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SWOT analysis on the transition from Lithium-Ion batteries to Sodium-Ion batteries

Sustainable Energy Technologies and Assessments 2025年 80卷

作者： Ebrahim Pilali Faraz Forutan Nia Eliyad Yamini Farbod Esmaeilion Walied Alfraidi Alireza Taklifi Davide Astiaso Garcia M. Soltani Department of Mechanical Engineering K.N. Toosi University of Technology Tehran 19967-15433 Iran Faculty of Mechanical Engineering Arak University of Technology Arak Iran Department of Electrical Engineering College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11564 Saudi Arabia Center of Excellence in Design and Optimization of Energy Systems School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Advanced Energy Initiative Center K.N. Toosi University of Technology Tehran 14176-14411 Iran Department of Planning Design Technology of Architecture Sapienza University of Rome Via Flaminia 72 00196 Rome Italy Waterloo Institute for Sustainable Energy (WISE) University of Waterloo Waterloo ON N2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada Centre for Sustainable Business International Business University Toronto Canada Balsillie School Fellow at the Balsillie School of International Affairs (BSIA) Waterloo ON Canada

The natural and atmospheric circumstances regarding energy resources are experiencing a transformation at the global level due to the exhaustion of the supply of conventional energy resources and climate change. With regards to energy storage systems, lithium-ion batteries (LIBs) have remained the most popular energy storage system technologies because of their high energy density, longer cycle life, and reliability. Nonetheless, the costs and scarcity of lithium as a raw material for REs are unreasonable in the long term. But, new chairs such as sodium-ion batteries (SIBs) are seeing potential benefits over traditional batteries including the availability of Sodium, cost of raw materials, and eco-friendliness. However, there are concerns with SIBs about their less energy density in comparison with LIBs, which needs the invention of expertise. This paper seeks to establish the feasibility of SIBs by analyzing their strengths and weaknesses, opportunities, and threats. Major and minor drawbacks presented in any SIB system are discussed regarding cost performance, safer operation, and environmental gains related to drawbacks such as energy density and cycles per life. Also, important dilemmas regarding LIBs presented in the literature including high efficiency, high power density, and environmentally sound recycling methods are discussed. This study underscores how different conditions may boost SIB performance, commercial feasibility and need for further technical efforts to eliminate existing obstacles so that SIBs can be effectively applied to smarter grids and other renewable energy systems.

关键词： Sodium-ion Battery Lithium-ion Battery SWOT analysis Battery recycling

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Corrigendum to “Renewables but unjust? Critical restoration geography as a framework for addressing global renewable energy injustice” [Energy Res. Soc. Sci. 114 (2024) 103609]

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Energy Research & Social Science 2024年 115卷

作者： Chinedu C. Nsude Rebecca Loraamm Joshua J. Wimhurst God'sgift N.. Chukwuonye Ramit Debnath Department of Geography and Environmental Sustainability University of Oklahoma Norman OK 73019 USA Center for Peace and Development University of Oklahoma Norman OK 73019 USA South Central Climate Adaptation Science Center University of Oklahoma Norman OK 73019 USA Department of Environmental Science University of Arizona Tucson AZ 85721 USA Cambridge Zero and Computer Laboratory University of Cambridge Cambridge CB3 0HE UK Cambridge Collective Intelligence and Design Group Department of Architecture University of Cambridge CB2 1PX UK Division of Humanities and Social Sciences California Institute of Technology Pasadena CA 92115 USA

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Efficiently Inferring Top-k Elephant Flows based on Discrete Tensor Completion

Efficiently Inferring Top-k Elephant Flows based on Discrete...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kun Xie Jiazheng Tian Xin Wang Gaogang Xie Jigang Wen Dafang Zhang College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York at Stony Brook USA State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Finding top- k elephant flows is a critical task in network measurement, with applications such as congestion control, anomaly detection, and traffic engineering. Traditional top- k flow detection problem focuses on using a small amount of memory to measure the total number of packets or bytes of each flow. Instead, we study a challenging problem of inferring the top- k elephant flows in a practical system with incomplete measurement data as a result of sub-sampling for scalability or data missing. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top- k elephant flows. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top- k flow inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top- k elephant flows with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. Extensive experiments using two real traces demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar data inference accuracy using significantly smaller time and storage space.

关键词： Inference algorithms Binary codes Data models Partitioning algorithms Indexes Computational modeling

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Online Internet Anomaly Detection With High Accuracy: A Fast Tensor Factorization Solution

Online Internet Anomaly Detection With High Accuracy: A Fast...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaocan Li Kun Xie Xin Wang Gaogang Xie Jigang Wen Guangxing Zhang Zheng Qin Department of Electrical and Computer Engineering State University of New York at Stony Brook USA College of Computer Science and Electronics Engineering Hunan University Changsha China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate traffic anomaly detection is very important, it very difficult to achieve. We propose to utilize tensor model to well exploit the multi-dimensional information hidden in the traffic data for more accurate online Internet anomaly detection. We decouple the tensor recovery problem to iteratively solve two sub problems, a tensor factorization sub-problem and an anomaly detection sub-problem. To reduce the high cost for computation and storage involved in tensor factorization, we propose two lightweight techniques to effectively derive factor matrices of tensor in the current window and iteration, taking advantage of tensor decomposition results of the previous window and iteration. We have done extensive experiments using two real traffic traces to compare with three tensor based algorithms and three matrix based algorithms. The experiment results demonstrate that our online anomaly detection algorithm can achieve the same anomaly detection accuracy as that of the best offline tensor based algorithm, but at 6100 times faster speed and with very low storage cost.

关键词： Anomaly detection Monitoring Matrix decomposition Microsoft Windows Principal component analysis Data models

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Forecasting business failure in highly imbalanced distribution based on delay line reservoir 26

Forecasting business failure in highly imbalanced distributi...

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26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018

作者： Rodan, A. Castillo, P.A. Faris, H. Al-Zoubi, Ala M. Mora, A.M. Jawazneh, H. Higher Colleges of Technology United Arab Emirates King Abdullah II School for Information Technology University of Jordan Jordan Department of Computer Architecture and Technology ETSIIT CITIC University of Granada Spain Spain

ISBN: (纸本)9782875870476

Bankruptcy is a critical financial problem that affects a high number of companies around the world. Thus, in recent years an increasing number of researchers have tried to solve it by applying different machine-learning models as powerful tools for the different economical agents related to the company. In this work, we propose the use of a simple deterministic delay line reservoir (DLR) state space by combining it with three popular classification algorithms (J48, k-NN, and MLP) as an efficient and accurate solution to the bankruptcy prediction problem. The proposed approach is evaluated on a real world dataset collected from Spanish companies. Obtained results show that the proposed models have a higher predictive ability than traditional classification approaches (without DLR reservoir state), resulting in a suitable and efficient alternative approach to solve this complex problem. © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Neural networks

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The scalability for parallel machine learning training algorithm: Dataset matters

arXiv

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arXiv 2019年

作者： Cheng, Daning Zhang, Hanping Xia, Fen Li, Shigang Zhang, Yunquan University of Chinese Academy of Sciences Beijing China SKL of Computer Architecture Institute of Computing Technology CAS China Department of Computer Science ETH Zurich Switzerland University at Buffalo State University of New York Wisdom Uranium Technology Co.Ltd Beijing

To gain a better performance, many researchers put more computing resource into an application. However, in the AI area, there is still a lack of a successful large-scale machine learning training application: The scalability and performance reproducibility of parallel machine learning training algorithm are limited and there are a few pieces of research focusing on why these indexes are limited but there are very few research efforts explaining the reasons in essence. In this paper, we propose that the sample difference in dataset plays a more prominent role in parallel machine learning algorithm scalability. Dataset characters can measure sample difference. These characters include the variance of the sample in a dataset, sparsity, sample diversity and similarity in sampling sequence. To match our proposal, we choose four kinds of parallel machine learning training algorithms as our research objects: (1) Asynchronous parallel SGD algorithm (Hogwild! algorithm) (2) Parallel model average SGD algorithm (Mini-batch SGD algorithm) (3) Decenterilization optimization algorithm, (4) Dual Coordinate Optimization (DADM algorithm). These algorithms cover different types of machine learning optimization algorithms. We present the analysis of their convergence proof and design experiments. Our results show that the characters datasets decide the scalability of the machine learning algorithm. What is more, there is an upper bound of parallel scalability for machine learning algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Scalability

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Reinforcement learning for channel coding: learned bit-flipping decoding

arXiv

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arXiv 2019年

作者： Carpi, Fabrizio Häger, Christian Martalò, Marco Raheli, Riccardo Pfister, Henry D. Department of Electrical and Computer Engineering New York University BrooklynNY United States Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Engineering and Architecture University of Parma Parma Italy Department of Electrical and Computer Engineering Duke University DurhamNC United States

In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve a decision-making process at each step, including bit-flipping (BF) decoding, residual belief propagation, and anchor decoding. We then illustrate how such algorithms can be mapped to Markov decision processes allowing for data-driven learning of optimal decision strategies, rather than basing decisions on heuristics or intuition. As a case study, we consider BF decoding for both the binary symmetric and additive white Gaussian noise channel. Our results show that learned BF decoders can offer a range of performance–complexity trade-offs for the considered Reed–Muller and BCH codes, and achieve near-optimal performance in some cases. We also demonstrate learning convergence speed-ups when biasing the learning process towards correct decoding decisions, as opposed to relying only on random explorations and past knowledge. Copyright © 2019, The Authors. All rights reserved.

关键词： White noise

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Using Gradient Based Multikernel Gaussian Process and Meta-Acquisition Function to Accelerate SMBO

Using Gradient Based Multikernel Gaussian Process and Meta-A...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Daning Cheng Hanping Zhang Fen Xia Shigang Li Yunquan Zhang SKL of Computer Architecture Institute of Computing Technology CAS China University of Chinese Academy of Sciences Beijing China Wisdom Uranium technology Co.Ltd Beijing University at Buffalo State University of New York Department of Computer Science ETH Zurich Switzerland

Automatic machine learning (automl) is a crucial technology in machine learning. Sequential model-based optimisation algorithms (SMBO) (e.g., SMAC, TPE) are state-of-the-art hyperparameter optimisation methods in automl. However, SMBO does not consider known information, like the best hyperparameters high possibility range and gradients. In this paper, we accelerate the traditional SMBO method and name our method as accSMBO. In accSMBO, we build a gradient-based multikernel Gaussian process with a good generalisation ability and we design meta-acquisition function which encourages that SMBO puts more attention on the best hyperparameters high possibility range. In L2 norm regularised logistic loss function experiments, our method exhibited state-of-the-art performance.

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GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS

arXiv

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arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

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Triple-band EBG Reflector Using CPW Structure with Interdigital Technique

Triple-band EBG Reflector Using CPW Structure with Interdigi...

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International Symposium on Antennas and Propagation (ISAP)

作者： T. Archevapanich P. Chomtong P. Akkaraekthalin Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Teacher Training in Electrical Engineering King Mongkut’s University of Technology North Bangkok Thailand Department of Electrical and Computer Engineering King Mongkut’s University of Technology North Bangkok Thailand

The electromagnetic band gap (EBG) unit cell was designed from mushroom-like EBG structure by adapting a coplanar waveguide (CPW) feed structure and interdigital technique on mushroom-like EBG. This new designed EBG is defined as interdigital coplanar waveguide (ICPW) structure. The proposed EBG can reduce the original mushroom-like EBG dimension from λ/2 to λ/4, which the ICPW-EBG unit cell dimension is 14.6x14.6 mm 2 . The ICPW-EBG unit cell was designed to response three frequency bands at 1.8 GHz, 2.45 GHz and 3.7 GHz. The array of 10×10 ICPW-EBG unit cells was then formed with the dimension of 147.3x147.3 mm 2 . When installing a dipole antenna above the ICPW-EBG array, the simulation results showed that the dipole antenna directivities at all frequency bands were improved from 2.15 dB to 9.12 dB at 1.8 GHZ, 9.05 dB at 2.45 GHz and 8.4 dB at 3.7 GHz, respectively.

关键词： Periodic structures Metamaterials Coplanar waveguides Power transmission lines Resonant frequency Dipole antennas Antenna arrays

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