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An improvement in the design process of sustainable peak power rating transformer for solar utility
(2025)
The transformer industry faces critical challenges, such as maintaining reliable production while meeting rising sustainability requirements. This paper introduces a significant advancement in the design and optimization of a transformer dedicated to a photovoltaic power plant, which exhibits a unique loading cycle distinct from standard power and distribution transformers. The main aim is to minimize the carbon footprint by enhancing the design process to improve operational performance, efficiency, and functional reliability. Such upgrades are essential for transitioning to a zero-emission electricity system and developing green energy projects.
In this paper, a transformer has been studied using a combination of electrical design and 3D finite element method simulation to evaluate various design parameters. An optimization study has been conducted using an innovative multi-objective genetic algorithm utilizing a cost function that factors in size and material costs to identify the most efficient and cost-effective design solutions.
The proposed design method was then validated through thermal model simulations and experimental tests based on the photovoltaic load cycle. A comparison of critical thermal parameters directly affecting the transformer’s lifetime and reliability was also drawn. The results were consistent with the expected outcomes, confirming the effectiveness and reliability of the proposed design methodology.
Abstract
This data descriptor contains information about an extensive production data set for a five-axis CNC milling process. Three geometrically different products were manufactured and relevant features from the numerical control of the machine were recorded. The recorded manufacturing process contains the preparation of the machine for the next product (changeover) as well as the machining process (production). The experimental manufacturing was organized with the aid of a changeover matrix to ensure that all possible changeover combinations for the three products were considered. The production was repeated five times, resulting in 30 manufacturing sessions and five complete changeover matrices. The data set was recorded in a laboratory environment. A rich feature set including i.e. the NC-code of the products, tool information, and a Jupyter notebook is provided with the data set.
Trees play a critical role in climate regulation, biodiversity, and carbon storage as they cover approximately 30% of the global land area. Nowadays, Machine Learning (ML)is key to automating large-scale tree species classification based on active and passive sensing systems, with a recent trend favoring data fusion approaches for higher accuracy. The use of 3D Deep Learning (DL) models has improved tree species classification by capturing structural and geometric data directly from point clouds. We propose a fully Multimodal Tree Species Classification Network (MMTSCNet) that processes Light Detection and Ranging (LiDAR) point clouds, Full-Waveform (FWF) data, derived features, and bidirectional, color-coded depth images in their native data formats without any modality transformation. We conduct several experiments as well as an ablation study to assess the impact of data fusion. Classification performance on the combination of Airborne Laser Scanning (ALS) data with FWF data scored the highest, achieving an Overall Accuracy (OA) of nearly 97%, a Mean Average F1-score (MAF) of nearly 97%, and a Kappa Coefficient of 0.96. Results for the other data subsets show that the ALS data in combination with or even without FWF data produced the best results, which was closely followed by the UAV-borne Laser Scanning (ULS) data. Additionally, it is evident that the inclusion of FWF data provided significant benefits to the classification performance, resulting in an increase in the MAF of +4.66% for the ALS data, +4.69% for the ULS data under leaf-on conditions, and +2.59% for the ULS data under leaf-off conditions. The proposed model is also compared to a state-of-the-art unimodal 3D-DL model (PointNet++) as well as a feature-based unimodal DL architecture (DSTCN). The MMTSCNet architecture outperformed the other models by several percentage points, depending on the characteristics of the input data.
This paper presents a new methodology for the design process of DC ripple filters for voltage source converters. It focuses on fast-switching, wide-bandgap-material-based converters. Therefore, a wide frequency range of up to 100 MHz is taken into consideration during the whole process. Different tools like analytic calculations, time-domain modelling, and the finite element method are used for different tasks in order to generate a realistic model in terms of filter effect and reliability. The models are validated by small-signal measurements using a vector network analyser as well as realistic high-power tests. The contribution of this paper is to provide a tool for DC link filter design to estimate the filter efficiency and the current stress on the filter elements with a special focus on WBG hardware.
This article examines the transformative effects of Smart Factory technologies - such as human-robot collaboration, intelligent assistance systems and cyber-physical production systems - on organizational design, with a particular focus on central fields of action for Human Resources management (HRM) and operational management. A case study of a German automotive supplier is used to examine how digitalization and automation are changing human work and organizational structures. Two future scenarios for organizational models are proposed: the swarm organization, which consists exclusively of highly qualified employees while robots take over routine tasks, and the polarized organization, which is characterized by a division between highly qualified specialists and low-skilled employees. Each scenario brings different challenges and opportunities for HR management, as companies need to adapt to digital skills, new models of collaboration and the management of a highly specialized or polarized workforce. This paper provides a conceptual framework and actionable insights for HRM and production management to manage the shift towards advanced, automated organizational models and ensure a smooth transition to the Smart Factory of the future.
Introduction: Schizophrenia imposes a significant burden on global public health and is associated with pervasive stigmatization, perpetuating misconceptions of danger and incompetence. This review examines the efficacy of simulation interventions in reducing stigmas associated with schizophrenia and fostering empathy towards affected individuals.
Methods: A comprehensive literature review spanning from August 2021 to September 2022 identified 14 relevant studies meeting inclusion criteria.
Results: Analysis revealed a diverse landscape of simulation-based interventions, characterized by variability in methodological rigor, intervention design, and technological modalities. While some studies demonstrated promising outcomes in stigma reduction and empathy enhancement, methodological limitations and inconsistencies underscore the need for cautious interpretation of findings. Furthermore, mixed outcomes in stigma characteristics and empathy development highlight the complexity of intervention effectiveness.
Discussion: Despite these challenges, simulation interventions, particularly when integrated with additional components, hold potential in mitigating stigmatization and promoting empathy. Future research should prioritize methodological rigor, comprehensive outcome assessment, and tailored intervention strategies to advance the field of stigma reduction in schizophrenia.
This paper presents a complete workflow for the evaluation of GaN transistors in voltage source inverters. With the associated high switching speed of transistors based on GaN, it is important to consider some critical points in the design phase as well as in the measurement setup in order to be able to utilise and verify the advantages of GaN properly. For this reason, the presented circuit board’s design focuses on a minimised power loop inductance. Simulation models, an analytical approach and measurement results with the aim of determining this inductance are compared with each other. A good compliance results between the presented methods. Additionally, the description of a test bench is given, which enables the performance of the opposition method. This setup allows the measurement of the designed H-bridge’s arising losses and the GaN-transistor’s switching behaviour. In comparison to the conventional double pulse method, this approach enables results that are more accurate for determining losses.
Improvisation in music therapy is a highly complex and diverse form of creativity, offering a wide variety of musical information for music therapists to work with. To address this diversity in research and analysis, it is common to combine a wide range of interdisciplinary scientific approaches. Microanalysis methods in music therapy provide highly insightful results on a detailed musical level in musical improvisation but come at the cost of a time-consuming analysis procedure. The automation of these methods in machine learning environments and the use of the wealth of digitally obtainable musical information in clinical improvisations is highly promising for enabling the efficient use of microanalytic methods in clinical practice. In particular, assessment procedures – the systematic collection and analysis of client information to plan subsequent therapy sessions – can benefit greatly from a microanalytic insight into imitation patterns or entrainment processes as observable in musical instrument digital interface (MIDI) data. However, the automation of microanalytic methods poses a challenge in formalising analytical arguments while at the same time maintaining qualitative validity in a machine learning environment. This article provides an interdisciplinary theoretical framework for the microanalysis of musical data in clinical improvisation that is suitable for computational implementation, leading to the development of an automated analysis tool for further use in research and clinical practice. While a pilot application of the system presented in the article suggests general functionality, future challenges for the training of a supervised classification model have been identified that focus on the need for formalisation of microanalytic arguments and feature development to ensure qualitative validity.
Cost-oriented sensor concept for magnetostrictive force measurement and its material requirements
(2025)
Ferromagnetic materials change their magnetic properties under load, enabling the implementation of a force sensor. The magnetic field emerging from such a sensor can be measured by secondary sensors to approximate the load acting on the sensor. A test setup simulating a potential application environment is described and its measurement results are presented. Furthermore, relevant magnetic material properties of an exemplarily chosen cold working steel are discussed.
The combination of human cognitive skills and dexterity with the endurance and repeatability of robots is a promising approach to modern assembly. However, efficiently allocating tasks and planning an assembly sequence between humans and robots is a manual, complex, and time-consuming activity. This work presents a framework named “Extract–Enrich–Assess–Plan–Review” that facilitates holistic planning of human–robot assembly processes. The framework automatically Extracts data from heterogeneous sources, Assesses the suitability of each assembly step to be performed by the human or robot, and Plans multiple assembly sequence plans (ASP) according to boundary conditions. Those sequences allow for a dynamic adaptation at runtime and incorporate different human–robot interaction modalities that are Synchronized, Cooperative, or Collaborative. An expert remains in the loop to Enrich the extracted data, and Review the results of the Assess and Plan steps with options to modify the process. To experimentally validate this framework, we compare the achieved degree of automation using three different CAD formats. We also demonstrate and analyze multiple assembly sequence plans that are generated by our system according to process time and the interaction modalities used.
Providing care for vulnerable population groups in the event of a disaster or evacuation is in the interests of those affected, of local authorities, health insurance companies, other insurance companies and the responsible authorities and organizations with security tasks (BOS). Evacuation and supply planning is currently mostly carried out using regionally or locally adapted so-called “isolated or individual solutions” or by means of an individual, time-consuming and usually manual and direct personal data query. Can existing medical health data from healthcare assessment tools provide valid information for the planning of care and care needs in disaster and civil protection?MethodsResearch and analysis of suitable and regularly used care assessment tools in the care sector in Germany.ResultsThe healthcare assessment tools cannot be simply combined. Without adaptation they cannot be used at all or only after concerted efforts to interpret the needs for the care of vulnerable groups in a non-clinical context.DiscussionAn improved use of individual medical data in disaster and civil protection offers many advantages in prevention, diagnostics, therapy and care in the context of disaster medicine, and not only from an ethical point of view. Due to the different tactical procedures and care strategies in disaster medicine, the field of acute care must be clearly separated from the area of evacuation and care of vulnerable groups. Currently, only the German Armed Forces (Bundeswehr) offer an internally secured infrastructure that allows all those involved in the care of soldiers to access all important medical data quickly and easily from any location.
Introduction: Tovertafel® is a VR-based serious game for dementia care (SGDC) that aims to stimulate residents affected by dementia in nursing homes, promote social and cognitive skills and reduce apathy. The aim of this study is to investigate the effects of using Tovertafel® on apathy, social interaction and social activity of people with dementia (PWD) in long-term inpatient care in Germany.
Methods: In this monocentric intervention study, 25 residents of an inpatient long-term care facility with moderate or severe dementia had two weekly applications of Tovertafel® over a period of 8 weeks. Effects on the residents’ social interaction and activity were recorded before (T1), during (T2) and 1 h after (T3) each intervention using the Engagement of a Person with Dementia Scale (EPWDS). The degree of apathy was assessed using the Apathy Evaluation Scale (AES). Effects of Tovertafel® were examined using a simple repeated measures analysis of variance (ANOVA).
Results: Thirteen residents with moderate (52%) and 12 residents with severe dementia (48%) were included. Results showed that residents’ apathy changed over the course of the trial and was partially reduced. ANOVA revealed significant changes in the positive expression of social participation in the overall group between individual observation times (p < 0.001; T1: MW = 2.67, SD = 1.352; T2: MW = 3.66, SD = 1.365; T3: MW = 3.10, SD = 1.300) and a significantly lower negative expression of social participation at T2 (MW = 1.09, SD = 0.358) than at T1 (MW = 1.19, SD = 0.579; p = 0.028). There was a significantly higher positive expression of behavioral involvement in the overall group at T3 (MW = 1.17, SD = 0.552) than at T1 (p = 0.003) or T2 (p = 0.045). Analyses did not find any significant interaction between observation times and degree of dementia.
Discussion: Results of the study show that the use of Tovertafel® over a period of 2 months had significant effects on apathy, social activity and social interaction in people with moderate or severe dementia. Symptoms of apathy could be reduced and social interaction and activity increased. However, due to limitations of the study design and special circumstances of the COVID-19 pandemic situation, findings might be overestimated and must be interpreted with care. Further research is necessary.
Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data.
The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.
Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the use of already available classification maps, which may not be the true ground truth and may contain noise from multiple possible sources. This is also true for the classification maps of the MapBiomas project, which provides land use and land cover (LULC) maps on a yearly basis, classifying the Amazon basin into more than 24 classes based on the Landsat data. In this study, we utilize the Sentinel-2 data with a higher spatial resolution in conjunction with the MapBiomas maps to evaluate a proposed noise removal method and to improve classification results. We introduce a novel noise detection method that relies on identifying anchor points in feature space through clustering with self-organizing maps (SOM). The pixel label is relabeled using nearest neighbor rules, or can be removed if it is unknown. A challenge in this approach is the quantification of noise in such a real-world dataset. To overcome this problem, highly reliable validation sets were manually created for quantitative performance assessment. The results demonstrate a significant increase in overall accuracy compared to MapBiomas labels, from 79.85% to 89.65%. Additionally, we trained the L2HNet using both MapBiomas labels and the filtered labels from our approach. The overall accuracy for this model reached 93.75% with the filtered labels, compared to the baseline of 74.31%. This highlights the significance of noise detection and filtering in remote sensing, and emphasizes the need for further research in this area.
In the context of climate change, vineyard monitoring to better understand spatiotemporal patterns of grapevine development is of utter importance for precision viticulture. We present a time series analysis of hyperspectral in situ and multispectral UAV data for different irrigation systems in Lower Franconia and correlate results with sensor data for soil moisture, temperature, and precipitation. Analysis of Variance (ANOVA) and a Tukey’s HSD test were performed to see whether Vegetation Indices (VIs) are significantly different with respect to irrigation systems as well as topographic position in the vineyard. Correlation between in situ measurements and UAV data for selected VIs is also investigated for upscaling analysis. We find significant differences with respect to irrigation, as well as for topographic position for most of the VIs investigated, highlighting the importance of adapted water management. Correlation between in situ and UAV data is significant only for some indices (NDVI and CIRedEdge, 𝑟2 of 0.33 and 0.49, respectively), while shallow soil moisture patterns correlate well with in situ-derived VIs such as the CIRedEdge and RG index (𝑟2 of 0.34 and 0.46).
The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.
Drawing on the work of Antonio Gramsci and liberation scholars from Latin America, this article examines the role of social work, as both an academic discipline and a practice, in the context of the recent refugee crisis. This role mirrors the historical development of social work and is further highlighted by recent empirical studies of refugee experiences on Greek islands. The authors call for an active political coalition between social work practitioners and refugees, with the aim of empowering the latter in their quest for liberation from the regime of European refugee management.
High-temperature calibration methods in additive manufacturing involve the use of advanced techniques to accurately measure and control the temperature of the build material during the additive manufacturing process. Infrared cameras, blackbody radiation sources and non-linear optimization algorithms are used to correlate the temperature of the material with its emitted thermal radiation. This is essential for ensuring the quality and repeatability of the final product. This paper presents the calibration procedure of an imaging system for in-situ measurement of absolute temperatures and temperature gradients during powder bed fusion of metal with laser beam (PBF-LB/M) in the temperature range of 500 K–1500 K. It describes the design of the optical setup to meet specific requirements in this application area as well as the procedure for accounting the various factors influencing the temperature measurement. These include camera-specific effects such as varying spectral sensitivities of the individual pixels of the sensor as well as influences of the exposure time and the exposed sensor area. Furthermore, influences caused by the complex optical path, such as inhomogeneous transmission properties of the galvanometer scanner as well as angle-dependent transmission properties of the f-theta lens were considered. A two-step fitting algorithm based on Planck's law of radiation was applied to best represent the correlation. With the presented procedure the calibrated thermography system provides the ability to measure absolute temperatures under real process conditions with high accuracy.
On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.
Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.
Introduction:
Entrepreneurs play a central role in economic and social stability, yet the start-up rate in Germany has declined in recent years, possibly due to the stress associated with entrepreneurial endeavors. Stressors such as financial uncertainty and time pressure are prevalent among entrepreneurs and negatively affect their psychological well-being. However, research on stress management strategies among self-employed individuals remains limited.
Methods:
This pilot study conducted a quantitative analysis with 117 self-employed participants in Germany. The study focused on typical entrepreneurial work demands and selected stress coping mechanisms.
Results:
The analysis revealed a significant correlation between quantitative demands and mental exhaustion. Furthermore, a high positive correlation between presenteeism and workload suggests that presenteeism may partially explain the variance in workload. These findings underscore how high job demands can lead to self-endangering behaviors that are detrimental to mental health.
Discussion:
Although no significant moderating effect of proactive coping on the relationship between job demands and mental exhaustion was observed, significant negative correlations between proactive coping and both job demands and mental exhaustion suggest a potential protective role of proactive coping against work-related stress. This study highlights the importance of understanding stress coping strategies among self-employed individuals and their impact on entrepreneurial success and mental well-being. Further research in this area is warranted to develop effective interventions to support the well-being and productivity of self-employed individuals in Germany.
In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.
