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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

共 36972 条记录，以下是401-410 订阅

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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Preliminary Conception and Relevant Suggestions About knowledge Construction on Defense Science and Technology Information Under Big data Environment 1

Preliminary Conception and Relevant Suggestions About Knowle...

引用

1st international conference on Intelligent manufacturing and Cloud Computing, ICIMCC 2024

作者： Geng, Weibo Wei, Zhaolei Zhao, Yingxiao You, Hongliang Xu, Ping Guo, Quankui Cai, Jiaoyang Military Science Information Research Center Academy of Military Sciences Beijing100142 China PLA Army Chemical Defense College Beijing102200 China

ISBN: (纸本)9781643685779

With the development of big data technology, the theoretical methods of knowledge construction on defense technology information field urgently needs to be optimized and improved. This paper analyzes in detail the situations and issues confronting knowledge construction about defense science and technology information, which based on the basic characteristics of the big data environment and the main challenges of Defense Science and Technology Information (DS&TI) research. It systematically sorts out the capability requirements and data categories of DS&TI research in the era of big data. The paper designs the overall framework for knowledge construction on DS&TI, which includes "data foundation layer - generation method layer - knowledge classification layer - capability requirement layer" and proposes strategies and relevant suggestions about knowledge construction on DS&TI field. This article's research provides macro guidance and reference for knowledge construction on DS&TI and provides support for DS&TI research. © 2025 The Authors.

关键词： data Science

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Trustworthy Collaborative Business Intelligence Using Zero-knowledge Proofs and Blockchains 36th

Trustworthy Collaborative Business Intelligence Using Zero-K...

引用

36th international conference on Advanced Information systems engineering (CAiSE)

作者： Quattrocchi, Giovanni Plebani, Pierluigi Politecn Milan Informaz Dipartimento Elettron Bioingn Milan Italy

ISBN: (纸本)9783031609992;9783031610004

In the era of data-driven decision-making, the ability to securely and reliably exchange analytical data among organizations (collaborative business intelligence) is becoming increasingly important. This paper envisions a novel framework for trustworthy data exchange, leveraging Zero-knowledge Proofs (ZK-Proofs) to maintain data privacy and integrity, and the blockchain for reliable auditing. Our framework emphasizes enhancing business intelligence capabilities through non-operational analytics, particularly in the generation of aggregated insights for strategic decision-making among different organizations, without exposing the underlying raw data, thus preserving data sovereignty. We introduce a methodology to perform operations on data cubes using ZK-Proofs, allowing for the generation of more aggregated data cubes from initial raw data hypercubes. The framework exploits the data-Fact Model to identify the available transformation paths on raw data.

关键词： collaborative business intelligence zero-knowledge proofs data exchange data warehouse data sharing

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Federated Learning for XSS Detection: A Privacy-Preserving Approach 16

Federated Learning for XSS Detection: A Privacy-Preserving A...

引用

16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Jazi, Mahran Ben-Gal, Irad Department of Industrial Engineering Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9789897587160

Collaboration between edge devices has increased the scale of machine learning (ML), which can be attributed to increased access to large volumes of data. Nevertheless, traditional ML models face significant hurdles in securing sensitive information due to rising concerns about data privacy. As a result, federated learning (FL) has emerged as another way to enable devices to learn from each other without exposing user’s data. This paper suggests that FL can be used as a validation mechanism for finding and blocking malicious attacks such as cross-site scripting (XSS). Our contribution lies in demonstrating the practical effectiveness of this approach on a real-world dataset, the details of which are expounded upon herein. Moreover, we conduct comparative performance analysis, pitting our FL approach against traditional centralized parametric ML methods, such as logistic regression (LR), deep neural networks (DNNs), support vector machines (SVMs), and k-nearest neighbors (KNN), thus shedding light on its potential advantages. The dataset employed in our experiments mirrors real-world conditions, facilitating a meaningful assessment of the viability of our approach. Our empirical evaluations reveal that the FL approach not only achieves performance on par with that of centralized ML models but also provides a crucial advantage in terms of preserving the privacy of sensitive data. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Federated learning

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Contract-based Validation of Conceptual Design Bugs for engineering Complex Machine Learning Software 24

Contract-based Validation of Conceptual Design Bugs for Engi...

引用

ACM/IEEE 27th international conference on Model Driven engineering Languages and systems: Companion Proceedings (MODELS)

作者： Meijer, Willem Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798400706226

Context. Modern software systems increasingly commonly contain one or multiple machine learning (ML) components. Current development practices are generally on a trial-and-error basis, posing a significant risk of introducing bugs. One type of bug is the "conceptual design bug," referring to a misunderstanding between the properties of input data and prerequisites imposed by ML algorithms (e.g., using unscaled data in a scale-sensitive algorithm). These bugs are challenging to test at design time, causing problems at runtime through crashes, noticeably poor model performance, or not at all, threatening the system's robustness and transparency. Objective. In this work, I propose the line of research I intend to pursue during my PhD, addressing conceptual design bugs in complex ML software from a prevention-oriented perspective. I intend to build open-source tooling for ML engineers that can be used to detect conceptual design bugs, enabling them to make quality assurances about their system design's robustness. Approach. We need to understand conceptual bugs beyond the status quo, identifying their types, prevalence, impacts, and structural elements in the code. We operationalize this knowledge into a tool that detects them at design time, allowing ML engineers to resolve them before running their code and wasting resources. We anticipate this tool will leverage contract-based validation applied to partial ML software models. Evaluation. We plan to evaluate the built tool two-fold using professional (industrial) ML software. First, we will study its effectiveness regarding bug detection at design time, identifying whether it fulfills its functional objective. Second, we will study its usability, identifying whether ML engineers benefit when tools like this are introduced into their ML engineering workflow.

关键词： Machine Learning Software Bugs Software Design knowledge Mining Software Contracts Empirical Software engineering

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Enhancing IoT data Security with Blockchain Based Inner Product Encryption Algorithm

Enhancing IoT Data Security with Blockchain Based Inner Prod...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Poongodi, K. Gopica, C. Amrin, A. Sanjai, K. Dhaya Praveen, R. K. S Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Icu Medical India Llp Research & Development Chennai600006 India

ISBN: (纸本)9798350359688

This paper introduces a pioneering approach to enhancing access control in IoT systems by combining blockchain technology and inner product encryption. By leveraging the immutability and transparency of blockchain and the cryptographic strength of inner product encryption, the proposed framework addresses key challenges such as scalability, interoperability, and trustworthiness. It decentralizes authentication, ensuring robustness and eliminating single points of failure, while also safeguarding data privacy through advanced cryptographic techniques. The framework demonstrates superior security, efficiency, and resilience compared to conventional methods, as validated through extensive simulations and evaluations across various IoT deployment scenarios. Additionally, the solution offers scalability and interoperability, facilitating seamless integration with diverse IoT architectures and protocols. The paper concludes by discussing potential deployment considerations and outlining future research directions to further advance blockchain-driven access control with inner product encryption for IoT applications. © 2024 IEEE.

关键词： Access control Blockchain Inner Product encryption Internet of Things (IoT) Security

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A New data-Driven Modelling Framework for Moisture Content Prediction in Continuous Pharmaceutical Tablet manufacturing 11th

A New Data-Driven Modelling Framework for Moisture Content P...

引用

11th international conference on Industrial engineering and Applications - Europe (ICIEA - Europe)

作者： Deebes, Motaz Mahfouf, Mahdi Omar, Chalak Univ Sheffield Dept Automat & Control Syst Engn Sheffield England Univ Sheffield Dept Multidisciplinary Engn Educ Sheffield England

ISBN: (纸本)9783031581120;9783031581137

Continuous manufacturing of pharmaceutical tablets integrates multiple unit operations such as twin screw granulation and fluidized bed drying to transform powder into final dosage form such as tablets. However, complex process interactions can lead to variability in critical quality attributes including moisture content of the produced granules. This study presents an innovative multi-stage modelling framework to predict granule moisture content based on the twin screw granulator and the fluidized bed dryer process parameters. Machine learning techniques, including gradient boosting regression, and support vector regression were utilised to enhance predictive performance in ensemble method. Using data from a pilot-scale integrated continuous line, the stacking ensemble model achieved excellent accuracy with (R-2) of 91% for moisture content prediction. The Machine learning modelling framework demonstrates strong potential for advancing process knowledge, and optimization in continuous manufacturing of pharmaceutical tablets based on wet granultion.

关键词： Continuous manufacturing Twin Screw Granulation Fluidized Bed Drying Machine Learning Pilot Plant Scale Predictive Modelling

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Secure Co-Creation of Industrial knowledge Graph: Graph Complement Method with Federated Learning and ChatGPT 19

Secure Co-Creation of Industrial Knowledge Graph: Graph Comp...

引用

19th IEEE international conference on Automation Science and engineering, CASE 2023

作者： Xia, Liqiao Zheng, Pai Liang, Yongshi Zheng, Ge Ling, Zhengyang The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Institution for Manufacturing University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798350320695

Industrial areas have increasingly developed their own knowledge Graph (KG) for organizing and leveraging vast amounts of data. One major challenge in constructing KG is the heavy reliance on available resources, restricting the scalability and accuracy of the resulting graphs. To address this issue, an end-to-end method is proposed to create a multi-benefit ecosystem by integrating Federated Learning with ChatGPT (a popular language model). Different stakeholders may leverage ChatGPT to search for novel knowledge that complements their existing KGs, however, this approach could potentially introduce ambiguous and wrong triples into the KG. To overcome this, Federated Learning is applied to align and disambiguate the triples using other industrial KGs as super-vision. The proposed method applies a multi-field hyperbolic embedding method to vectorize entities and edges, which are then associatively aggregated to achieve edge replenishment and entity fusion for each KG encrypted. Finally, an incentive win-win mechanism is proposed to motivate diverse stakeholders to contribute to this co-creation actively. A case study is conducted on different industrial KG to evaluate the proposed method. Results demonstrate that this method provides a practical solution for KG co-creation and no compromise to data security. © 2023 IEEE.

关键词： Learning systems

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Restaurant Recommendation System using Machine Learning Algorithms

Restaurant Recommendation System using Machine Learning Algo...

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2024 international conference on knowledge engineering and Communication systems, ICKECS 2024

作者： Goyal, Shefali Singh, Nongmeikapam Thoiba Bajetha, Iesh Singh, Sukhpreet Singh, Himanshu Kumar, Prashant Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350359688

The necessity for recommendation systems spans across various domains, not least in the realm of dining and exploring new locales. Restaurants, in particular, benefit from such systems to both attract customers and offer them a chance to indulge in preferred and renowned dishes. Discovering these dishes, especially in unfamiliar areas, poses a significant challenge. This paper introduces a restaurant recommendation system that leverages food and service rating distributions to calculate matrix density. Additionally, we implement a popularity-based model to suggest restaurants to patrons. A ranking mechanism is utilized, based on scoring, to recommend top-rated restaurants and their most popular dishes. We use singular value decomposition and collaborative filtering to improve the model. The model's performance is assessed using the RMSE metric. This study is conducted using data from Kaggle and includes the development of a web-based application created with Python's Flask framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Generalized Self-Attentive Spatiotemporal GCN with OPTICS Clustering for Recommendation systems 15

Generalized Self-Attentive Spatiotemporal GCN with OPTICS Cl...

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15th international conference on Information and knowledge Technology, IKT 2024

作者： Zolfaghari, Saba Hasheminejad, Seyed Mohammad Hossein Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

ISBN: (纸本)9798331522254

In today’s data-driven world, recommender systems are essential for filtering information to deliver personalized content, with collaborative filtering (CF) being a popular technique for predicting user preferences based on past interactions. However, capturing the temporal dynamics of user behavior and handling cold-start scenarios remain significant challenges, as user preferences naturally evolve over time and CF often relies solely on historical data. To address these issues, we propose a novel approach that combines a self-attention-based spatiotemporal graph convolutional network with OPTICS clustering which forms adaptive, time-sensitive subgraphs. This enables the model to adapt to changing user preferences and prioritize the most relevant interactions dynamically. Evaluated on the MovieLens100k dataset, our model outperforms baseline methods, effectively generating embeddings for new user-item pairs and demonstrating an inductive capability that enhances both accuracy and adaptability in recommendations. © 2024 IEEE.

关键词： Spatio-temporal data

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