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Long Short-term cognitive networks

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arXiv 2021年

作者： Nápoles, Gonzalo Grau, Isel Jastrzebska, Agnieszka Salgueiro, Yamisleydi Department of Cognitive Science & Artificial Intelligence Tilburg University Netherlands Information Systems Group Eindhoven University of Technology Netherlands Faculty of Mathematics and Information Science Warsaw University of Technology Poland Department of Computer Science Faculty of Engineering Universidad de Talca Campus Curicó Chile

In this paper, we present a recurrent neural system named Long Short-term Cognitive Networks (LSTCNs) as a generalization of the Short-term Cognitive Network (STCN) model. Such a generalization is motivated by the difficulty of forecasting very long time series efficiently. The LSTCN model can be defined as a collection of STCN blocks, each processing a specific time patch of the (multivariate) time series being modeled. In this neural ensemble, each block passes information to the subsequent one in the form of weight matrices representing the prior knowledge. As a second contribution, we propose a deterministic learning algorithm to compute the learnable weights while preserving the prior knowledge resulting from previous learning processes. As a third contribution, we introduce a feature influence score as a proxy to explain the forecasting process in multivariate time series. The simulations using three case studies show that our neural system reports small forecasting errors while being significantly faster than state-of-the-art recurrent models. © 2021, CC BY-NC-ND.

关键词： Forecasting

Design of FBG-Based Sensing System for Meat Quality Assessment and Steak Cooking Optimisation

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IET Optoelectronics 2025年第1期19卷

作者： Jawad Mirza Ahmad Atieh Firdos Kanwal Salman Ghafoor Muhammad Ijaz Benish Kanwal Anwar Ul Haq Ahmad Almogren SEECS Photonics Research Group Islamabad Pakistan Electrical Engineering Department HITEC University Taxila Taxila Pakistan Optiwave Systems Inc. Ottawa Canada Electrical Engineering Department Mirpur University of Science and Technology Mirpur Pakistan SEECS National University of Sciences and Technology Islamabad Pakistan Department of Engineering Manchester Metropolitan University Manchester UK Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Water presence in raw meat not only degrades its quality by diluting the natural flavours, but also increases the risk of bacterial growth, altering texture, and juiciness which ultimately affects the quality of end products. Therefore, quality assessment of raw meat in markets as well as in eateries is important to ensure the consumer's safety and satisfaction. In this paper, we propose a nondestructive technique to detect water presence in raw meat and steak cooking temperature optimisation using a fibre Bragg grating (FBG) temperature sensor inserted into the piece of raw meat. The contribution of this paper is twofold: First, the water presence in the piece of raw meat is detected by analysing the shift in Bragg wavelength corresponding to time-dependent gradual decrease in temperature of heated probe of FBG sensor due to convective heat transfer from probe to meat. Secondly, the optimum cooking temperature of a particular type of steak is achieved after quality assessment of meat by analysing the shift in Bragg wavelength corresponding to time-dependent gradual increase in probe temperature due to convective heat transfer from meat to probe. The results show that the proposed sensing system can complete the error-free detection of water in 250 g piece of raw meat within 30 s for water contents of 50, 75, and 100 g which account for 20%, 30%, and 40% of the net weight of meat, respectively. Moreover, the simulated and experimental values of temperature sensitivity (TS) of FBG sensor used in this work are around 13 and 10 pm/°C, respectively. The proof of the concept of a smart restaurant based upon the proposed work is also discussed. This study provides a nondestructive, simple, and fast solution for detection of water in raw meat and achieving the optimum cooking temperature for different types of steaks.

关键词： Bragg gratings fibre optic sensors indoor communication optical communication optical fibre networks

Machine learning pipeline for battery state of health estimation

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arXiv 2021年

作者： Roman, Darius Saxena, Saurabh Robu, Valentin Flynn, David Pecht, Michael Smart Systems Group School of Engineering & Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Center for Advanced Life Cycle Engineering University of Maryland College ParkMD20742 United States CWI National Research Institute for Mathematics and Computer Science Amsterdam Netherlands

Lithium-ion batteries are ubiquitous in modern day applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this paper, we design and evaluate a machine learning pipeline for estimation of battery capacity fade — a metric of battery health — on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasising the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and can be generalized to other critical components that require real-time estimation of SOH. © 2021, CC BY.

关键词： Pipelines

Reconfigurable Embedded Devices Using Reinforcement Learning to Develop Action-Policies

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Reconfigurable Embedded Devices Using Reinforcement Learning...

Autonomic Computing and Self-Organizing systems (ACSOS),International Conference on

作者： Alwyn Burger David W King Gregor Schiele Embedded Systems Group University of Duisburg-Essen Duisburg Germany Dept of Electrical and Computer Engineering Air Force Institute of Technology Dayton OH USA

ISBN: (数字)9781728172774

ISBN: (纸本)9781728172781

The size of sensor networks supporting smart cities is ever increasing. Sensor network resiliency becomes vital for critical networks such as emergency response and waste water treatment. One approach is to engineer `self-aware' sensors that can proactively change their component composition in response to changes in work load when critical devices fail. By extension, these devices could anticipate their own termination, such as battery depletion, and offload current tasks onto connected devices. These neighboring devices can then reconFigure themselves to process these tasks, thus avoiding catastrophic network failure. In this article, we present an array of self-aware sensors who use Q-learning to develop a policy that guides device reaction to various environmental stimuli. The novelty lies in the use of field programmable gate arrays embedded on the sensors that take into account internal system state, configuration, and learned state-action pairs, that guide device decisions in order to meet system demands. Experiments show that even relatively simple reward functions develop Q-learning policies that yield positive device behaviors in dynamic environments.

关键词： Task analysis Field programmable gate arrays Performance evaluation Servers Hardware Computational modeling Embedded systems

Retraction notice to “Thermal management of a battery pack using a layer of phase change material around the batteries: Changes in the airflow through the battery” [J. Energy Storage 52 (2022) 104759]

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Journal of Energy Storage 2024年 92卷

作者： Mohammed N. Ajour Ahmad H. Milyani Nidal H. Abu-Hamdeh Turki AlQemlas Moath K. Khaled Arash Karimipour Center of Research Excellence in Renewable Energy and Power Systems Energy Efficiency Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Mechanical Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah 21589 Saudi Arabia Energy Efficiency Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia College of Engineering and Technology American University of the Middle East Kuwait Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Dipartimento di Ingegneria Astronautica Elettrica ed Energetica Sapienza Universit'a di Roma Via Eudossiana 18 Roma 00184 Italy

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

A game-theoretic approach towards collaborative coded computation offloading

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arXiv 2021年

作者： Ng, Jer Shyuan Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Leung, Cyril Kim, Dong In Zhang, Junshan Yang, Qiang Alibaba Group and Alibaba-NTU Joint Research Institute Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology Design Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Canada Sungkyunkwan University Korea Republic of School of Electrical Computer and Energy Engineering Arizona State University United States Hong Kong University of Science and Technology Hong Kong

Coded distributed computing (CDC) has emerged as a promising approach because it enables computation tasks to be carried out in a distributed manner while mitigating straggler effects, which often account for the long overall completion times. Specifically, by using polynomial codes, computed results from only a subset of edge servers can be used to reconstruct the final result. However, incentive issues have not been studied systematically for the edge servers to complete the CDC tasks. In this paper, we propose a tractable two-level game-theoretic approach to incentivize the edge servers to complete the CDC tasks. Specifically, in the lower level, a hedonic coalition formation game is formulated where the edge servers share their resources within their coalitions. By forming coalitions, the edge servers have more Central Processing Unit (CPU) power to complete the computation tasks. In the upper level, given the CPU power of the coalitions of edge servers, an all-pay auction is designed to incentivize the edge servers to participate in the CDC tasks. In the all-pay auction, the bids of the edge servers are represented by the allocation of their CPU power to the CDC tasks. The all-pay auction is designed to maximize the utility of the cloud server by determining the allocation of rewards to the winners. Simulation results show that the edge servers are incentivized to allocate more CPU power when multiple rewards are offered, i.e., there are multiple winners, instead of rewarding only the edge server with the largest CPU power allocation. Besides, the utility of the cloud server is maximized when it offers multiple homogeneous rewards, instead of heterogeneous rewards. © 2021, CC0.

关键词： Game theory

Semantic Communications:Theories,Technologies and Applications

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中国通信（英文版） 2024年第7期21卷前插1-前插5页

作者： Zhang Ping Shi Guangming Cui Shuguang Zhang Zhaoyang Niu Kai Xiao Yong Qin Zhijin Dai Jincheng Shao Shuo Deniz Gündüz Eleonora Grassucci School of Information and Communication Engineering at Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology IMT-2020(5G)Experts Panel Experts Panel for China's 6G development Peng Cheng Laboratory School of Artifi-cial Intelligence Xidian University electrical and computer engineering with the University of Arizona Texas A&M University UC Davis Chinese Univer-sity of Hong Kong(Shenzhen)(CUHK-Shenzhen) Zhejiang University School of Artificial Intelli-gence BUPT School of Electron-ic Information and Communications at the Huazhong University of Science and Technology(HUST) WuhanChina Peng Cheng Laboratory Guang-zhouChina and the Pazhou Laboratory(Huangpu)Guang-zhouChina Tsinghua University BeijingChina Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and Telecommunications School of Electronics Informa-tion and Electrical EngineeringShanghai Jiao Tong Univer-sityChina Information Processing in the Electrical and Electronic Engineering Department at Imperial College London UK Intelligent Systems and Networks Group Department at Imperial College London UK Department of Information Engineering Electronicsand Telecommunications of the Sapienza University of Rome

The last seventy years have witnessed the transi-tion of communication from Shannon's theoretical concept to current high-efficiency practical *** respect to Shannon information theory,to fulfill the high-bandwidth utilization,any further increase in the data rate requires a significant augmentation in the received signal power unless the bandwidth is extended in proportion to the incre-mental data rate.

关键词： Special issues and sections Semantics Communication systems Data processing Information theory Data augmentation

Fast, reliable, and secure drone communication: A comprehensive survey

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arXiv 2021年

作者： Hassija, Vikas Chamola, Vinay Agrawal, Adhar Goyal, Adit Luong, Nguyen Cong Niyato, Dusit Yu, F. Richard Guizani, Mohsen Department of Computer Science and IT Jaypee Institute of Information Technology Noida201304 India Department of Electrical and Electronics Engineering APPCAIR BITS-Pilani Pilani Campus 333031 India Faculty of Information Technology PHENIKAA University Hanoi12116 Viet Nam A&A Green Phoenix Group JSC No. 167 Hoang Ngan Trung Hoa Cau Giay Hanoi11313 Viet Nam School of Computer Science and Engineering Nanyang Technological University Singapore Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Computer Science and Engineering Department Qatar University Qatar

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them. © 2021, CC BY.

关键词： Fog computing