terms of Pareto fronts with individuals (solutions) whose fitness was close to the optimal values of three most important target QCs (being used for supervision): flatness, void events and RMSEs of the chips. Suggestions could thus be provided to experts/operators how to best change process values and associated machining parameters at the injection molding process in order to achieve significantly higher product quality for the final chips at the end of the bonding process

Variability in the water footprint of arable crop production across European regions

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Capacity building program to improve stakeholder resilience and adaptation to climate change in Jamaica (CBCA)

Jamaica will face future climate trends marked by increases in the intensity and frequency of climate extremes, escalating rainfall variability, and increased droughts and floods; combined with fragile ecosystems and sensitive coastal zones, the result is that Jamaica has a relatively high vulnerability to climate change. Climate-Smart Agriculture (CSA), which incorporates adaptation/resilience and mitigation measures while ensuring sustainable productivity, has the potential to build synergies and limit trade-offs in agriculture under present climate uncertainties, reduce existing knowledge gaps, and facilitate alignment between sectors and policies. The Evidence-Based, Gender-Equitable Framework for Prioritizing Climate-Smart Agriculture Interventions has been adapted from different tools and research methods to overcome the challenge of identifying context-specific technologies and better understanding the trade-offs and co-benefits of different combinations of portfolios could deliver for different stakeholders. Findings from spatial a water-balance model show low water yield, simulations of future climate characteristics using the crop model AquaCrop show that irrigated systems to balance the crops water demand are crucial to achieving higher yields, a multi-criteria analysis with stakeholders identified CSA practices for each key-value chain (crop), and Climate-Smart Agriculture (CSA) training manuals and programs were co-created with extension workers and farmers

From Human Perception and Action Recognition to Causal Understanding of Human-Robot Interaction in Industrial Environments

Human-robot collaboration is migrating from lightweight robots in laboratory environments to industrial applications, where heavy tasks and powerful robots are more common. In this scenario, a reliable perception of the humans involved in the process and related intentions and behaviors is fundamental. This paper presents two projects investigating the use of robots in relevant industrial scenarios, providing an overview of how industrial human-robot collaborative tasks can be successfully addressed

Priority questions in multidisciplinary drought research

Addressing timely and relevant questions across a multitude of spatio-temporal scales, state-of-the-art interdisciplinary drought research will likely increase in importance under projected climate change. Given the complexity of the various direct and indirect causes and consequences of a drier world, scientific tasks need to be coordinated efficiently. Drought-related research endeavors ranging from individual projects to global initiatives therefore require prioritization. Here, we present 60 priority questions for optimizing future drought research. This topical catalogue reflects the experience of 65 scholars from 21 countries and almost 20 fields of research in both natural sciences and the humanities. The set of drought-related questions primarily covers drought monitoring, impacts, forecasting, climatology, adaptation, as well as planning and policy. The questions highlight the increasingly important role of remote sensing techniques in drought monitoring, importance of drought forecasting and understanding the relationships between drought parameters and drought impacts, but also challenges of drought adaptation and preparedness policies