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Perspective on cycling stability of lithium-iron manganese phosphate for lithium-ion batteries

Rare Metals 2023年第3期42卷 740-750页

作者： Kun Zhang Zi-Xuan Li Xiu Li Xi-Yong Chen Hong-Qun Tang Xin-Hua Liu Cai-Yun Wang Jian-Min Ma Guangxi Key Laboratory of Processing for Nonferrous Metals and Featured Materials School of ResourcesEnvironment and MaterialsGuangxi UniversityNanning 530004China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 611731China School of Transportation Science and Engineering Beihang UniversityBeijing 102206China Intelligent Polymer Research Institute AIIMInnovation CampusUniversity of WollongongNSW 2500Australia

Lithium-iron manganese phosphates(LiFex Mn_(1-x)PO_(4),0.1<x<0.9)have the merits of high safety and high working ***,they also face the challenges of insufficient conductivity and poor cycling *** progress has b... 详细信息

Lithium-iron manganese phosphates(LiFex Mn_(1-x)PO_(4),0.1

关键词： Lithium iron manganese phosphate Cathode Cycling stability Electrolyte modification Doping Coating Controlled synthesis

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhongyang Li Xin Yuan Wei Liu Xin Xu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view.

关键词： Location awareness Accuracy Attention mechanisms Image coding Signal processing Encoding Satellite images Object recognition Speech processing Drones

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space 20

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall. © 2024 IEEE.

关键词： Convolutional neural networks

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Adaptive Monitoring Optimization Based on Deep-Q-Network for Energy Harvesting Wireless Sensor Networks 12th

Adaptive Monitoring Optimization Based on Deep-Q-Network for...

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12th International Conference on Mobile Networks and Management, MONAMI 2022

作者： Bao, Xuecai Bian, Peilun Tan, Wenqun Xu, Xiaohua Nie, Jugen Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang Jiangxi330099 China Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin Jiangxi330029 China

ISBN: (纸本)9783031324420

In order to improve the energy efficiency of environmental monitoring for energy harvesting wireless sensor networks (EH-WSNs) in remote areas and achieve energy-neutral operation, an adaptive monitoring and energy management optimization method of EH-WSNs based on deep Q network (DQN) algorithm is proposed. In this paper, aiming at EH-WSNs with single-hop cluster structure, we first present a more realistic energy model established by combining different climate characteristics. Then, the optimization model of maximizing long-term monitoring utility is formulated based on harvested energy constraints. We use deep Q network (DQN) to learn random and dynamic solar energy harvesting process on solar-powered sensor nodes and optimize the monitored performance of EH-WSNs through the replay memory mechanism and freezing parameter mechanism. Finally, we present an adaptive monitoring optimization method based DQN to achieve the long-term utility. Through simulation verification and comparative analysis, in different rainy weather environments, the proposed optimization algorithm has greatly improved in terms of average monitoring reward, monitoring interruption rate and energy overflow rate. Moreover, it also indicates that the proposed algorithm has effective adaptation to the random and dynamic solar energy arrival. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Energy harvesting

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A New Approach of Intrusion Detection with Command Sequence-To-Sequence Model 6th

A New Approach of Intrusion Detection with Command Sequence-...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Liu, Wei Mao, Yu Ci, Linlin Zhang, Fuquan School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Traditionally, researchers have focused on network level intrusion detection and program level intrusion detection to improve computer security. However, neither approach is foolproof. We argue that the internal and external security of a computer system are equally important. Typically, a successful attacker manifests in the form of the attacker becoming a user on the host either with elevated or normal user privileges. At this point, user-level intrusion detection attempts to deter and curtail an attacker even after the system has been compromised. In this work, we introduce a new approach of intrusion detection based on recurrent neural networks (RNNs) to solve the long sequential problem. We build a robust user command sequence-to-sequence model by semantic modeling command. Our model implements the prediction of user command sequence and the prophesying of user behaviors. The experimental results on data sets of Purdue University, SEA and self-collected data show that an accurate, effective and efficient detection can be achieved by using the proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Steel Surface Defect Detection Based on Improved YOLOv8 20

Steel Surface Defect Detection Based on Improved YOLOv8

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Zhang, Jichen Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

There are some defects on the surface of steel that are difficult to detect, so we propose an improved algorithm based on YOLOv8 for detecting defects that are difficult to be detected. This improvement includes the introduction of an SPD module to minimize the problem of information loss due to downsampling;the addition of a small object detection head combined with EMA attention to further enhance the detection capabilities for small targets;the use of BiFPN to replace the original PANet, applying different weights to different features to strengthen feature fusion. Experiments were conducted on the NEU-DET dataset, and the algorithm improved mAP to 80.2%, an improvement of 3.9% over the baseline model. © 2024 IEEE.

关键词： Change detection

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Enhancing energy density of poly(vinylidene fluoride) composite film via addition of N-doped carbon modified boron nitride and sandwich-structural engineering

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Applied Surface Science 2023年第1期640卷

作者： Huang, Huabo Mei, Wentao Wang, Yucong Huang, Juan Li, Liang Ji, Jiayou Xiong, Chuanxi Hubei Key Laboratory of Plasma Chemistry and Advanced Materials Key Laboratory for Green Chemical Process of Ministry of Education School of Materials Science and Engineering Wuhan Institute of Technology Wuhan430205 China Key Laboratory of Green Processing and Functional New Textile Materials of Ministry of Education School of Textile Science and Engineering Wuhan Textile University Wuhan430200 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing School of Materials Science and Engineering Wuhan University of Technology Wuhan430070 China

It is a challenge to simultaneously enhance dielectric constant and breakdown strength of dielectrics and achieve a greatly elevated energy density. Herein, polyaniline was first coated onto the surface of boron nitride nanosheets (BN), and then nitrogen-doped carbon (NC) was formed through a carburization process, resulting in BN@NC composite, which was subsequently added to poly(vinylidene fluoride) (PVDF) to prepare a composite film with high dielectric performance. The characterization showed that BN@NC effectively preserved the nanosheet structure of BN and formed strong hydrogen bonding interactions with the PVDF matrix, promoting the β-PVDF formation. This not only improved the compatibility but also enhanced the interfacial polarization between conductive NC and the polymer, leading to a high dielectric constant. Moreover, the electrically insulating BN prevented the formation of leakage current, resulting in a high breakdown strength. Finally, a sandwich-structured film was constructed, and exhibited a dielectric constant of 10.2 (at 1 kHz), a breakdown strength of 380 MV m−1, and an energy density of 6.1 J cm−3, which were 20%, 36%, and 74% higher, respectively, than those of pure PVDF film (8.5, 280 MV m−1, and 3.5 J cm−3). This work highlights significant potential for applications in low-cost and high-energy density dielectric composites. © 2023 Elsevier B.V.

关键词： Boron nitride

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A Chinese Sentiment Analysis Model Incorporating Knowledge Graph and Deep Learning 7

A Chinese Sentiment Analysis Model Incorporating Knowledge G...

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7th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023

作者： Zhao, Shengping Chen, Jinlong Qin, Xingguo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9798350382877

With the rapid growth of social media and e-commerce, users are generating large amounts of textual review data on these platforms. Analyzing and mining these comment data is important for governments, enterprises, and consumers to make decisions. Therefore, sentiment analysis, especially for Chinese comments, has become an area of wide interest in industry and academia. Aiming at the characteristics of Chinese online reviews, this paper applies the techniques of knowledge graph and deep learning around sentence-level sentiment analysis and designs a sentiment analysis model that integrates different information such as sentiment knowledge graph and serialized annotation in a deep learning model to improve the accuracy and effectiveness of sentiment analysis of Chinese online reviews. This is important for improving products and services, understanding user needs, and making more informed business and policy decisions. © 2023 IEEE.

关键词： Sentiment analysis

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Lifetime Extension Control for Multi-Stack SOFC System with Inconsistent Stacks 42

Lifetime Extension Control for Multi-Stack SOFC System with ...

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42nd Chinese Control Conference, CCC 2023

作者： Li, Zexin Liu, Guoqiang Zhao, Wei Li, Xi School of Artificial Intelligence and Automation Huazhong University of Science & Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581543

The multi-stack fuel cell system (MFCS) is an important basis for the application of solid oxide fuel cell (SOFC) technology to large-scale power generation systems. MFCS can provide higher power, longer lifetime and better reliability. Different from the single-stack fuel cell system (SFCS), there may be some inconsistencies among the stacks in the MFCS. If the inconsistencies are ignored, they will significantly affect the lifetime of the MFCS. This study focuses on the lifetime extension of multi-stack SOFC power generation systems with inconsistent stacks. Since the multi-stack system of SOFC generally cannot control the flow rate of each stack respectively, it brings a difficulty to the control of the MFCS with inconsistencies among the stacks. In order to overcome this difficulty, this paper built a multi-stack SOFC system model, which can characterize the degradation and inconsistencies among SOFC stacks. And then designed a power distribution strategy based on fuzzy control to extend the lifetime of the MFCS. The strategy was verified by simulations. The simulation results show that the strategy can effectively control the inconsistencies among the stacks and significantly extend the lifetime of the MFCS. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Fuzzy control

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A Parallel Read-Write Circuit Design for Driving Memristor Crossbar Array

A Parallel Read-Write Circuit Design for Driving Memristor C...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Jiang, Ningye Jiang, Mingxuan Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9798350316889

Memristor crossbar array is considered as a promising circuit module for accelerating neural networks. Because the memristor is tunable and multi-state, it is important to design applicable Read-Write (RW) circuit for driving memristor crossbar array. In this paper, a novel RW circuit based on analog circuits is proposed for tuning memristors in high-dimensional array parallelly, which is scalable and low-overhead. The standalone RW circuit schematic is presented for reading/writing single memristor and verified in circuit simulation. Then, we extend and apply this circuit into a two-dimensional memristor crossbar array. The performance of memristor crossbar array controlled by designed RW circuits is verified via the simulations under various machine learning datasets. The comparison with related works is presented, which demonstrates the advantages of the proposed RW circuit regarding overhead and anti-error. © 2023 IEEE.

关键词： Memristors

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