@misc{PanzerBenderGronau2022, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Neural agent-based production planning and control}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, issn = {1867-5808}, doi = {10.25932/publishup-60477}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604777}, pages = {26}, year = {2022}, abstract = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.}, language = {en} } @article{PanzerBenderGronau2022, author = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert}, title = {Neural agent-based production planning and control}, series = {Journal of Manufacturing Systems}, volume = {65}, journal = {Journal of Manufacturing Systems}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0278-6125}, doi = {10.1016/j.jmsy.2022.10.019}, pages = {743 -- 766}, year = {2022}, abstract = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.}, language = {en} } @inproceedings{GrumThimRolingetal.2023, author = {Grum, Marcus and Thim, Christof and Roling, Wiebke and Sch{\"u}ffler, Arnulf and Kluge, Annette and Gronau, Norbert}, title = {AI case-based reasoning for artificial neural networks}, series = {Artificial intelligence and industrial applications}, volume = {771}, booktitle = {Artificial intelligence and industrial applications}, editor = {Masrour, Tawfik and El Hassani, Ibtissam and Barka, Noureddine}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-43523-2}, doi = {10.1007/978-3-031-43524-9_2}, pages = {17 -- 35}, year = {2023}, abstract = {Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.}, language = {en} } @phdthesis{Smirnov2023, author = {Smirnov, Artem}, title = {Understanding the dynamics of the near-earth space environment utilizing long-term satellite observations}, doi = {10.25932/publishup-61371}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-613711}, school = {Universit{\"a}t Potsdam}, pages = {xxxvi, 286}, year = {2023}, abstract = {The near-Earth space environment is a highly complex system comprised of several regions and particle populations hazardous to satellite operations. The trapped particles in the radiation belts and ring current can cause significant damage to satellites during space weather events, due to deep dielectric and surface charging. Closer to Earth is another important region, the ionosphere, which delays the propagation of radio signals and can adversely affect navigation and positioning. In response to fluctuations in solar and geomagnetic activity, both the inner-magnetospheric and ionospheric populations can undergo drastic and sudden changes within minutes to hours, which creates a challenge for predicting their behavior. Given the increasing reliance of our society on satellite technology, improving our understanding and modeling of these populations is a matter of paramount importance. In recent years, numerous spacecraft have been launched to study the dynamics of particle populations in the near-Earth space, transforming it into a data-rich environment. To extract valuable insights from the abundance of available observations, it is crucial to employ advanced modeling techniques, and machine learning methods are among the most powerful approaches available. This dissertation employs long-term satellite observations to analyze the processes that drive particle dynamics, and builds interdisciplinary links between space physics and machine learning by developing new state-of-the-art models of the inner-magnetospheric and ionospheric particle dynamics. The first aim of this thesis is to investigate the behavior of electrons in Earth's radiation belts and ring current. Using ~18 years of electron flux observations from the Global Positioning System (GPS), we developed the first machine learning model of hundreds-of-keV electron flux at Medium Earth Orbit (MEO) that is driven solely by solar wind and geomagnetic indices and does not require auxiliary flux measurements as inputs. We then proceeded to analyze the directional distributions of electrons, and for the first time, used Fourier sine series to fit electron pitch angle distributions (PADs) in Earth's inner magnetosphere. We performed a superposed epoch analysis of 129 geomagnetic storms during the Van Allen Probes era and demonstrated that electron PADs have a strong energy-dependent response to geomagnetic activity. Additionally, we showed that the solar wind dynamic pressure could be used as a good predictor of the PAD dynamics. Using the observed dependencies, we created the first PAD model with a continuous dependence on L, magnetic local time (MLT) and activity, and developed two techniques to reconstruct near-equatorial electron flux observations from low-PA data using this model. The second objective of this thesis is to develop a novel model of the topside ionosphere. To achieve this goal, we collected observations from five of the most widely used ionospheric missions and intercalibrated these data sets. This allowed us to use these data jointly for model development, validation, and comparison with other existing empirical models. We demonstrated, for the first time, that ion density observations by Swarm Langmuir Probes exhibit overestimation (up to ~40-50\%) at low and mid-latitudes on the night side, and suggested that the influence of light ions could be a potential cause of this overestimation. To develop the topside model, we used 19 years of radio occultation (RO) electron density profiles, which were fitted with a Chapman function with a linear dependence of scale height on altitude. This approximation yields 4 parameters, namely the peak density and height of the F2-layer and the slope and intercept of the linear scale height trend, which were modeled using feedforward neural networks (NNs). The model was extensively validated against both RO and in-situ observations and was found to outperform the International Reference Ionosphere (IRI) model by up to an order of magnitude. Our analysis showed that the most substantial deviations of the IRI model from the data occur at altitudes of 100-200 km above the F2-layer peak. The developed NN-based ionospheric model reproduces the effects of various physical mechanisms observed in the topside ionosphere and provides highly accurate electron density predictions. This dissertation provides an extensive study of geospace dynamics, and the main results of this work contribute to the improvement of models of plasma populations in the near-Earth space environment.}, language = {en} } @article{Omel'chenkoLaing2022, author = {Omel'chenko, Oleh and Laing, Carlo R.}, title = {Collective states in a ring network of theta neurons}, series = {Proceedings of the Royal Society of London. Series A, Mathematical, physical and engineering sciences}, volume = {478}, journal = {Proceedings of the Royal Society of London. Series A, Mathematical, physical and engineering sciences}, number = {2259}, publisher = {Royal Society}, address = {London}, issn = {1364-5021}, doi = {10.1098/rspa.2021.0817}, pages = {23}, year = {2022}, abstract = {We consider a ring network of theta neurons with non-local homogeneous coupling. We analyse the corresponding continuum evolution equation, analytically describing all possible steady states and their stability. By considering a number of different parameter sets, we determine the typical bifurcation scenarios of the network, and put on a rigorous footing some previously observed numerical results.}, language = {en} } @article{RabovskyMcClelland2020, author = {Rabovsky, Milena and McClelland, James L.}, title = {Quasi-compositional mapping from form to meaning}, series = {Philosophical transactions of the Royal Society of London : B, Biological sciences}, volume = {375}, journal = {Philosophical transactions of the Royal Society of London : B, Biological sciences}, number = {1791}, publisher = {Royal Society}, address = {London}, issn = {0962-8436}, doi = {10.1098/rstb.2019.0313}, pages = {9}, year = {2020}, abstract = {We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of human language processing should explain both the outcome of the comprehension process and the continuous internal processes underlying this performance. These points motivate our discussion of a neural network model of sentence comprehension, the Sentence Gestalt model, which we have used to account for the N400 component of the event-related brain potential (ERP), which tracks meaning processing as it happens in real time. The model, which shares features with recent deep learning-based language models, simulates N400 amplitude as the automatic update of a probabilistic representation of the situation or event described by the sentence, corresponding to a temporal difference learning signal at the level of meaning. We suggest that this process happens relatively automatically, and that sometimes a more-controlled attention-dependent process is necessary for successful comprehension, which may be reflected in the subsequent P600 ERP component. We relate this account to current deep learning models as well as classic linguistic theory, and use it to illustrate a domain general perspective on some specific linguistic operations postulated based on compositional analyses of natural language. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.}, language = {en} } @article{Rabovsky2020, author = {Rabovsky, Milena}, title = {Change in a probabilistic representation of meaning can account for N400 effects on articles}, series = {Neuropsychologia : an international journal in behavioural and cognitive neuroscience}, volume = {143}, journal = {Neuropsychologia : an international journal in behavioural and cognitive neuroscience}, publisher = {Elsevier}, address = {Oxford}, issn = {0028-3932}, doi = {10.1016/j.neuropsychologia.2020.107466}, pages = {7}, year = {2020}, abstract = {Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles' cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.}, language = {en} } @article{HempelAdolphsLandwehretal.2020, author = {Hempel, Sabrina and Adolphs, Julian and Landwehr, Niels and Willink, Dilya and Janke, David and Amon, Thomas}, title = {Supervised machine learning to assess methane emissions of a dairy building with natural ventilation}, series = {Applied Sciences}, volume = {10}, journal = {Applied Sciences}, number = {19}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {10.3390/app10196938}, pages = {21}, year = {2020}, abstract = {A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10\%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.}, language = {en} } @article{Rabovsky2020, author = {Rabovsky, Milena}, title = {Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model}, series = {Neuropsychologia}, volume = {143}, journal = {Neuropsychologia}, publisher = {Elsevier}, address = {Amsterdam}, pages = {7}, year = {2020}, abstract = {Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles' cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.}, language = {en} } @misc{Rabovsky2020, author = {Rabovsky, Milena}, title = {Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, issn = {1866-8364}, doi = {10.25932/publishup-52698}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-526988}, pages = {9}, year = {2020}, abstract = {Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles' cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.}, language = {en} }