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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.
In its Renewables 2022 Report, the International Energy Agency (IEA) projects that the share of renewable energies in the global energy mix will increase from 22.8% in 2015 to 38.1% in 2027. This trend goes hand-in-hand with increasing construction of plants for the generation of renewable energies, leading to increased demand for (re)insurance. Comparable to the development of traditional energy sources, the hedging of current risks is a key element in the further development of renewable energies. According to projections by the IEA, by 2027 most of the energy from renewable sources will be generated using photovoltaics or solar as well as onshore and offshore wind.
Die IEA prognostiziert einen Anstieg des globalen Anteils erneuerbarer Energien am Energiemix von 22,8% in 2015 auf 38,1% in 2027, begleitet von verstärktem (Rück-) Versicherungsbedarf. Sechs Herausforderungen in der Rückversicherung erneuerbarer Energien werden skizziert, darunter komplexe Underwriting-Anforderungen, zunehmende Integration von ESG-Faktoren, Bewertung der Zeichnungspolitik der Zedenten, Mangel an Schadenerfahrung, Naturkatastrophenexposition und die Anpassung von Rückversicherungsformen an die spezifischen Bedürfnisse. Die Zukunft erfordert den Aufbau von Expertise für erneuerbare Energien und eine ganzheitliche Bewertung von Schadensszenarien. Besonders im Offshore Wind Geschäft bleibt ein Druck auf Preise und Bedingungen bestehen.
As a customer, it can be frustrating to face an empty shelf in a store. The market does not always realize that a product has been out of stock for a while, as the item is still listed as in stock in the inventory management system. To address this issue, a camera should be used to check for Out-of-Stock (OOS) situations.
This master thesis evaluates different model configurations of Artificial Neural Networks (ANNs) to determine which one best detects OOS situations in the market using images. To create a dataset, 2,712 photos were taken in six stores. The photos clearly show whether there is a gap on the shelf or if the product is in stock. Based on the pre-trained VGG16 model from Keras, two fully connected layers were implemented, with 36 different ANNs differing in the optimization method and activation function pairings. In total, 216 models were generated in this thesis to investigate the effects of three different optimization methods combined with twelve different activation function pairings. An almost balanced ratio of OOS and in-stock data was used to generate these models.
The evaluation of the generated OOS models shows that the FTRL optimization method achieved the least favorable results and is therefore not suitable for this application. Model configurations using the Adam or SGD optimization methods achieve much better results. Of the top six model configurations, five use the Adam optimization method and one uses SGD. They all achieved an accuracy of at least 93% and were able to predict the Recall for the OOS class with at least 91%.
As the data ratio between OOS and in-stock data did not correspond to reality in the previously generated models, the in-stock images were augmented. Including the augmented images, new OOS models were generated for the top six model configurations. The results of these OOS models show no convergences. This suggests that more epochs in the training phase lead to better results. However, the results of the OOS model using the Adam optimization method and the Sigmoid and ReLU activation functions stand out positively. It achieved the best result with an accuracy of 97.91% and a Recall of the OOS class of 87.82%.
Overall, several OOS models have the potential to increase both market sales and customer satisfaction. In a future study, the OOS models should be installed in the market to evaluate their performance under real conditions. The resulting insights can be used for continuous optimization of the model.
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.
Die Handelsspannungen zwischen den USA und China, die sich seit Anfang 2017 verschärft haben, hatten weitreichende Folgen. Am 6. Juli 2018 verhängten die USA Zölle auf chinesische Waren im Wert von 34 Mrd. USD, was Maßnahmen seitens Chinas nach sich zog. Dieser anhaltende Handelsstreit hat zu wirtschaftlichen Spannungen zwischen den beiden größten Volkswirtschaften der Welt geführt, die sich in der Einführung von Zöllen, Handelsbeschränkungen und geopolitischen Unsicherheiten niederschlagen. Die Auswirkungen dieser Maßnahmen waren in verschiedenen Sektoren spürbar und führten zu Unterbrechungen in den globalen Lieferketten, erhöhten Produktionskosten und Nachfrageschwankungen auf den Märkten. Unternehmen, die in diesem komplexen Handelsumfeld tätig sind, sehen sich nun mit erheblichen Unsicherheiten konfrontiert. Die Automobilindustrie, einschließlich Autos, Autoteile, Stahl und Aluminium, ist besonders betroffen.
The prolonged US-China trade tension, initiated in 2017, has led to significant consequences, impacting global supply chains and causing economic tension between the two largest economies. Particularly affecting the automotive sector, the trade war has influenced motor insurance premiums in China, contributing to a declining trend in non-life insurance growth rates from 2017 to 2021. However, a positive outlook is projected for 2023-2026, indicating potential recovery opportunities. The trade war's short-term impacts on the Chinese motor insurance market include increased costs, low premium growth, and economic challenges. In the long term, transformative changes, including market diversification, innovative products, data-driven pricing, and technology-enabled risk prevention, are expected to shape a dynamic and competitive motor insurance landscape in China, offering growth potential despite initial challenges.
To date, the establishment of high-titer stable viral packaging cells (VPCs) at large scale for gene therapeutic applications is very time- and cost-intensive. Here we report the establishment of three human suspension 293-F-derived ecotropic MLV-based VPCs. The classic stable transfection of an EGFP-expressing transfer vector resulted in a polyclonal VPC pool that facilitated cultivation in shake flasks of 100 mL volumes and yielded high functional titers of more than 1 × 106 transducing units/mL (TU/mL). When the transfer vector was flanked by transposon terminal inverted repeats (TIRs) and upon co-transfection of a plasmid encoding for the transposase, productivities could be slightly elevated to more than 3 × 106 TU/mL. In contrast and using mRNA encoding for the transposase, as a proof of concept, productivities were drastically improved by more than ten-fold exceeding 5 × 107 TU/mL. In addition, these VPC pools were generated within only 3 weeks. The production volume was successfully scaled up to 500 mL employing a stirred-tank bioreactor (STR). We anticipate that the stable transposition of transfer vectors employing transposase transcripts will be of utility for the future establishment of high-yield VPCs producing pseudotype vector particles with a broader host tropism on a large scale.
To date, the establishment of high-titer stable viral packaging cells (VPCs) at large scale for gene therapeutic applications is very time- and cost-intensive. Here we report the establishment of three human suspension 293-F-derived ecotropic MLV-based VPCs. The classic stable transfection of an EGFP-expressing transfer vector resulted in a polyclonal VPC pool that facilitated cultivation in shake flasks of 100 mL volumes and yielded high functional titers of more than 1 × 106 transducing units/mL (TU/mL). When the transfer vector was flanked by transposon terminal inverted repeats (TIRs) and upon co-transfection of a plasmid encoding for the transposase, productivities could be slightly elevated to more than 3 × 106 TU/mL. In contrast and using mRNA encoding for the transposase, as a proof of concept, productivities were drastically improved by more than ten-fold exceeding 5 × 107 TU/mL. In addition, these VPC pools were generated within only 3 weeks. The production volume was successfully scaled up to 500 mL employing a stirred-tank bioreactor (STR). We anticipate that the stable transposition of transfer vectors employing transposase transcripts will be of utility for the future establishment of high-yield VPCs producing pseudotype vector particles with a broader host tropism on a large scale.
Maximising Distribution Grid Utilisation by Optimising E-Car Charging Using Smart Meter Gateway Data
(2023)
The transition towards climate neutrality will result in an increase in electrical vehicles, as well as other electric loads, leading to higher loads on electrical distribution grids. This paper presents an optimisation algorithm that enables the integration of more loads into distribution grid infrastructure using information from smart meters and/or smart meter gateways. To achieve this, a mathematical programming formulation was developed and implemented. The algorithm determines the optimal charging schedule for all electric vehicles connected to the distribution grid, taking into account various criteria to avoid violating physical grid limitations and ensuring non-discriminatory charging of all electric vehicles on the grid while also optimising grid operation. Additionally, the expandability of the infrastructure and fail-safe operation are considered through the decentralisation of all components. Various scenarios are modelled and evaluated in a simulation environment. The results demonstrate that the developed optimisation algorithm allows for higher transformer loads compared to a P(U) control approach, without causing grid overload as observed in scenarios without optimisation or P(U) control.