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2022 International Research Council on the Biomechanics of Injury, IRCOBI 2022

作者： Jenerowicz, Marcin Haase, Thomas Linnenberg, Markus Hoschke, Klaus Boljen, Matthias Hiermaier, Stefan Germany The Faculty of Engineering The Albert-Ludwigs-University Freiburg Germany Sustainable Systems Engineering The Faculty of Engineering The Albert-Ludwigs-University Germany The Fraunhofer Institute for High-Speed Dynamics EMI Germany

The objective of this study is to evaluate different rib bone surrogates by comparing them to specific, male PMHS data from Charpail et al. (2005), Kindig (2009), Agnew et al. (2018) and Kang et al. (2020). The fraunhofer EMI 5th male rib surrogates were fabricated from Scalmalloy® (AlMg4.5Sc0.7Zr0.3) using additive manufacturing. The CTS PRIMUS breakable dummy from Crashtest-Service-Dummy-Solution GmbH is already being used as an anthropomorphic test device (ATD) in various areas. Its cast 5th left rib, constructed from a mixture of epoxy resin and aluminium powder, was also included in this study. A test setting based on the setup of Charpail et al. (2005) was developed. The specimens were dynamically (500 mm/s) loaded to failure in a bending scenario imitating a frontal thoracic impact. During the tests, not only force and displacement were measured, but also the strain distribution using 3D digital image correlation (3D-DIC) and compared to PMHS data of the previously mentioned studies. Both rib surrogates show strong deviations from the PMHS characteristic values. Nevertheless, there are also common characteristics in key variables to certain age groups of the PMHS data. The differences shown are an important contribution to the further development and improvement of both surrogates. © 2022 International Research Council on the Biomechanics of Injury. All rights reserved.

关键词： 3D printers

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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Computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production systems 28

Maturity Evaluation of Domain-Specific Language Ecosystems f...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Greiner, Sandra Wiesmayr, Bianca Feichtinger, Kevin Meixner, Kristof Konersmann, Marco Pfeiffer, Jérôme Oberlehner, Michael Schmalzing, David Wortmann, Andreas Rumpe, Bernhard Rabiser, Rick Zoitl, Alois University of Bern Software Engineering Group Switzerland Technische Universität Wien CDL SQI Institute of Information Systems Engineering Austria RWTH Aachen University Software Engineering Germany Germany Johannes Kepler University Linz LIT CPS Lab Austria Johannes Kepler University Linz CDL VaSiCS LIT CPS Lab Austria

ISBN: (纸本)9798350339918

engineering Cyber-Physical Production systems (CPPSs) heavily relies on Domain-Specific Languages (DSLs), which are tailored to a specific class of problems inherent to CPPSs. DSLs enable non-programming experts to solve problems in their domain, such as modeling production processes, implementing control software, or managing the variability of production systems. A DSL ecosystem encompasses the entire infrastructure (e.g., libraries and tools) built around its language and contributes to the successful and easy adoption thereof by domain experts. We present a maturity evaluation model for DSL ecosystems serving two aims: to developers, it reveals missing but essential aspects of the ecosystem, and users (e.g., industrial companies) can evaluate the maturity of a DSL ecosystem. We propose criteria to evaluate the maturity of DSL ecosystems and apply them to existing, publicly available DSLs which have been adopted in CPPSs. The results demonstrate that all components of the model are covered and they allow for deriving hypotheses about DSL ecosystems used in CPPSs. © 2023 IEEE.

关键词： Ecosystems

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Power in Computer Security and Privacy: A Critical Lens

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IEEE Security and Privacy 2023年第2期21卷 48-52页

作者： Redmiles, Elissa M. Bennett, Mia M. Kohno, Tadayoshi Max Planck Institute for Software Systems Saarland66123 Germany University of Washington SeattleWA98195 United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA98195 United States

Critical theory is an approach to research focused on acknowledging and dismantling power structures. In this piece, we illustrate the ways in which security and privacy research already takes a critical approach and ... 详细信息

关键词： Computer networks

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Success Factors of Requirement Elicitation in the Field of software engineering 4

Success Factors of Requirement Elicitation in the Field of S...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Attanayaka, Buddhima Nawinna, Dasuni Manathunga, Kalpani Abeygunawardhana, Pradeep K.W. Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors. © 2022 IEEE.

关键词： Requirements engineering

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Supporting software Engineers in IT Security and Privacy through Automated Knowledge Discovery 25

Supporting Software Engineers in IT Security and Privacy thr...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Ehl, Marco Ahmadian, Amir Shayan Großer, Katharina Elsofi, Duaa Adel Ali Herrmann, Marc Specht, Alexander Schneider, Kurt Jürjens, Jan Institute for Software Technology University of Koblenz Koblenz Germany Software Engineering Group Leibniz University Hannover Hannover Germany Fraunhofer ISST Dortmund Germany

ISBN: (纸本)9798400706295

Security and privacy are increasingly essential concepts in software engineering. New threats and corresponding countermeasures are continuously discovered. Concurrently, projects are becoming more complex and are exposed to a greater number of threats. This presents a significant challenge for software engineers. As a result, security and privacy are often neglected due to a lack of knowledge, limited time, and financial constraints. While systematic literature reviews exist to address the increasing volume of publications, software engineers still require up-to-date knowledge of current threats and measures. This paper presents an automated, time-efficient, and cost-effective method for discovering knowledge from state-of-the-art literature and project artifacts, such as design documents. The presented method utilizes Large Language Models (LLMs) for data extraction and is demonstrated through a prototypical implementation and evaluation. This evaluation involves security and privacy in open-access scientific publications and project documentation from European Union research and development projects. The extracted knowledge is used to populate a quality model that is specifically designed to provide software engineers with information that helps them apply the findings. This quality model offers software engineers valuable, up-to-date insights into security and privacy, bridging the gap between scientific research and practical applications. Copyright © 2025 held by the owner/author(s).

关键词： software quality

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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Towards Self-Configuring Plug & Produce Robot systems Based on Ontologies

Towards Self-Configuring Plug & Produce Robot Systems Based ...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Christian Eymüller Julian Hanke Alexander Poeppel Wolfgang Reif Institute for Software and Systems Engineering University of Augsburg Augsburg Germany

The industry is undergoing a disruptive shift from mass production to individually manufactured one-offs. To achieve this transformation, the production facilities must also be flexible and able to configure themselves according to requirements. Often it is not enough to reconfigure the software, but the hardware must also be adapted accordingly. Such customizable hardware and software systems are called Plug & Produce systems. For such Plug & Produce robot systems, we have developed an automatic configuration generation for hardware configurations based on capability/skill descriptions of individual hardware components. The configuration creation is done with the help of an ontology, in which information about modular hardware components, their capabilities, and the interrelationships of their interfaces (geometric, electrical, and data) is stored.

关键词： Industries Mass production Automation Ontologies software systems Hardware Production facilities

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Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrates

Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrate...

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2024 IEEE Silicon Photonics Conference, SiPhotonics 2024

作者： Wan, Zeyu Yang, Yun-Cheng Chen, Wei-Hsin Chiu, Chih-Chuan Zhao, Yunlong Feifel, Markus Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui The University of British Columbia Department Of Materials Engineering Vancouver Canada Fraunhofer Institute For Solar Energy Systems Heidenhofstrasse Freiburg Germany The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada National Taiwan University Graduate Institute Of Electronics Engineering Taipei Taiwan National Taiwan University Graduate Institute Of Photonics And Optoelectronics Taipei Taiwan National Taiwan University Graduate School Of Advanced Technology Taipei Taiwan

ISBN: (纸本)9798350394047

This work successfully developed an independent Ge-VCSEL epitaxy and fabrication technology route. This is the second successful Ge-VCSEL technology reported worldwide, and the first Ge-VCSEL technology with key details disclosed, including Ge substrate specification, transition layer structure and composition, and fabrication process etc. Compared with the GaAs counterparts, after epitaxy optimization, the Ge-VCSEL wafer has a 40% lower surface root-mean square roughness and 72% lower average bow-warp. Additionally, the Ge-VCSEL has a 10% lower threshold current density and a 19% higher maximum slope efficiency than the GaAs-VCSEL. © 2024 IEEE.

关键词： Surface emitting lasers

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