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How does the digitalization of the SCOR model contribute to the efficiency of supply chain operations?

Logistique et Management 2024年第4期32卷 194-214页

作者： Hrouga, Mustapha Said, Dhaou Department of Finance Operations and Marketing Brest Business School Brest France School of Electrical Engineering and Computer Science University of Ottawa Canada

Résumé: Les outils digitaux tels que la blockchain, l’Internet des objets (IoT) et le big data (BD) ont été largement utilisés pour digitaliser la logistique et la gestion de la chaîne d’approvisionnement (CA). En fait, la technologie blockchain peut offrir divers avantages tels que la confiance, la traçabilité et la transparence qui permettent d’améliorer les performances de la chaîne d’approvisionnement. En plus, l’IoT est considéré comme la technologie la plus encourageante pour suivre et partager des informations en temps réel entre tous les acteurs de la CA. Enfin, le BD est considéré comme un outil très puissant et efficace pour optimiser la CA et permet à plusieurs entreprises de contrôler et de traiter une énorme quantité de données. C’est pour ces raisons que dans cet article, nous proposons d’utiliser ces technologies numériques pour développer un modèle SCOR digital pour une CA efficace, intelligente et performante. Afin de mettre en lumière les performances de cette étude, un modèle SCOR est initialement présenté pour mettre en évidence les avantages et les impacts de ces trois technologies sur les principales activités de la CA, notamment plan, source, make, deliver et return. Ensuite, les trois technologies sont combinées où le cloud est utilisé comme PaaS (platform as service) afin de les connecter. Enfin, pour valider le modèle SCOR 4.0 proposé, nous avons présenté un exemple didactique avec une CA multi-échelons et demande déterministe. Les résultats ont montré que le modèle proposé a permis d’améliorer la visibilité de la CA et d’augmenter le niveau de satisfaction des clients. © 2024 KEDGE Business School.

关键词： Supply chain 4.0 Digitalisation Modèle SCOR Blockchain internet des objets Big data Supply chain 4.0 digitalization SCOR model blockchain Internet of Things big data

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Investigation of Colors Categorized into Color Names on a Liquid Crystal Display to Enhance CAPTCHAs

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International Journal of the Society of Material engineering for Resources 2022年第2期25卷 212-217页

作者： Ishizawa, Chikako Kobayashi, Nozomu Kageyama, Yoichi Department of Mathematical Science and Electrical-Electronic-Computer Engineering Graduate School of Engineering Science Akita University Akita010-8502 Japan

This study focuses on the human ability to tolerate subtle diff erences in color and categorize similar colors into a single mass to enhance completely automated public turing test to tell computers and humans apart (CAPTCHA). First, an experiment was conducted to check whether the color selection condition could control the color categorization results. Consequently, it was clarifi ed that when the selection condition was added, the color to be categorized changed, implying the possibility of controlling the categorized result. Subsequently, an investigation was conducted to clarify the range of liquid crystal display colors categorized as major color names. As a result, it was clarified that the hue angle size of the colors categorized by more than half of the participants was sometimes greater than the hue angle size between the primary and complementary colors. It was also clarifi ed that among the categorized colors, there were categorized colors in the two color names. Finally, results from the fi rst and second experiments were collated. It was confi rmed that by using the categorized color range and selection conditions, it is possible to obtain an answer with a color name that is diff erent from the original color name. These results suggest that the ability of humans to categorize similar colors into a single mass enhances CAPTCHA. © 2022 Society of Materials engineering for Resources of Japan. All rights reserved.

关键词： Color

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End-to-End Secure Video Streaming Using Homomorphic Encryption Techniques 2

End-to-End Secure Video Streaming Using Homomorphic Encrypti...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798331529529

In the rapidly evolving landscape of digital media, video streaming has become a ubiquitous method of content delivery. However, the increasing sophistication of cyber threats poses significant risks to the security and privacy of streamed content. Traditional encryption methods, while effective in many scenarios, often fall short in providing comprehensive protection without compromising performance and user experience. This paper introduces a novel approach to achieving end-to-end secure video streaming by leveraging homomorphic encryption techniques. Homomorphic encryption allows for computations to be performed on encrypted data without needing to decrypt it first, thereby maintaining data privacy and security throughout the streaming process. Our proposed method integrates homomorphic encryption into the video streaming pipeline, ensuring that video data remains secure from the point of origin to the end user. We discuss the theoretical foundations of homomorphic encryption, detail the implementation of our approach, and evaluate its performance against conventional encryption methods. Experimental results demonstrate that our technique provides robust security with minimal impact on streaming quality and latency, making it a viable solution for secure video streaming in various applications. © 2024 IEEE.

关键词： Video streaming

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Towards Enhancing Child Safety: A Deep Learning Approach to Detect Child Safe and Unsafe Objects

Towards Enhancing Child Safety: A Deep Learning Approach to ...

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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Khan, Fairuz Areefin Dey, Ashim Chittagong University of Engineering and Technology Department of Electrical and Electronic Engineering Chittagong4349 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331535476

The safety and well-being of children are a paramount concern, and the accurate detection of potentially haz-ardous objects is crucial in ensuring their protection, especially in indoor situations. In the Artificial Intelligence (AI) age, the future belongs to domestic robots;they will be used for child education, surveillance, and healthcare support. Early identification of unsafe objects and intervention by these next-generation social family robots can play an essential role in providing a safer environment for children. Keeping these issues in mind, this study aims to tackle daily risks and accidents that endanger children by using computer vision to detect unsafe objects. To achieve this goal, a unique dataset namely ChildSUn is introduced containing approximately 5350 images of child-safe and unsafe objects of ten categories. Various CNN architectures, including custom CNN, VGG16, VGG19, ResNet50, Inception-V3, EfficientNet-B0, and MobileNetV2, are implemented on the prepared dataset. A deep dive analysis is performed on the implemented models. The EfficientNet-B0 model showed exceptional performance, reaching a remarkable level of accuracy of 99.63% on the test dataset. These findings underscore the potential of EfficientNet-B0 as an effective solution for real-world child safety applications and encourage further exploration of deep learning techniques to enhance child protection globally. We hope this work will pave the way for future social household robot development, which will ensure child safety in any place. © 2024 IEEE.

关键词： Deep learning

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Certification with an NP Oracle 14

Certification with an NP Oracle

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14th Innovations in Theoretical computer science Conference, ITCS 2023

作者： Blanc, Guy Koch, Caleb Lange, Jane Strassle, Carmen Tan, Li-Yang Department of Computer Science Stanford University CA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States

ISBN: (纸本)9783959772631

In the certification problem, the algorithm is given a function f with certificate complexity k and an input x*, and the goal is to find a certificate of size ≤ poly(k) for f's value at x*. This problem is in NPNP, and assuming P ≠ NP, is not in P. Prior works, dating back to Valiant in 1984, have therefore sought to design efficient algorithms by imposing assumptions on f such as monotonicity. Our first result is a BPPNP algorithm for the general problem. The key ingredient is a new notion of the balanced influence of variables, a natural variant of influence that corrects for the bias of the function. Balanced influences can be accurately estimated via uniform generation, and classic BPPNP algorithms are known for the latter task. We then consider certification with stricter instance-wise guarantees: for each x*, find a certificate whose size scales with that of the smallest certificate for x*. In sharp contrast with our first result, we show that this problem is NPNP-hard even to approximate. We obtain an optimal inapproximability ratio, adding to a small handful of problems in the higher levels of the polynomial hierarchy for which optimal inapproximability is known. Our proof involves the novel use of bit-fixing dispersers for gap amplification. © Guy Blanc, Caleb Koch, Jane Lange, Carmen Strassle, and Li-Yang Tan;licensed under Creative Commons License CC-BY 4.0.

关键词： Polynomial approximation

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A High-Voltage DC-DC LLC Resonant Converter by Using a Symmetrical Voltage Multiplier Circuit 3

A High-Voltage DC-DC LLC Resonant Converter by Using a Symme...

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3rd International Conference on electrical Machines and Drives, ICEMD 2023

作者： Takarli, Reza Adib, Mohammadreza Vahedi, Abolfazl Beiranvand, Reza Iran University of Science and Technology Department of Electrical Engineering Tehran Iran Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350393743

This paper introduces a high-voltage output DC-DC LLC resonant converter designed for various applications. Utilizing soft switching techniques, the converter minimizes electromagnetic interference, enhancing efficiency and operational frequency. Notably, it regulates output voltage by modulating switching frequency to accommodate load changes and input voltage fluctuations while ensuring soft switching. The core design employs a six-switch inverter, reducing voltage stresses and enhancing overall performance. Additionally, a symmetrical multiplier in the output stage quadruples the resonant tank voltage, improving power density without significant changes in output impedance or transformer turns ratio. The study includes detailed operational principles, mathematical analysis, and validation through simulation, substantiated by a prototype implementation. The performance of the proposed resonant converter is verified using a prototype that has 40 V input voltage and 320 V output voltage. © 2023 IEEE.

关键词： DC-DC converters

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Machine-type communications in NOMA-based terahertz wireless networks

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International Journal of Intelligent Networks 2022年 3卷 31-47页

作者： Sabuj, Saifur Rahman Rubaiat, Maisha Iqbal, Mehzabien Mobashera, Monica Malik, Afrida Ahmed, Imtiaz Matin, Mohammad Abdul Department of Electronic Engineering Hanbat National University Korea Republic of Department of Electrical and Electronic Engineering Brac University Bangladesh Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States Department of Electrical and Computer Engineering University of Denver DenverCO United States

Terahertz (THz) band is one of the most promising aspects of wireless communication systems because of its potential to meet the growing demands for the envisioned next-generation of cellular communications. THz band connectivity can alleviate bandwidth shortages and transmit power constraints using multiple-input multiple-output (MIMO) systems. To obtain better throughput and enhanced spectral efficiency, non-orthogonal multiple access (NOMA) configuration can be incorporated into MIMO systems, as NOMA uses non-orthogonal resource allocation by assigning the same carrier frequency to multiple devices in the power domain. In this paper, we focus on 2 × 1 MIMO-NOMA and cooperative 2 × 1 MIMO-NOMA systems for THz spectrum in the downlink transmission. In order to evaluate the effectiveness of the proposed system architecture, we derive accurate data rate, transmission latency, and reliability expressions for 2 × 1 MIMO-NOMA system consists of critical and non-critical devices by leveraging finite block length theory. Moreover, we derive the closed-form expressions for power splitting coefficients, data rate, transmission latency, and reliability for the considered cooperative 2 × 1 MIMO-NOMA system. Extensive numerical results are presented to validate the feasibility of the proposed 2 × 1 MIMO-NOMA as well as the cooperative 2 × 1 MIMO-NOMA systems in the THz band. © 2022 The Authors

关键词： Energy harvesting

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Segmentation of Agricultural Fields in Aerial Imagery Using Enhanced Deep Learning Models 19

Segmentation of Agricultural Fields in Aerial Imagery Using ...

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19th International Conference on Emerging Technologies, ICET 2024

作者： Aslam, Sumera Bakr, Muhammad Abu Khan, Sultan Daud National University of Technology Department of Electrical Engineering Islamabad Pakistan National University of Technology Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350379587

Precision agriculture utilizes advanced technologies to enhance crop management and optimize agricultural yield. A crucial component involves segmenting agriculture fields in aerial imagery, which is critical for accurate identification of crops, weeds, and other agricultural traits. However, aerial image segmentation is challenging due to diverse agricultural patterns and visually similar elements, which results in pixel misclassification. This study focuses on segmenting agriculture fields from aerial imagery using UNet with ResNet block, UNet with DenseNet-121 block, and DeepLabV3-Plus models. Despite their robust frameworks, existing models struggle to distinguish between comparable classes due to differences in plant appearance, lighting conditions, and soil backgrounds. The results show that the UNet-ResNet and UNet-DenseNet models provide more accurate and visually consistent segmentations than DeepLabV3-Plus. UNet-ResNet outperformed the other models in terms of accuracy, with 88.16 percent and the lowest loss of 0.48. This study presents insights and recommendations to enhance model performance for accurate agriculture patterns segmentation. Future research should focus on creating models capable of handling nuanced variances in agricultural datasets to promote efficient and sustainable farming techniques. © 2024 IEEE.

关键词： Semantic Segmentation

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A Comparative of Classification Models for Predicting of Heart Diseases

A Comparative of Classification Models for Predicting of Hea...

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2023 International Conference on science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Arowolo, Micheal Olaolu Adesina, Ruth Omorinsola Adebiyi, Marion Olubunmi Adebiyi, Ayodele Ariyo University of Missouri Department of Electrical Engineering and Computer Science Columbia United States Landmark University Department of Computer Science Omu-Aran Nigeria

ISBN: (纸本)9798350324785

To slow the rising mortality toll, early detection of heart illness is essential, as it is one crucial organ in human;if it stops beating, life ends. To put it simply, cardiovascular diseases (CVDs) are the important global killer. In 2019, CVDs were responsible for the deaths of 17.9 million individuals worldwide. The medical field has benefited greatly from technological advancements, but there are still some challenges associated with machine learning, such as data that is noisy, error-prone, or contains outliers. Due to their often-late diagnosis, these diseases are principal reason of bereavement in heart patients, making it imperative to learn more about effective algorithms for disease prognosis. Accurate predictions of such results with Machine Learning algorithms can support in the initial diagnosis of diseases, mitigation of their effects, and even prevention of their occurrence by providing crucial information to both medical professionals and patients. This study proposes a model that can rapidly and cheaply diagnose cardiac problems. Therefore, this research employs SVM, Naive Bayes, and Random Forest to detect cardiac illnesses using a data set from Kaggle, with 93%, 85%, and 95% accuracy, respectively;however, the Random Forest attained the best accuracy of 96.9%. © 2023 IEEE.

关键词： Forecasting

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Drawing Order Diagrams Through Two-Dimension Extension

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Journal of Graph Algorithms and Applications 2023年第9期27卷 783-802页

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge & Data Engineering Group Research Center for Information System Design & Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order diagrams provide an important tool for understanding and thus discovering knowledge in such data. Easily readable drawings of such order diagrams are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work, we present the novel algorithm DimDraw to decompose an ordered set (e.g., a concept hierarchy) in linear orders and to produce a corresponding order diagram. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of its transitive incompatibility graph. To evaluate the quality of the algorithm, a user study was conducted where generated drawings were compared with ones from state-of-the-art drawing algorithms. © 2023 Brown University. All rights reserved.

关键词： Set theory

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