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It remains difficult to segregate pelagic habitats since structuring processes are dynamic on a wide range of scales and clear boundaries in the open ocean are non-existent. However, to improve our knowledge about existing ecological niches and the processes shaping the enormous diversity of marine plankton, we need a better understanding of the driving forces behind plankton patchiness. Here we describe a new machine-learning method to detect and quantify pelagic habitats based on hydrographic measurements. An Autoencoder learns two-dimensional, meaningful representations of higher-dimensional micro-habitats, which are characterized by a variety of biotic and abiotic measurements from a high-speed ROTV. Subsequently, we apply a density-based clustering algorithm to group similar micro-habitats into associated pelagic macro-habitats in the German Bight of the North Sea. Three distinct macro-habitats, a “surface mixed layer,” a “bottom layer,” and an exceptionally “productive layer” are consistently identified, each with its distinct plankton community. We provide evidence that the model detects relevant features like the doming of the thermocline within an Offshore Wind Farm or the presence of a tidal mixing front.
Measured building survey
(2022)
The precisely measured documentation of historical buildings by means of drawings – the building survey – serves several purposes: it is not only an important part of the scientific study of architecture, but also an essential basis for building measures in existing contexts. It is the only way to undertake well-founded research respectively detailed architectural planning, from structural improvement to conservation and restoration measures. In addition to a historical overview of building surveying as a method, this volume mainly offers practical assistance regarding the different methods for documenting historic buildings – starting from manual methods of measuring up to electronic and computer-aided utilities available today. The content is complemented by many illustrations and examples as well as an introduction to methods, that go beyond the mere graphic documentation and thus represent ways of conducting thorough building examinations. The book offers easy access to the topic of building surveying, both for students as well as for anyone dealing professionally with historic buildings
Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation.
This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.
In an industrial product development process, the Bill of Materials (BOM) is a hierarchical, multi-level representation of all components, parts and quantities of a product. With increasing complexity of industrial products, also BOMs become more complex and thus prone to errors, for example when the individual parts of a product are changed during the product development process. Frequently, these Bill of Materials errors have to be identified manually or by using simple, rule-based schemes. In this paper, we provide a technical background of BOMs, showing the intricacy of temporal BOMs errors in an industrial product development process. The work of other authors, which focused on association mining and tree reconciliation to detect Bill of Materials errors, is analysed. We found that there is currently no system being able to prescribe where in a Bill of Materials and when in the product development process, errors are probable to occur. Also, Machine Learning (ML) methods have not been applied yet. Based on these findings, we formalize the notions Bill of Materials and Bill of Materials errors. Furthermore, we present a deterministic distance measure for BOMS. We provide an answer to the main question of how to represent a Bill of Materials for Machine Learning tasks by solving the orthogonal Procrustes problem for dynamic, hierarchical datasets. Then, we describe an isolation forest based approach to temporal anomaly detection, which points at potential errors in a Bill of Materials at a specific timestamp. Furthermore, we apply Machine Learning and present a multi-output Multi Layer Perceptron for the prediction of temporal Bill of Materials errors. The model predicts where and at which point of time Bill of Materials errors are probable to occur, which renders it a prescriptive system. Eventually, we optimize the performance of our model using contextualization via -means clustering. Finally, we apply our prescriptive pipeline to a real world dataset and show its superiority to existing methods using a qualitative comparison.
Thermal cyclers are used to perform polymerase chain reaction runs (PCR runs) and Peltier modules are the key components in these instruments. The demand for thermal cyclers has strongly increased during the COVID-19 pandemic due to the fact that they are important tools used in the research, identification, and diagnosis of the virus. Even though Peltier modules are quite durable, their failure poses a serious threat to the integrity of the instrument, which can lead to plant shutdowns and sample loss. Therefore, it is highly desirable to be able to predict the state of health of Peltier modules and thus reduce downtime. In this paper methods from three sub-categories of supervised machine learning, namely classical methods, ensemble methods and convolutional neural networks, were compared with respect to their ability to detect the state of health of Peltier modules integrated in thermal cyclers. Device-specific data from on-deck thermal cyclers (ODTC®) supplied by INHECO Industrial Heating & Cooling GmbH (Fig 1), Martinsried, Germany were used as a database for training the models. The purpose of this study was to investigate methods for data-driven condition monitoring with the aim of integrating predictive analytics into future product platforms. The results show that information about the state of health can be extracted from operational data - most importantly current readings - and that convolutional neural networks were the best at producing a generalized model for fault classification.
Design research is an academic issue and increasingly a success factor for industrial,organizational and social innovation. Efficient methodical support is crucial. The fiercerejection of 1st generation design methods in the early 1970s resulted in thepostmodernist attitude of "no methods", and subsequently in the strong adoption ofscientific ways of thinking for design research. The situation regarding methodologyhas been characterized by unproductive dualisms such as scientific vs. designerlymethods, normative vs. descriptive methods, research vs. design. The potential of theearly (1st generation) methods is neglected and the practical usefulness of designresearch is impeded. The suggestion for 2nd generation methods, conceived asdiscursive instruments, as discussed by Rittel and others has hardly been taken up indesign. The development of MAPS is aimed at the support of practice-oriented design,innovation and research processes. The long-term aim is the development of anintegrated knowledge and communication platform for research through design. MAPSis based upon the idea of a productive reconciliation of the strong dualisms between"scientific" and "designerly" modes of inquiry and supports the emerging concept ofdesign thinking. The paper reports on the ongoing research and development processfrom MAPS1.0 towards MAPS2.0 and beyond.
Beyond Dualisms in Methodology. An Integrative Design Research Medium "MAPS" and some Reflections
(2008)
Design research is an academic issue and increasingly an essential success factor for industrial, organizational and social innovation. The fierce rejection of 1st generation design methods in the early 1970s resulted in the postmodernist attitude of "no methods", and subsequently, after more than a decade, in the strong adoption of scientific methods, or "the" scientific method, for design research. The current situation regarding methodology is characterized by unproductive dualisms such as scientific methods vs. designerly methods, normative methods vs. descriptive methods, research vs. design. The potential of the early (1st generation) methods is neglected and the practical usefulness of design research is impeded. The suggestion for 2nd generation methods as discussed by Rittel and others has hardly been taken up in design. The development of a methodological tool / medium for research through design – MAPS1 – (which is the central part of the paper) presents the cause and catalyst for some reflections about the usability / desirability / usefulness of methodical support for the design (research) process.