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Operando FTIR study on water additive in lithium-sulfur batteries to mitigate shuttle effect

Journal of Energy Chemistry 2024年第11期98卷 702-713页

作者： Érick A.Santos Martim C.Policano Manuel J.Pinzón Isabela Galantini Vanessa A.Goncalves Francisco C.B.Maia Lucyano J.A.Macedo Gustavo Doubek Renato G.Freitas Hudson Zanin Advanced Energy Storage Division Center for Innovation on New EnergiesSchool of Electrical and Computer EngineeringUniversity of CampinasAv Albert Einstein 400CampinasSP 13083-852Brazil Catalytic Processes and Materials Group Department of Chemical EngineeringFaculty of Science and TechnologyMESA+Institute for NanotechnologyUniversity of TwentePO Box 2177500 AE EnschedeThe Netherlands Centre for Cooperative Research on Alternative Energies(CIC EnergiGUNE) Basque Research and Technology Alliance(BRTA)Vitoria-Gasteiz 01510Spain Institute of Physics&Institute of Chemistry Laboratory of Computational MaterialsFederal University of Mato GrossoCuiabá78060-900MTBrazil Brazilian Synchrotron Light Laboratory Brazilian Center for Research in Energy and MaterialsCampinas 13083-970SPBrazil Advanced Energy Storage Division LABCenter for Innovation on New EnergiesSchool of Chemical EngineeringUniversity of CampinasAv Albert Einstein 500Campinas 13083-852SPBrazil

Additives in the electrolytes of Li-S batteries aim to increase overall capacity,improve Li ion conductivity,enhance cyclability,and mitigate the shuttle effect,which is one of the major issues of this ***,the use of water as an additive in the commonly used electrolyte,1.0 M LiTFSI/1.0%(w/w) LiNO_(3) and a 1:1 mixture of 1,3-dioxolane(DOL) and 1,2-dimethoxyethane(DME) was *** used Co_(2)Mn_(0.5)Al_(0.5)O_(4)(CMA) as an electrocatalyst anchored on an activated carbon(AC) electrode with added sulfur via a melt-diffusion *** structural analysis of CMA via Rietveld refinement showed interatomic spaces that can promote ionic conductivity,facilitating Li^(+) ion *** tests determined 1600 ppm as the optimal water concentration,significantly reducing the shuttle ***-mortem XPS analysis focused on the lithium metal anode revealed the formation of Li_(2)O layers in dry samples and LiOH in wet *** capacity was observed in wet samples,which can be attributed to the superior ionic conductivity of LiOH at the electrode/electrolyte interface,surpassing that of Li_(2)O by 12 ***,Operando FTIR experiments provided real-time insights into electrolyte degradation and SEI formation,elucidating the activity mechanisms of water and Li_(2)CO_(3) over the *** work presents results that could aid future advancements in Li-S battery technology,offering possibilities to mitigate its challenges with inexpensive and scalable additives.

关键词： Diffusion Li-S Ionic conductivity Operando FTIR

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blockchain Smart Contract Virtual Machine Optimization technology for Domain Name Systems

Blockchain Smart Contract Virtual Machine Optimization Techn...

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2021 IEEE International Conference on Industrial Application of Artificial Intelligence, IAAI 2021

作者： Wang, Shiyu Wang, Keyuan Jia, Linpeng Yu, Lei Li, Zhongcheng Sun, Yi Li, Jun Zhou, Jihua Sun, Yi Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Xiong' An Intelligent City Innovation Federation Blockchain Lab Xiong' An 071700 China Chongqing Jinmei Communication Chongqing400030 China

ISBN: (纸本)9781665495066

The existing domain name system relies heavily on the root domain name server and has unilateral management problems, which is not conducive to the open and equal development of the Internet environment. blockchain technology provides new technical means to solve the centralization problem of the root resolution system but induces high consumption and slow query issues. In this paper, we studied optimizing the blockchain smart contract virtual machine, specifically EVM (Ethereum Virtual Machine), for realizing a domain name system through a smart contract. We abstracted the key data structure of domain name management and added core attributes to the account structure to accelerate domain name management through transactions. Furthermore, we modified the EVM structure and added EVM opcodes that can directly call the underlying logic related to upper-level domain name management to simplify the transaction processing flow. The evaluations show that the system has achieved a two orders of magnitude improvement in the performance of the domain name resolution system based on blockchain. © 2021 IEEE.

关键词： Smart contract

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Uncertainty-Guided Diffusion Model for Camouflaged Object Detection

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IEEE Transactions on Multimedia 2025年

作者： Yang, Jinsheng Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Song, Shuxiang Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China

Recently, diffusion models have significantly improved the performance of Camouflaged Object Detection (COD) by adding noise to a mask and iteratively denoising it to match the target distributions. Due to the direct extraction of features from noisy masks and the lack of conditional constraints on a prediction area, the diffusion model may deviate from a correct prediction range and produces mispredictions in regions with high uncertainty. To address this issue, we propose an uncertainty-guided diffusion model (UGDNet) for COD, which explicitly quantifies uncertainty and integrates it as an anchor condition into the diffusion models to provide an initialization of the diffusion regions. The core idea is first to utilize a probability representation and transformer to explicitly model uncertainty, aiming to identify areas where a model may generate overconfident mispredictions. Then, we use the uncertainty as an anchor condition to provide a reference prediction range for the diffusion model, guiding each step of the diffusion process. Furthermore, we use uncertainty to guide feature aggregation, prompting the model to pay extra attention to the semantic features of regions with high uncertainty to refine the segmentation results further. The experimental results indicate that our proposed UGDNet achieves higher accuracy than existing state-of-the-art models on five COD benchmarks, including COD10K, NC4K, CAMO, CHAMELEON, and CDS2K. © 1999-2012 IEEE.

关键词： Semantics

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CNNRF-Ensemble: A Multifaced Approach For Predicting White Spot Syndrome Virus In Shrimp Farming

CNNRF-Ensemble: A Multifaced Approach For Predicting White S...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Kazi Tanvir Sadi Al Huda Shaiful Ajam Opee Sayem Kabir Asraf Ali Touhid Bhuiyan Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Artificial Intelligence Research and Innovation Lab Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria VA USA

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries, filling the rapid growth demand, especially in Bangladesh. However, the White Spot Syndrome Virus(WSSV) creates and constantly threatens the global shrimp population, impacting the economic and ecological systems. Recently, researchers have tried to predict disease, but no studies have achieved significant accuracy using ML and DL algorithms. In this study, we have proposed an optimized CNNRF-Ensemble model for predicting WSSV using multifaceted data, including geographical features, environmental attributes, farm characteristics, operational practices, and socioeconomic indicators. Our proposed model achieved 95.714% accuracy in sequential model and afterwards, combining with RandomForestClassifier in Ensemble learning we obtained 99.99% accuracy. Our study suggested deep learning as an accurate, easy and low-cost method to predict shrimp WSSV, which helps to predict viruses and create awareness of shrimp firming.

关键词： Deep learning Accuracy Biological system modeling Signal processing algorithms Ensemble learning Convolutional neural networks Socioeconomics Viruses (medical) Farming Diseases

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OldVisOnline: Curating a Dataset of Historical Visualizations

arXiv

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arXiv 2023年

作者： Zhang, Yu Jiang, Ruike Xie, Liwenhan Zhao, Yuheng Liu, Can Ding, Tianhong Chen, Siming Yuan, Xiaoru The Department of Computer Science University of Oxford United Kingdom the School of Intelligence Science and Technology Peking University China The School of Data Science Fudan University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fundamental Software Innovation Lab Huawei Technologies China

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital libraries

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GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts

arXiv

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arXiv 2024年

作者： Guo, Zihao Sun, Qingyun Yuan, Haonan Fu, Xingcheng Zhou, Min Gao, Yisen Li, Jianxin School of Computer Science and Engineering Beihang University China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Huawei Technologies Co. Ltd China Institute of Artificial Intelligence Beihang University China

Real-world graphs have inherently complex and diverse topological patterns, known as topological heterogeneity. Most existing works learn graph representation in a single constant curvature space that is insufficient to match the complex geometric shapes, resulting in low-quality embeddings with high distortion. This also constitutes a critical challenge for graph foundation models, which are expected to uniformly handle a wide variety of diverse graph data. Recent studies have indicated that product manifold gains the possibility to address topological heterogeneity. However, the product manifold is still homogeneous, which is inadequate and inflexible for representing the mixed heterogeneous topology. In this paper, we propose a novel Graph Mixture of Riemannian Experts (GraphMoRE) framework to effectively tackle topological heterogeneity by personalized fine-grained topology geometry pattern preservation. Specifically, to minimize the embedding distortion, we propose a topology-aware gating mechanism to select the optimal embedding space for each node. By fusing the outputs of diverse Riemannian experts with learned gating weights, we construct personalized mixed curvature spaces for nodes, effectively embedding the graph into a heterogeneous manifold with varying curvatures at different points. Furthermore, to fairly measure pairwise distances between different embedding spaces, we present a concise and effective alignment strategy. Extensive experiments on realworld and synthetic datasets demonstrate that our method achieves superior performance with lower distortion, highlighting its potential for modeling complex graphs with topological heterogeneity, and providing a novel architectural perspective for graph foundation models. © 2024, CC BY.

关键词： Graph embeddings

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Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction

arXiv

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arXiv 2024年

作者： Jiang, Jianping Zhou, Xinyu Wang, Bingxuan Deng, Xiaoming Xu, Chao Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks salient texture appearance for hand mesh reconstruction. In this paper, we propose EvRGBHand – the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time, space, and information dimensions, EvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity as well as background overflow issues in event-based HMR. We further propose EvRGBDegrader, which allows our model to generalize effectively in challenging scenes, even when trained solely on standard scenes, thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera. For code, models, and dataset, please refer to https://***/***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Unpaired Multimodal Facial Expression Recognition 15th

Unpaired Multimodal Facial Expression Recognition

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15th Asian Conference on computer Vision, ACCV 2020

作者： Xia, Bin Wang, Shangfei Key Lab of Computing and Communication Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China Anhui Robot Technology Standard Innovation Base Hefei China

ISBN: (纸本)9783030695408

Current works on multimodal facial expression recognition typically require paired visible and thermal facial images. Although visible cameras are readily available in our daily life, thermal cameras are expensive and less prevalent. It is costly to collect a large quantity of synchronous visible and thermal facial images. To tackle this paired training data bottleneck, we propose an unpaired multimodal facial expression recognition method, which makes full use of the massive number of unpaired visible and thermal images by utilizing thermal images to construct better image representations and classifiers for visible images during training. Specifically, two deep neural networks are trained from visible and thermal images to learn image representations and expression classifiers for two modalities. Then, an adversarial strategy is adopted to force statistical similarity between the learned visible and thermal representations, and to minimize the distribution mismatch between the predictions of the visible and thermal images. Through adversarial learning, the proposed method leverages thermal images to construct better image representations and classifiers for visible images during training, without the requirement of paired data. A decoder network is built upon the visible hidden features in order to preserve some inherent features of the visible view. We also take the variability of the different images’ transferability into account via adaptive classification loss. During testing, only visible images are required and the visible network is used. Thus, the proposed method is appropriate for real-world scenarios, since thermal imaging is rare in these instances. Experiments on two benchmark multimodal expression databases and three visible facial expression databases demonstrate the superiority of the proposed method compared to state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Infrared imaging

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Expelliarmus: Command Cancellation Attacks on Smartphones using Electromagnetic Interference

Expelliarmus: Command Cancellation Attacks on Smartphones us...

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

作者： Ming Gao Fu Xiao Weiran Liu Wentao Guo Yangtao Huang Yajie Liu Jinsong Han School of Cyber Science and Technology Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China School of Computer Science Nanjing University of Posts and Telecommunications China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks China Zhejiang Provincial Key Laboratory of Blockchain and Cyberspace Governance China

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users’ operations and thus taking full control over victim devices. Expelliarmus neutralizes users’ touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users’ commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.

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A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation

arXiv

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arXiv 2025年

作者： Ma, Xiaowen Lian, Rongrong Wu, Zhenkai Guan, Renxiang Hong, Tingfeng Zhao, Mengjiao Ma, Mengting Nie, Jiangtao Du, Zhenhong Song, Siyang Zhang, Wei School of Software Technology Zhejiang University Hangzhou310027 China Noah's Ark Lab Huawei Shanghai201206 China School of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Computer Science and Technology Northwestern Polytechnical University Xian710072 China School of Earth Sciences Zhejiang University Hangzhou310027 China School of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Computing and Mathematical Sciences University of Leicester United Kingdom Innovation Center of Yangtze River Delta Zhejiang University Jiaxing314103 China

As a common method in the field of computer vision, spatial attention mechanism has been widely used in semantic segmentation of remote sensing images due to its outstanding long-range dependency modeling capability. However, remote sensing images are usually characterized by complex backgrounds and large intra-class variance that would degrade their analysis performance. While vanilla spatial attention mechanisms are based on dense affine operations, they tend to introduce a large amount of background contextual information and lack of consideration for intrinsic spatial correlation. To deal with such limitations, this paper proposes a novel scene-Coupling semantic mask network, which reconstructs the vanilla attention with scene coupling and local global semantic masks strategies. Specifically, scene coupling module decomposes scene information into global representations and object distributions, which are then embedded in the attention affinity processes. This Strategy effectively utilizes the intrinsic spatial correlation between features so that improve the process of attention modeling. Meanwhile, local global semantic masks module indirectly correlate pixels with the global semantic masks by using the local semantic mask as an intermediate sensory element, which reduces the background contextual interference and mitigates the effect of intra-class variance. By combining the above two strategies, we propose the model SCSM, which not only can efficiently segment various geospatial objects in complex scenarios, but also possesses inter-clean and elegant mathematical representations. Experimental results on four benchmark datasets demonstrate the the effectiveness of the above two strategies for improving the attention modeling of remote sensing images. For example, compared to the recent advanced method LOGCAN++, the proposed SCSM has 1.2%, 0.8%, 0.2%, and 1.9% improvements on the LoveDA, Vaihingen, Potsdam, and iSAID datasets, respectively. The dataset and cod

关键词： Semantic Segmentation

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