Use In Production Environments Research Articles

Investigation of coherence scanning Mirau interferometers with integrated vibration compensation for use in production environments

Investigation of coherence scanning Mirau interferometers with integrated vibration compensation for use in production environments

Flame: an open source framework for model development, hosting, and usage in production environments

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

Monte Carlo Tree Search for online decision making in smart industrial production

This paper considers the issue of rapid automated decision making in changing factory environments, situations including human-robot collaboration, mass customisation and the need to rapidly adapt activities to new conditions. The approach taken is to adapt the Monte Carlo Tree Search (MCTS) algorithm to provide online choices for the possible actions of machines and workers, interleaving them dynamically in response to the changing conditions of the production process. This paper describes how the MCTS algorithm has been adapted for use in production environments and then the proposed method is illustrated by two examples of the system in use, one simulated and one in a physical test cell.

Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers

Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very important for investment decision-making by traders, portfolio managers and investors. Sentiment analysis models can provide an efficient method for extracting actionable signals from the news. However, financial sentiment analysis is challenging due to domain-specific language and unavailability of large labeled datasets. General sentiment analysis models are ineffective when applied to specific domains such as finance. To overcome these challenges, we design an evaluation platform which we use to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers. We perform more than one hundred experiments using publicly available datasets, labeled by financial experts. We start the evaluation with specific lexicons for sentiment analysis in finance and gradually build the study to include word and sentence encoders, up to the latest available NLP transformers. The results show improved efficiency of contextual embeddings in sentiment analysis compared to lexicons and fixed word and sentence encoders, even when large datasets are not available. Furthermore, distilled versions of NLP transformers produce comparable results to their larger teacher models, which makes them suitable for use in production environments.

Morphological analysis of the filamentous fungus Penicillium chrysogenum using flow cytometry\u2014the fast alternative to microscopic image analysis

An important parameter in filamentous bioreactor cultivations is the morphology of the fungi, due to its interlink to productivity and its dependency on process conditions. Filamentous fungi show a large variety of morphological forms in submerged cultures. These range from dispersed hyphae, to interwoven mycelial aggregates, to denser hyphal aggregates, the so-called pellets. Depending on the objective function of the bioprocess, different characteristics of the morphology are favorable and need to be quantified accurately. The most common method to quantitatively characterize morphology is image analysis based on microscopy. This method is work intensive and time consuming. Therefore, we developed a faster, at-line applicable, alternative method based on flow cytometry. Within this contribution, this novel method is compared to microscopy for a penicillin production process. Both methods yielded in comparable distinction of morphological sub-populations and described their morphology in more detail. In addition to the appropriate quantification of size parameters and the description of the hyphal region around pellets, the flow cytometry method even revealed a novel compactness parameter for fungal pellets which is not accessible via light microscopy. Hence, the here presented flow cytometry method for morphological analysis is a fast and reliable alternative to common tools with some new insights in the pellet morphology, enabling at-line use in production environments.

FGNG: A fast multi-dimensional growing neural gas implementation

The Growing Neural Gas algorithm (GNG) is a well-known classification algorithm that is capable of capturing topological relationships that exist in the input data. Unfortunately, simple implementations of the GNG algorithm have time complexity O(n2), where n is the number of nodes in the graph. This fact makes these implementations impractical for use in production environments where large data sets are used. This paper aims to propose an optimized implementation that breaks the O(n2) barrier and that addresses data in high-dimensional spaces without changing the GNG semantics. The experimental results show speedups of over 50 times for graphs with 200,000 nodes.

The Development of Criteria for the Prediction of Yarn Behaviour under Tension

The methods that have been used to define quality criteria for the prediction of yarn behaviour are critically reviewed. Work is described which leads to the definition of improved quality criteria for use in production environments and enables ring-spun yarns to be compared with open-end-spun yarns. An evaluation of the nature of the variation of results obtained from the measurement of strength and elongation confirms that the distributions of the results are adequately described by Gauss’s Law. A method for the visual representation of yarn quality in terms of the strength and elongation criteria for a given end-use is described.

Safe laser system design for production

The majority of industrial lasers used in manufacturing are Class IV lasers. Engineering and administrative control measures appropriate to that class of lasers must be enforced to ensure their safe use in production environments, i.e. they should be designed into Class 1 laser systems. In addition, several other design characteristics unique to industrial applications are normally incorporated into these Class I systems, such as interlocked enclosures, process monitoring by means of closed circuit television (CCTV) or microscope optics, and optical fibers as well as conventional, fixed optics for laser beam delivery. The accessible emission for an integrated production laser system must be below the Accessible Emission Limit for a Class I laser. This paper addresses solutions to safety design issues through examples of production industrial laser systems. The appropriate administrative control programs that complement the engineering control measures are also discussed.