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A comparative analysis of two different approaches developed to deal with molecular relaxation in photoacoustic spectroscopy is here reported. The first method employs a statistical analysis based on partial least squares regression, while the second method relies on the development of a digital twin of the photoacoustic sensor based on the theoretical modelling of the occurring relaxations. Methane detection within a gas matrix of synthetic air with variable humidity level is selected as case study. An interband cascade laser emitting at 3.345 µm is used to target methane absorption features. Two methane concentration ranges are explored targeting different absorptions, one in the order of part-per-million and one in the order of percent, while water vapor absolute concentration was varied from 0.3 % up to 2 %. The results achieved employing the detection techniques demonstrated the possibility to efficiently retrieve the target gas concentrations with accuracy > 95 % even in the case of strong influence of relaxation effects.
Algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances’ meta-features and algorithms’ landmark performances, Machine Learning (ML) approaches are applied to solve AS problems. However, the standard training process of benchmark ML approaches in AS either needs to train the models specifically for every algorithm or relies on the sparse one-hot encoding as the algorithms’ representation. To escape these intermediate steps and form the mapping function directly, we borrow the learning to rank framework from Recommender System (RS) and embed the bi-linear factorization to model the algorithms’ performances in AS. This Bi-linear Learning to Rank (BLR) has proven to work with competence in some AS scenarios and thus is also proposed as a benchmark approach. Thinking from the evaluation perspective in the modern AS challenges, precisely predicting the performance is usually the measuring goal. Though approaches’ inference time also needs to be counted for the running time cost calculation, it’s always overlooked in the evaluation process. The multi-objective evaluation metric Adjusted Ratio of Root Ratios (A3R) is therefore advocated in this paper to balance the trade-off between the accuracy and inference time in AS. Concerning A3R, BLR outperforms other benchmarks when expanding the candidates range to TOP3. The better effect of this candidates expansion results from the cumulative optimum performance during the AS process. We take the further step in the experimentation to represent the advantage of such TOPK expansion, and illustrate that such expansion can be considered as the supplement for the convention of TOP1 selection during the evaluation process.
Dieser Artikel richtet sich an IT-Führungskräfte, die die Nutzung von Cloud Computing (CC) in ihren Unternehmen fördern wollen. Eine Fallstudie des Konzerns Continental AG zeigt, wie Continental CC in großem Maßstab für die Organisation nutzbar machte. Das Unternehmen ging dabei in drei Schritten vor: Nach einer Experimentierphase wurde der Einsatz zunehmend professionalisiert und CC abschließend umfangreich nutzbar gemacht. Hieraus können drei Hand-lungsempfehlungen für Führungskräfte abgeleitet werden: (1) die CC-Strategie nach dem Delivery-Modell differenzieren, (2) in die Integration von Infrastructure-as-a-Service (IaaS)- und Platform-as-a-Service (PaaS)-Anbietern investieren und (3) den Wissenstransfer innerhalb des Unternehmens fördern, um Veränderungen zu ermöglichen.
For its third installment, the Data Science Challenge of the 19th symposium “Database Systems for Business, Technology and Web” (BTW) of the Gesellschaft für Informatik (GI) tackled the problem of predictive energy management in large production facilities. For the first time, this year’s challenge was organized as a cooperation between Technische Universität Dresden, GlobalFoundries, and ScaDS.AI Dresden/Leipzig. The Challenge’s participants were given real-world production and energy data from the semiconductor manufacturer GlobalFoundries and had to solve the problem of predicting the energy consumption for production equipment. The usage of real-world data gave the participants a hands-on experience of challenges in Big Data integration and analysis. After a leaderboard-based preselection round, the accepted participants presented their approach to an expert jury and audience in a hybrid format. In this article, we give an overview of the main points of the Data Science Challenge, like organization and problem description. Additionally, the winning team presents its solution.
Experimental investigations are conducted to quantify the influence of specimen thickness and orientation on the mechanical properties of selective laser melted stainless steel 316 L. The results indicate that the mechanical strength and ductility increase with increasing specimen thickness until a saturation value is reached from a specimen thickness of about 2 mm. Specimen orientation dependency is pronounced for thin specimens (<1.5 mm), whereas only small deviations in strength are observed for thicker specimens with orientations of 30°, 45° and 90° to build direction. The mechanical properties of the specimen orientation of 0° to build direction shows great deviation to the other orientations and the smallest overall strength. A reliable design of selective laser melted components should account for specimen thickness and orientation, e. g. by a correction factor. Furthermore, it is recommended to avoid loads vertical (90°) and parallel (0°) to build direction to guarantee higher ductility and strength.
We conceptualize the new phenomenon of the Fractional Chief InformationOfficer (CIO) as a part-time executive who usually works for more than one pri-marily small- to medium-sized enterprise (SME) and develop promising avenuesfor future research on Fractional CIOs. We conduct an empirical study by drawingon semi-structured interviews with 40 individuals from 10 different countries whooccupy a Fractional CIO role. We derive a definition for the Fractional CIO, dis-tinguish it from other forms of employment, and compare it with existing researchon CIO roles. Further, we find four salient engagement types of Fractional CIOsoffering value for SMEs in various situations: Strategic IT management, Restruc-turing, Rapid scaling, and Hands-on support. The results reveal similarities withexisting CIO roles as well as novel insights concerning the different engagementtypes. Lastly, we propose a research agenda for the Fractional CIO field, based onfour research themes derived from existing CIO research and insights from theinterviews.
We present the Regensburg Breast Shape Model (RBSM)—a 3D statistical shape model of the female breast built from 110 breast scans acquired in a standing position, and the first publicly available. Together with the model, a fully automated, pairwise surface registration pipeline used to establish dense correspondence among 3D breast scans is introduced. Our method is computationally efficient and requires only four landmarks to guide the registration process. A major challenge when modeling female breasts from surface-only 3D breast scans is the non-separability of breast and thorax. In order to weaken the strong coupling between breast and surrounding areas, we propose to minimize the variance outside the breast region as much as possible. To achieve this goal, a novel concept called breast probability masks (BPMs) is introduced. A BPM assigns probabilities to each point of a 3D breast scan, telling how likely it is that a particular point belongs to the breast area. During registration, we use BPMs to align the template to the target as accurately as possible inside the breast region and only roughly outside. This simple yet effective strategy significantly reduces the unwanted variance outside the breast region, leading to better statistical shape models in which breast shapes are quite well decoupled from the thorax. The RBSM is thus able to produce a variety of different breast shapes as independently as possible from the shape of the thorax. Our systematic experimental evaluation reveals a generalization ability of 0.17 mm and a specificity of 2.8 mm. To underline the expressiveness of the proposed model, we finally demonstrate in two showcase applications how the RBSM can be used for surgical outcome simulation and the prediction of a missing breast from the remaining one. Our model is available at https://www.rbsm.re-mic.de/.
We present a sensitive UV LED photoacoustic setup for the detection of gaseous acetone and discuss its applicability towards breath analysis. We investigated the performance of the sensor for low acetone concentrations down to 0.1 parts per million (ppmV). The influences of temperature, flow, pressure, optical power and LED duty cycle on the measured signal have been examined. To gain a better understanding of the different effects on the photoacoustic signal, correlation analysis was applied and feature importance was determined using a large measured dataset. Furthermore, the cross-sensitivities towards O2, CO2 and H2O have been studied extensively. Finally, the sensor’s performance to detect acetone between 0.1–1 ppmV within gas mixtures simulating breath exhale conditions has been investigated, too. With a limit of detection (LoD) of 12.5 parts per billion (ppbV) (3σ) measured under typical breath exhale gas mixture conditions, the sensor demonstrated a high potential for the application of acetone detection in human breath analysis.
The performance of a photoacoustic trace gas sensor for the detection of acetone in N2 and synthetic air is reported. The sensor system utilises an amplitude modulated UV LED. The light source has an emission maximum at 278 nm and a maximum CW output power of 300 mW according to the datasheet. Three different collimating and focusing approaches have been investigated to guide the highly divergent LED light into the acoustic resonator of the photoacoustic measurement cell. A 3D printed aluminium cell was designed to optimize light coupling by simultaneously minimizing the photoacoustic background signal generation. Hence, the diameter of the resonator was set to a comparable large diameter of 10 mm and the inner walls of the resonator were mirror polished. The additive manufacturing procedure allowed for integration of a spirally formed gas channel, enabling gas heating prior to detection. The sensor performance was investigated by measuring acetone in N2 and synthetic air at different concentrations. The UV LED current was set to 86 % of the maximum value according to the datasheet of the light source in order to increase the lifetime and thermal stability. An Allan-Werle deviation analysis validates a stable sensor performance. The limit of detection (LoD) was determined at a 3σ noise level with a 10 s lock-in amplifier time constant by sampling data points over 20 s with a data acquisition rate of 5 Hz. LoDs of 80.8 ppbV and 19.6 ppbV were obtained for acetone in N2 and synthetic air, respectively.