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Deep learning enhanced prediction of microwave dielectric constant of spinel ceramics eliminating manual feature engineering

引用

Materials Today Physics 2025年 54卷

作者： Liu, Xiaobin Huang, Qiuxia Su, Chang Shao, Ning Zhang, Lei Tian, Yapeng Zhou, Huanfu College of Physics and Electronic Information Engineering Guilin University of Technology Guilin514004 China College of Computer Science and Engineering Guilin University of Technology Guilin514004 China Guangxi Key Laboratory of Embedded Technology and Intelligent Systems Guilin University of Technology Guilin514004 China Henan Institute of Advanced Technology College of Materials Science and Engineering Zhengzhou University Zhengzhou450003 China School of Materials Science and Engineering Guilin University of Technology Guilin514004 China

Machine learning (ML) has demonstrated considerable promise and superiority in the prediction of microwave dielectric ceramic (MWDC) properties. Nonetheless, applying traditional ML models often requires processing numerous features, making their implementation complex and unwieldy. In this work, a deep learning (DL) model named CRANCNN-M2V that predicts the dielectric constant of spinel-MWDCs eliminating manual feature engineering has been constructed. The model can identify essential features directly from chemical compositions using the data embedding method. Our model further improved the network based on a Compositionally Restricted Attention-based Neural Network (CrabNet) and showed the enhanced prediction performance in the dielectric constant of spinel-MWDCs, achieving an RMSE of 1.52, an MAE of 0.938, and an R2 of 0.954, and it also outperformed commonly used traditional ML models (e.g., XGBoost, Random Forest (RF), Decision Tree (DT), etc.). Furthermore, the contribution of different elements in the dielectric constant of spinel MWDCs has been analyzed via our CRANCNN-M2V model. Highly accurate and efficient prediction of our model will effectively promote the design and development of spinel-MWDCs applied for wireless communication. © 2025 Elsevier Ltd

关键词： Prediction models

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Integrating Static Code Analysis Toolchains

arXiv

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arXiv 2024年

作者： Kern, Matthias Erata, Ferhat Iser, Markus Sinz, Carsten Loiret, Frederic Otten, Stefan Sax, Eric FZI Research Center for Information Technology Karlsruhe Germany Karlsruhe Institute of Technology Institut für Theoretische Informatik Karlsruhe Germany KTH Royal Institute of Technology Embedded Control Systems Stockholm Sweden Yale University Department of Computer Science New Haven United States UNIT Information Technologies Research & Development Izmir Turkey

This paper proposes an approach for a tool-agnostic and heterogeneous static code analysis toolchain in combination with an exchange format. This approach enhances both traceability and comparability of analysis results. State of the art toolchains support features for either test execution and build automation or traceability between tests, requirements and design information. Our approach combines all those features and extends traceability to the source code level, incorporating static code analysis. As part of our approach we introduce the "ASSUME Static Code Analysis tool exchange format" that facilitates the comparability of different static code analysis results. We demonstrate how this approach enhances the usability and efficiency of static code analysis in a development process. On the one hand, our approach enables the exchange of results and evaluations between static code analysis tools. On the other hand, it enables a complete traceability between requirements, designs, implementation, and the results of static code analysis. Within our approach we also propose an OSLC specification for static code analysis tools and an OSLC communication framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Static analysis

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Paulina Pawłowicz Vaidy Sunderam Che-Lun Hung Andrzej Kwiecień Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data.

关键词： Proteins Solid modeling Three-dimensional displays Pipelines Cluster computing Big Data Biology Organisms Decoding Sparks

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Challenges and Opportunities of DevOps in Cyber-Physical Production systems Engineering

Challenges and Opportunities of DevOps in Cyber-Physical Pro...

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Industrial Cyber-Physical systems (ICPS)

作者： István Koren Felix Rinker Kristof Meixner Jasminka Matevska Jörg Walter Chair of Process and Data Science RWTH Aachen University Aachen Germany Christian Doppler Laboratory SQI Institute of Information Systems Engineering TU Wien Vienna Austria School of Electrical Engineering and Computer Science City University of Applied Sciences Bremen Germany Distributed Computing and Communication OFFIS - Institute for Information Technology Oldenburg Germany

DevOps is a set of practices that combines software development and operations to enable a continuous software product life cycle to improve the quality of software systems. Although DevOps is considered successful for typical software systems, it has not yet been widelyadopted in the area of - Physlcal Production systems (CPPSs) that integrate physical components with computer-based control and communication systems. Related to industrial settings, many new challenges arise, like long-term investments, missing flexibility in asset-heavy production environments, and the inherent physicality of hardware. This paper examines the use of the DevOps methodology in the manufacturing domain. It identifies and discusses the unique challenges and describes first solution proposals to overcome those challenges, based on literature and experiences from the industry. The article provides useful guidance to researchers and practitioners on potential pitfalls and exciting opportunities.

关键词： Production systems Data collection Software systems Hardware Safety Stakeholders Security

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MEMORY-ENRICHED COMPUTATION AND LEARNING IN SPIKING NEURAL NETWORKS THROUGH HEBBIAN PLASTICITY

arXiv

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arXiv 2022年

作者： Limbacher, Thomas Özdenizci, Ozan Legenstein, Robert Institute of Theoretical Computer Science Graz University of Technology Graz8010 Austria TU Graz SAL Dependable Embedded Systems Lab Silicon Austria Labs Graz8010 Austria

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network architecture that is enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. It improves their abilities for out-of-distribution generalization, one-shot learning, cross-modal generative association, language processing, and reward-based learning. As spiking neural networks are the basis for energy-efficient neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle. © 2022, CC BY.

关键词： Neural networks

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Provably Convergent Federated Trilevel Learning

arXiv

引用

arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data

Semantic Map Learning of Traffic Light to Lane Assignment ba...

引用

International Conference on Intelligent Transportation

作者： Thomas Monninger Andreas Weber Steffen Staab Mercedes-Benz Research & Development North America Sunnyvale CA USA Mercedes-Benz AG Research & Development Stuttgart Germany University of Stuttgart Institute of Parallel and Distributed Systems Stuttgart Germany Electronics and Computer Science University of Southampton Southampton United Kingdom

Understanding which traffic light controls which lane is crucial to navigate intersections safely. Autonomous vehicles commonly rely on High Definition (HD) maps that contain information about the assignment of traffic lights to lanes. The manual provisioning of this information is tedious, expensive, and not scalable. To remedy these issues, our novel approach derives the assignments from traffic light states and the corresponding motion patterns of vehicle traffic. This works in an automated way and independently of the geometric arrangement. We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method. In addition, our novel rejection method includes accompanying safety considerations by leveraging statistical hypothesis testing. Finally, we propose a dataset transformation to re-purpose available motion prediction datasets for semantic map learning. Our publicly available API for the Lyft Level 5 dataset enables researchers to develop and evaluate their own approaches 5 5 https://***/map-learning/tl2la.

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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E-THeTA: An Efficient and Adaptive TDMA Approach for Wireless Body Area Networks

E-THeTA: An Efficient and Adaptive TDMA Approach for Wireles...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Jorge F. Herculano Luís O. S. de Andrade Willians De P. Pereira Alirio S. de Sá Distributed Systems Laboratory (LaSiD) Graduate Program in Mechatronics (PPGM) Computer Science Institute (IC) and Polytechnic School (EP) Federal University of Bahia (UFBA) Ondina Campus Salvador Bahia Brazil Federal University of Southern Bahia (UFSB) Itabuna Campus Bahia Brazil Sergipe Federal Institute of Education Science and Technology (IFS) Socorro Campus Sergipe Brazil Rondônia Federal Institute of Education Science and Technology (IFRO) Porto Velho Campus Rondônia Brazil

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

Several research efforts have proposed adaptive solutions for Time Division Multiple Access (TDMA) to satisfy time requirements and save energy on Wireless Body Area Networks (WBANs) affected by radiofrequency interferences and carrying dynamic traffic. Even with the benefits, most of these solutions waste network bandwidth, decreasing network performance and the ability to recover from some unexpected communication conditions. This paper introduces the E-THeTA, an adaptive and efficient approach based on TDMA crafted to enhance performance, save energy, and efficiently use WBAN bandwidth. Compared to IEEE 802.15.4 and IEEE 802.15.6 standards and other adaptive approaches, the proposed solution reduces message losses, consumes less energy, and maintains low communication latencies in most evaluated scenarios.

关键词： Wireless communication IEEE 802.15 Standard Radio frequency Time division multiple access Adaptive systems Bandwidth Body area networks Standards

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Destination Scheduling for Secure Pinhole-Based Power-Line Communication

arXiv

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arXiv 2023年

作者： Kundu, Chinmoy Dubey, Ankit Tonello, Andrea M. Nallanathan, Arumugam Flanagan, Mark F. School of Electrical and Electronic Engineering University College Dublin Belfield Ireland Department of EE Indian Institute of Technology Jammu Jammu & Kashmir India Institute of Networked and Embedded Systems University of Klagenfurt Austria School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a powerline communication (PLC) based Internet-of-Things system in the presence of an eavesdropper. We consider a pinhole (PH) architecture for a multi-node PLC network to capture the keyhole effect in PLC. The transmitter-to-PH link is shared between the destinations and an eavesdropper which correlates all end-to-end links. The individual channel gains are assumed to follow independent log-normal statistics. Furthermore, the additive impulsive noise at each node is modeled by an independent Bernoulli-Gaussian process. Exact computable expressions for the average secrecy capacity (ASC) and the probability of intercept (POI) performance over many different networks are derived. Approximate closed-form expressions for the asymptotic ASC and POI are also provided. We find that the asymptotic ASC saturates to a constant level as transmit power increases. We observe that the PH has an adverse effect on the ASC. Although the shared link affects the ASC, it has no effect on the POI. We show that by artificially controlling the impulsive to background noise power ratio and its arrival rate at the receivers, the secrecy performance can be improved. © 2023, CC BY.

关键词： Impulse noise

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