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Multifocal intraocular lenses incorporate a variety of design considerations, including dimensioning of the base monofocal shape and the diffraction grating. While studying three different lens models, we present a practical approach for mathematical modelling and evaluation of these geometries. Contrary to typical lens measurement methods, non-contact measurements were performed on the Alcon SN6AD1, HumanOptics MS 612 DAY and the AMO ZMA00 lenses using a confocal microscope. Subsequent data processing includes centering, tilting correction, filtering and an algorithmic decomposition into a conic and polynomial part and the diffraction grating. Lastly, evaluation of fitting parameters and grating shape is done to allow for inferences about further optical properties. Results and analysis show the confocal microscope to be a suitable imaging method for lens measurements. The processing of this data enables the reconstruction of the annular diffraction grating over the complete lens diameter. Apodization, near addition and diffraction efficiency characteristics are found utilizing the grating shape. Additionally, near-optical axis curvature, asphericity and higher order polynomials are identified qualitatively from the reconstruction of the monofocal base form. Derived properties also include the lens optical base and addition power. By making use of the surface geometries, as well as the lens’ material and thickness, a full lens model can be created for further studies. In summary, our analytical approach enables the insight to various intraocular lens design decisions. Furthermore, this procedure is suitable for lens model creation for research and simulation.
Feasibility Study of Wheel Torque Prediction with a Recurrent Neural Network Using Vehicle Data
(2023)
In this paper, we present a feasibility study on predicting the torque signal of a passenger car with the help of a neural network. In addition, we analyze the possibility of using the proposed model structure for temperature prediction. This was carried out with a neural network, specifically a three-layer long short-term memory (LSTM) network. The data used were real road load data from a Jaguar Land Rover Evoque with a Twinster gearbox from GKN. The torque prediction generated good results with an accuracy of 55% and a root mean squared error (RMSE) of 49 Nm, considering that the data were not generated under laboratory conditions. However, the performance of predicting the temperature signal was not satisfying with a coefficient of determination (R2) score of −1.396 and an RMSE score of 69.4 °C. The prediction of the torque signal with the three-layer LSTM network was successful but the transferability of the network to another signal (temperature) was not proven. The knowledge gained from this investigation can be of importance for the development of virtual sensor technology.
The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents (“oracles”) and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models.
We consider a risk model in discrete time with dividends and capital injections. The goal is to maximise the value of a dividend strategy. We show that the optimal strategy is of barrier type. That is, all capital above a certain threshold is paid as dividend. A second problem adds tax to the dividends but an injection leads to an exemption from tax. We show that the value function fulfils a Bellman equation. As a special case, we consider the case of premia of size one. In this case we show that the optimal strategy is a two barrier strategy. That is, there is a barrier if a next dividend of size one can be paid without tax and a barrier if the next dividend of size one will be taxed. In both models, we illustrate the findings by de Finetti’s example.
Changing our unsustainable linear water management pattern is necessary to face growing global water challenges. This article proposes an integrated framework to analyse and understand the role of different contextual conditions in the possible transition towards water circularity. Our framework combines a systematic multi-level perspective to explore the water system and the institutional work theory for technology legitimation. The framework consists of the following stages: (1) describing and understanding the water context, (2) assessment of the selected technologies’ circularity level, (3) assessment of the alternative circular technologies’ legitimacy, and (4) identification of the legitimation actions to support the upscale of alternative circular technologies. The practical applicability of the integrated assessment framework and its four assessment stages was demonstrated in the exploration of circular water technologies for the horticulture sector in Westland, the Netherlands. The results revealed the conditions that hinder or enable the legitimation of the circular water technologies, such as political environmentalism, trust in water governing authorities, and technical, financial, and knowledge capabilities.
Editorial
(2020)
In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.
As the population ages, the demand for care for older adults is increasing. To maintain their independence and autonomy, even with declining health, assistive technologies such as connected medical devices or social robots can be useful. In previous work, we introduced a novel health monitoring system that combines commercially available products with apps designed specifically for older adults. The system is intended for the long-term collection of subjective and objective health data. In this work, we present an exploratory user experience (UX) and usability study we conducted with older adults as the target group of the system and with younger expert users who tested our msystem. All participants interacted with a social robot conducting a health assessment and tested sensing devices and an app for data visualization. The UX and usability of the individual components of the system were rated highly in questionnaires in all sessions. All participants also said they would use such a system in their everyday lives, demonstrating the potential of these systems for self-managing users’ health. Finally, we found factors such as previous experience with social robots and technological expertise to have an influence on the reported UX of the users.
3d printing is capable of providing dose individualization for pediatric medicines and translating the precision medicine approach into practical application. In pediatrics, dose individualization and preparation of small dosage forms is a requirement for successful therapy, which is frequently not possible due to the lack of suitable dosage forms. For precision medicine, individual characteristics of patients are considered for the selection of the best possible API in the most suitable dose with the most effective release profile to improve therapeutic outcome. 3d printing is inherently suitable for manufacturing of individualized medicines with varying dosages, sizes, release profiles and drug combinations in small batch sizes, which cannot be manufactured with traditional technologies. However, understanding of critical quality attributes and process parameters still needs to be significantly improved for this new technology. To ensure health and safety of patients, cleaning and process validation needs to be established. Additionally, adequate analytical methods for the in-process control of intermediates, regarding their printability as well as control of the final 3d printed tablets considering any risk of this new technology will be required. The PolyPrint consortium is actively working on developing novel polymers for fused deposition modeling (FDM) 3d printing, filament formulation and manufacturing development as well as optimization of the printing process, and the design of a GMP-capable FDM 3d printer. In this manuscript, the consortium shares its views on quality aspects and measures for 3d printing from drug-loaded filaments, including formulation development, the printing process, and the printed dosage forms. Additionally, engineering approaches for quality assurance during the printing process and for the final dosage form will be presented together with considerations for a GMP-capable printer design.
The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.