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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.
A cable-driven parallel robot (CDPR) was developed for the installation of curtain wall modules (CWM). The research addressed the question of whether the CDPR was capable installing CWMs with sufficient accuracy while being competitive compared to conventional manual methods. In order to develop and test such a system, a conceptual framework that consisted of three sub-systems was defined. The tests, carried out in two close-to-real demonstration buildings, revealed an absolute accuracy of the CWM installation of 4 to 23 mm. The working time for installing a CWM was reduced to 0.51 h. The results also show that the system is competitive for a workspace greater than 96 m2 compared to conventional manual methods. However, improvements such as reducing the hours for setting up the CDPR on the one hand and achieving a faster and more robust MEE on the other hand will be still necessary in the future.
The Twistbeam axle suspension is a cheap and robust layout for rear axles at front wheel driven midsize cars. Appropriate models have to take the elastic deformation of the torsion beam into account. A Finite Element approach requires detailed informations of the material properties and the shape which are usually only available in the final production stage. This paper presents a lumped mass model which can easily be integrated into a multibody vehicle model and can be used in the early stage of development. An approximation by the design kinematics further reduces the complexity of the model and considers only the kinematic properties of the Twistbeam suspension. Simulations using a nonlinear and three-dimensional vehicle model with different maneuvers, such as steady-state cornering, step steer input, and driving straight ahead on random road, demonstrate the performance and, in particular, the difference of the presented Twistbeam suspension models.
The TMeasy is a tyre model suitable for vehicle handling analyses and enables easy parametrisation. Recently, a convenient interface to Modelica was implemented by DLR to support the TMeasy also for vehicle modelling in multi-physical domains. This paper focuses especially on the particular problem of reliable reproduction of the tyre’s bore torque which occurs during parking manoeuvres. It outlines the theory behind it, discusses the Modelica interface implementation, and presents the results of parameter identification which were achieved based on real experiments with DLR’s research platform ROboMObil.
The performance of nanoelectronic and molecular electronic devices relies strongly on the employed functional units and their addressability, which is often a matter of appropriate interfaces and device design. Here, we compare two promising designs to build solid-state electronic devices utilizing the same functional unit. Optically addressable Ru-terpyridine complexes were incorporated in supramolecular wires or employed as ligands of gold nanoparticles and contacted by nanoelectrodes. The resulting small-area nanodevices were thoroughly electrically characterized as a function of temperature and light exposure. Differences in the resulting device conductance could be attributed to the device design and the respective transport mechanism, that is, thermally activated hopping conduction in the case of Ru-terpyridine wire devices or sequential tunneling in nanoparticle-based devices. Furthermore, the conductance switching of nanoparticle-based devices upon 530 nm irradiation was attributed to plasmon-induced metal-to-ligand charge transfer in the Ru-terpyridine complexes used as switching ligands. Finally, our results reveal a superior device performance of nanoparticle-based devices compared to molecular wire devices based on Ru-terpyridine complexes as functional units.
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.
Identification of bats is most practically done by exploiting the characteristic features of their echolocation calls. This usually involves expert knowledge, expensive equipment and time-consuming post processing of previously recorded calls. Automated solutions exist, but are usually not as accurate as human experts. We present an automated solution for the processing of bat calls and identification of bat species with extremely high classification accuracy that can be used during live recording or in an automated post-processing software. Our algorithm is the first application of a Deep Convolutional Neural Network to classify bat species based on sound spectrogram images of their echolocation calls. We tested several deep CNN architectures including a modified Google Inception and a ResNet50 architecture. The nets were trained on a very large call database consisting of images of snippets of call spectrograms. All our software was developed in the Python programming language and an executable of the software is available on request.
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.
Dubbing, i.e., the lip-synchronous translation and revoicing of audio-visual media into a target language from a different source language, is essential for the full-fledged reception of foreign audio-visual media, be it movies, instructional videos or short social media clips. In this paper, we objectify influences on the ‘dubbability’ of translations, i.e., how well a translation would be synchronously revoiceable to the lips on screen. We explore the value of traditional heuristics used in evaluating the qualitative aspects, in particular matching bilabial consonants and the jaw opening while producing vowels, and control for quantity, i.e., that translations are similar to the source in length. We perform an ablation study using an adversarial neural classifier which is trained to differentiate “true” dubbing translations from machine translations. While we are able to confirm the value of matching lip closure in dubbing, we find that the opening angle of the jaw as determined by the realized vowel may be less relevant than frequently considered in audio-visual translation.
We present an open source plugin for live subtitling in the popular open source video conferencing software BigBlueBut-ton. Our plugin decodes each speaker’s audio stream separately and in parallel, thereby obliviating the need for speaker di-arization and seamlessly handling overlapped talk. Any Kaldi-compatible nnet3 model can be used with our plugin and we demonstrate it using freely available TDNN-HMM-based ASR models for English and German. Our subtitles can be used as they are (e.g., in loud environments) or can form the basis for further NLP processes. Our tool can also simplify the collection of remotely recorded multi-party dialogue corpora.