Long fiber thermoplastic (LFT) materials can be compounded in an effective LFT direct (LFT-D) process. Polycarbonate (PC) is used in various applications for example in and around battery enclosures for electro mobili...
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Laser powder bed fusion (PBF-LB/M) is an additive manufacturing (AM) process to produce high-strength, functionally integrated, lightweight metal components which cannot be built conventionally. This article addresses...
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The integration of personal devices in health surveillance has introduced significant patient data risks. In this research, we developed a Patients’ Personal Data Sovereignty System (PPDSS) to intelligently mask the ...
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The integration of personal devices in health surveillance has introduced significant patient data risks. In this research, we developed a Patients’ Personal Data Sovereignty System (PPDSS) to intelligently mask the patient's personal and sensitive data that should not be part of the data for analysis, ensuring these sensitive records do not find its way into machine learning models or to the Local Storage of the Capture Device. The approach presented in this paper is to ensure a high level of privacy and confidentiality for patients’ private health information (such as name, address, age and phone number) from the process of data collection, transmission, and storage to data analysis. The PPDSS is an android application built using new Flutter-based Cross Platform Technology which allows us to target other devices in future with same code base is designed to handle the data masking and elimination of Personal Identifiable Patients Data captured using the device camera before sending to the Machine Learning Models as texts. Records were captured from paper records using the camera on a smartphone installed with PPDSS. The image captured by PPDSS was obfuscated and then converted to text using AI-powered optical character recognition (OCR). The result is a personalized governance strategy of patient data, which ensures personal data privacy, confidentiality, and ethical use while maximizing the benefits of data-driven insights. The paper contributes to data governance by proposing a way of solving the problems at the point of data collection, rather than after the data have been collected.
Customization is an important competitive factor that enables companies to serve their customers better and succeed in an increasingly demanding and diverse market world. Instead of producing mass products for a broad...
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This work presents a heating technology for multi-die attach based on inductive silver sintering. Thus, the heat for the sintering reaction is confined locally to the bond region, avoiding heating of the entire power ...
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The polymorphism and the thermo-physical properties of alumina create significant challenges for deposition of thermally sprayed coatings. For a better understanding of the associated phenomena, in this study two feed...
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Increasing global competition among electric vehicle manufacturers has elevated the Noise, Vibration, and Harshness (NVH) properties of drivetrains to a key quality feature. Unfavorable NVH properties occur w...
Increasing global competition among electric vehicle manufacturers has elevated the Noise, Vibration, and Harshness (NVH) properties of drivetrains to a key quality feature. Unfavorable NVH properties occur when the drivetrain’s dynamic excitation forces excite modes of the sound-transmitting components at the respective eigenfrequencies. An established strategy to avoid this unwanted NVH behavior is topology optimization (TO), as it can shift unwanted modes of the drivetrain out of the excitation frequency range through geometrical adaptations using finite-element (FE) simulation. Another way to significantly influence unwanted NVH behavior in electric vehicle drivetrains are elastomer engine mounts which are employed to isolate the engine’s vibrations from the surrounding system and to dissipate vibrational energy. However, the effectiveness of these functionalities is limited in frequency ranges of local elastomer modes. Despite TO being well-established, challenges are given in shifting elastomer modes due to the complex material behavior. Firstly, the material’s softness results in an uncontrollably high density of local mount modes for TO, which presents a challenge in developing engine mounts without modes in the drivetrain’s excitation range, especially for electrified engines. Secondly, the elastomer’s nonlinear material impedes calculating the component’s modes with a linearized material approach. The goal of this contribution therefore is to develop a method that considers the complex material behavior of engine mounts in a TO process. To achieve this goal, the ideal transfer behavior of the engine mount, and consequently the positioning of mount modes within the frequency range, is determined based on the sound-transmitting structure and used as an objective for the optimization process. To consider the elastomer’s nonlinear transfer behavior during optimization, material parameters for each considered frequency are applied. The
Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growi...
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For both economic and research purposes, accurate information on forest composition, and the amount of dead wood is of paramount importance. It is not only important to know the biomass and the distribution of tree sp...
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ISBN:
(纸本)9781510664517
For both economic and research purposes, accurate information on forest composition, and the amount of dead wood is of paramount importance. It is not only important to know the biomass and the distribution of tree species but also to detect vermin and diseases to assess the health of a forest. Performing such inventory accurately with conventional methods of surveying (e.g., terrestrial laser scanners) is very labor and time-consuming as forests can be very dense, thus requiring many setups with a terrestrial laser scanner. We present an innovative approach for forest inventory combining UAV-borne light detection and ranging (LiDAR), multispectral aerial imagery, and terrestrial point cloud data measured with a handheld laser scanner. For such multi-sensor measurement campaigns, however, reliable extraction of forest parameters such as canopy height, the diameter at breast height (DBH), deadwood volumes, etc., strongly relies on the quality of sensor data fusion. Especially in dense forests, the GNSS signal, which is necessary for georeferencing the point clouds, can be very weak. However, terrestrial laser scans can capture much more information underneath the forest canopy which is partly obscured to the airborne data. To circumvent this mismatch, we propose an easily adaptable two-step workflow for fusing the directly georeferenced airborne LiDAR point clouds with the corresponding multi-spectral photogrammetric data and their unreferenced terrestrial counterparts. In the first step of the processing chain, individual terrestrial scans of the forest are coregistered using spherical laser targets located above exactly measured reference points. Secondly, these coarsely coregistered scans are then combined with the georeferenced airborne point clouds using control points. This is especially challenging as forests are very unstructured environments, and in addition, typical SLAM-features such as leaves, and branches tend to move. The result of this procedure are ra
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