Grundlegende betrachtungen zur wirkung eines "inversen" spanungsverhältnisses als basis für die fräswerk-zeugkonstruktion

The demand for higher productivity and quality, flexibility as well as process safety are marking the development in the field of metal-cutting manufacturing process. Thereby the field of low vibration milling plays a special role. Therefore the development and design of modern milling tools is more and more often affected by novel machining strategies. The article deals with the development and design of a low vibration milling tool including the reversal of conventional chip- cross- section b/h > 1 to the "invers" ratio b/h < 1. For this the difference between the two cross sections will be analysed. The focus of the first experimental research is the determination of the effects of reversing the chip- cross- section on the cutting forces as well as chip formation and - forming. The influence of the tool side rake angle (γf) in milling with "inverse" chip- cross- section will be studied. The results gathered in the field of "inverse" chip- cross- ratio provides the base for formulation of design fundamentals and drafts of a novel milling tool with peeling function

Applications of Spectrally-Resolved Photoluminescence in Silicon Photovoltaics

In broad terms, this thesis is devoted to measuring and interpreting the photoluminescence spectra emitted from different structures in crystalline silicon wafers and solar cells. Based on the knowledge accumulated, it also establishes a variety of applications of photoluminescence spectroscopy in silicon photovoltaics. The thesis may be divided into 3 main categories: band-to-band luminescence from wafers, deep-level luminescence from defects and impurities, and composite luminescence from different structures and layers in solar cells. First, this thesis utilizes band-to-band photoluminescence spectra emitted from planar silicon wafers to determine the values of the band-to-band absorption coefficient and the radiative recombination coefficient as a function of temperature with high precision. Parameterizations of these two coefficients are established to allow convenient calculations. Based on the newly established temperature data, the impacts of surface geometries and excess carrier profiles on luminescence spectra emitted from various silicon wafers are investigated via both modeling and experiments as a function of temperature. The results suggest that, the accuracy of many photoluminescence-based techniques, established mainly at room temperature in the literature, can be further improved by performing the measurements at higher temperatures due to the increasing impacts of surface reflectivities and excess carrier profiles on luminescence spectra with rising temperatures. These applications highlight the significance of the established data of the two coefficients for spectral fitting techniques. Next, the thesis investigates the deep-level luminescence from defects and impurities distributed around sub-grain boundaries in multicrystalline silicon wafers. The thesis shows that, the dislocations at sub-grain boundaries and the defects and impurities trapped around the dislocations emit separate luminescence peaks at low temperatures. The luminescence intensity of the trapped defects and impurities is found to be altered significantly after phosphorus gettering, whereas the dislocation luminescence is not changed throughout different solar cell processing steps. Also, the trapped defects and impurities are found to be preferentially distributed on one side of the sub-grain boundaries due to the asymmetric distribution of their luminescence intensity across the sub-grain boundaries. In addition, the thesis also demonstrates that the damage induced by laser doping is related to dislocations, since its deep-level luminescence spectrum has similar properties to those emitted from dislocations in multicrystalline silicon wafers. The interface between the laser-doped and un-doped regions is found to contain more damage than the laser-doped regions. Furthermore, the thesis reports a new photoluminescence-based method to separate the luminescence signatures from different layers and structures in a single silicon substrate, courtesy of the well-resolved luminescence peaks at low temperatures from different layers. In particular, the technique is applied to characterize the doping level of both locally-diffused and laser-doped regions on various silicon solar cells and cell precursors, utilizing band-gap narrowing effects in heavily-doped silicon. The results show that, the interface between the laser-doped and un-doped regions is much more heavily-doped that the doped regions. In addition, the technique is also applied to evaluate and the parasitic absorption of different surface passivation films on finished solar cells, due to the correlation between the sub band-gap luminescence intensity from these passivation films and the optical absorption in the films. The technique is contactless and nondestructive, requires minimal sample preparation, and provides micron-scale spatial resolutions. Finally, the thesis combines the advantages of spectrally-resolved photoluminescence (PLS) and photoluminescence excitation spectroscopy (PLE) to develop a PLS-PLE-combined technique for characterizing wafers and solar cells. In particular, the entire photoluminescence spectrum from a silicon wafer or solar cell is captured and monitored while the excitation energy is varied. This technique allows us to quantitatively evaluate both the doping level and the junction depth of various diffused silicon wafers, the defects induced by the post-diffusion thermal treatment at different depths below the wafer surface, and the enhanced diffusion at grain boundaries and sub-grain boundaries in multicrystalline silicon wafers. The results show that, the enhanced diffusion happens at both grain boundaries and sub-grain boundaries

ViLLM-Eval: A Comprehensive Evaluation Suite for Vietnamese Large Language Models

The rapid advancement of large language models (LLMs) necessitates the development of new benchmarks to accurately assess their capabilities. To address this need for Vietnamese, this work aims to introduce ViLLM-Eval, the comprehensive evaluation suite designed to measure the advanced knowledge and reasoning abilities of foundation models within a Vietnamese context. ViLLM-Eval consists of multiple-choice questions and predict next word tasks spanning various difficulty levels and diverse disciplines, ranging from humanities to science and engineering. A thorough evaluation of the most advanced LLMs on ViLLM-Eval revealed that even the best performing models have significant room for improvement in understanding and responding to Vietnamese language tasks. ViLLM-Eval is believed to be instrumental in identifying key strengths and weaknesses of foundation models, ultimately promoting their development and enhancing their performance for Vietnamese users. This paper provides a thorough overview of ViLLM-Eval as part of the Vietnamese Large Language Model shared task, held within the 10th International Workshop on Vietnamese Language and Speech Processing (VLSP 2023).Comment: arXiv admin note: text overlap with arXiv:2305.08322 by other author

VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification

The increasing reliance of software projects on third-party libraries has raised concerns about the security of these libraries due to hidden vulnerabilities. Managing these vulnerabilities is challenging due to the time gap between fixes and public disclosures. Moreover, a significant portion of open-source projects silently fix vulnerabilities without disclosure, impacting vulnerability management. Existing tools like OWASP heavily rely on public disclosures, hindering their effectiveness in detecting unknown vulnerabilities. To tackle this problem, automated identification of vulnerability-fixing commits has emerged. However, identifying silent vulnerability fixes remains challenging. This paper presents VFFINDER, a novel graph-based approach for automated silent vulnerability fix identification. VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and represents them in annotated ASTs. VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features. We conducted experiments to evaluate VFFINDER on a dataset of 36K+ fixing and non-fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 47% with the same review effort of 5% compared to the existing approaches.Comment: Accepted by IEEE KSE 202

Le portail g-INFO pour surveiller la grippe Influenza A

Le portail g-INFO pour surveiller la grippe Influenza

Deep learning for image classification of submersible pump impeller

This study presented a deep learning-based model in the submersible pump impellers quality inspection process. The proposed method aimed to relieve worker workload, automate the system, as well as increase the accuracy in defect detection and classification. The proposed approach aims to be implemented on systems with low investment cost and limited resources, i.e., small single-board computers, enabling flexible deployment in industrial environments. The model consisted of three convolutional neural network (CNN) models, i.e., visual geometry group 16 (VGG16), ResNet50, and a custom model. The outputs of three networks were either synthesized later through an ensemble stage or used separately. A graphical user interface (GUI) was also developed for real-time inspection and user-friendly interaction. The approach achieved up to 99.8% accuracy in identifying defects, including surface scratches, corrosion, and geometric irregularities. The proposed method improved the quality assurance process by reducing manual inspection efforts. Future research could explore advanced techniques like anomaly detection to further enhance system performance and versatility

g-INFO portal: a solution to monitor Influenza A on the Grid for non-grid users

International audienceIn this paper, we introduce a portal for monitoring Influenza A on a grid-based system. Influenza A keeps on being a major threat to public health worldwide; especially if one virus can mutate itself so that it acquires the capacity for human to human transmission of H1N1 as well as the high death rate of H5N1. The existing g-INFO (Grid-based Information Network for Flu Observation) project provides a complete system for monitoring flu virus on the Grid. We present here a portal that operates on top of the g-INFO system as a solution for non-grid users to utilize grid services for analyzing molecular biology data of Influenza A

Active disturbance rejection control-based anti-coupling method for conical magnetic bearings

Conical-shape magnetic bearings are currently a potential candidate for various magnetic force-supported applications due to their unique geometric nature reducing the number of required active magnets. However, the bearing structure places control-engineering related problems in view of underactuated and coupling phenomena. The paper proposes an Adaptive Disturbance Rejection Control (ADRC) for solving the above-mentioned problem in the conical magnetic bearing. At first, virtual current controls are identified to decouple the electrical sub-system, then the active disturbance rejection control is employed to eliminate coupling effects owing to rotational motions. Comprehensive simulations are provided to illustrate the control ability

RiGaD: An aerial dataset of rice seedlings for assessing germination rates and density

The authors acknowledge that this work was supported by the Brussels Institute of Advanced Studies (Grant: BrIAS2024) and by a scientific stay grant from the FWO (Grant number: V501724N) .This study is also funded by the Ministry of Education and Trainning Project with the code number B2023.TCT.08