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Boosting Li-ion storage kinetics via constructing layered TiO_(2) anode

Particuology 2024年第10期93卷 22-29页

作者： Jiyue Hou Fei Wang Enfeng Zhang Ying Wang Peng Dong Yunxiao Wang Yiyong Zhang Xue Li Yingjie Zhang National and Local Joint Engineering Research Center of Lithium-ion Batteries and Materials Preparation Technology Faculty of Metallurgical and Energy EngineeringKunming University of Science and TechnologyKunming650093China College of Electrical Information Engineering Panzhihua UniversityPanzhihua617000China University Wollongong Australian Inst Innovat MatInst Superconducting&Elect MatInnovat Campus Squires WayWollongongNSW2500Australia

Due to the typical intercalation-deintercalation mechanism,TiO_(2) holds great promise as a sustainable anode for next-generation lithium-ion batteries(LIBs).However,commercial TiO_(2)(C–TiO_(2))is granular and shows slow ionic conductivity,which greatly hinders its development due to sluggish kinetics,leading to low reversible capacity and inferior rate *** this study,a two-dimensional layered TiO_(2)(L-TiO_(2))anode is prepared via a one-step calcination process,which can effectively shorten the lithium ions diffusion path and improve its lithium ions *** elucidated the enhanced electrochemical performance of L-TiO_(2) as an anode in LIBs through pseudocapacitive acceleration of lithium ions intercalation and deintercalation using various characterization techniques,including different scan rate cyclic voltammetry tests,in situ electrochemical impedance spectroscopy,in situ Raman spectroscopy,and in situ X-ray *** comparison to C–TiO_(2) material,L-TiO_(2) material showcases remarkable electrochemical performance,achieving a capacity of 166 mAh/g after 100 cycles at 0.1 ***,the lithium-ion diffusion coefficient calculated for the L-TiO_(2) is two orders of magnitude greater,underscoring its potential as a negative electrode material for LIBs.

关键词： Lithium-ionbatteries Anode MXene Layered TiO_(2)

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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Blockchain-based Secure Storage and Cross-domain Sharing Mechanism for Medical Image Data 24

Blockchain-based Secure Storage and Cross-domain Sharing Mec...

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8th International Conference on Cloud and Big Data Computing, ICCBDC 2024

作者： Yao, Chuanjia Jiang, Rong Long, Leijin Dong, Jianhang Wang, Chenguang School of Business Yunnan University of Finance and Economics Yunnan Kunming China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Yunnan Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China School of Information Yunnan University of Finance and Economics Yunnan Kunming China

ISBN: (纸本)9798400717253

With the rapid advancements in medical imaging technologies such as CT, MRI, PET, and ultrasound, these modalities have become pivotal for precise clinical diagnoses and treatment planning. Notably, they facilitate accurate patient evaluations and expedite early disease detection and targeted interventions. However, the voluminous nature of medical image data, coupled with stringent privacy requirements, poses significant challenges to efficient and secure storage and sharing within the realm of big data management. In light of these complexities, this paper introduces a blockchain-centric framework that innovatively integrates InterPlanetary File System (IPFS), cloud storage, AES and blockchain technologies. The framework is meticulously designed to address the dual objectives of secure storage and streamlined sharing of medical image data. A key novelty lies in the strategic differentiation of cloud service roles: the cloud's "chain-on" nodes participate in blockchain's validation and consensus processes without engaging in direct data management or operations, whereas "chain-off" nodes are tasked with establishing transient channels for data transmission upon successful validation of user requests by their chain-on counterparts. In practice, medical image owners encrypt the data using IPFS and create indexes derived from examination reports, facilitating keyword-based searches for data users. Moreover, a Hybrid Encryption scheme with Multiple Public Keys is implemented, necessitating users to utilize their blockchain management system-generated private keys to unlock the AES decryption key for image data access. This multi-faceted technology integration approach not only fortifies the security of storage and ensures privacy preservation but also streamlines retrieval processes and bolsters permission management for medical image data. Therefore, it presents a pioneering solution for medical image data storage and sharing. © 2024 Copyright held by the owner/author

关键词： Storage management

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Ambiguous Instance-Aware Contrastive Network with Multi-Level Matching for Multi-View Document Clustering 39

Ambiguous Instance-Aware Contrastive Network with Multi-Leve...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Shu, Zhenqiu Sun, Teng Luo, Yunwei Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology China

ISBN: (纸本)157735897X

Multi-view document clustering (MvDC) aims to improve the accuracy and robustness of clustering by fully considering the complementarity of different views. However, in real-world clustering applications, most existing works suffer from the following challenges: 1) They primarily align multi-view data based on a single perspective, such as features and classes, thus ignoring the diversity and comprehensiveness of representations. 2) They treat each instance equally in cross-view contrastive learning without considering ambiguous ones, which weakens the model’s discriminative ability. To address these problems, we propose an ambiguous instance-aware contrastive network with multi-level matching (AICN-MLM) for MvDC tasks. This model contains two key modules: a multi-level matching module and an ambiguous instance-aware contrastive learning module. The former attempts to align multi-view data from different perspectives, including features, pseudo-labels, and prototypes. The latter dynamically adjusts instance weights through a weight modulation function to highlight ambiguous instance pairs. Thus, our proposed method can effectively explore the consistency of multi-view document data and focus on ambiguous instances to enhance the model’s discriminative ability. Extensive experimental results on several multi-view document datasets verify the effectiveness of our proposed method. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

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Unintentional emissions of polychlorinated naphthalenes in China:Sources,composition,and historical trends

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Journal of Environmental sciences 2025年第2期148卷 221-229页

作者： Ye Huang Lin Huang Ying Li Nazupar Sidikjan Yunshan Zhang Yan Chen Yangmin Chen Ye Li Wei Du Long Chen YanWu Shanshan Zhang Jing Yang Wenjun Meng Guofeng Shen Min Liu Shu Tao Key Laboratory of Geographic Information Science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China Yunnan Provincial Key Laboratory of Soil Carbon Sequestration and Pollution Control Faculty of Environmental Science&EngineeringKunming University of Science&TechnologyKunming 650500China Laboratory of Earth Surface Processes College of Urban and Environmental SciencePeking UniversityBeijing 100871China

Polychlorinated naphthalenes(PCNs)are detrimental to human health and the *** the commercial production of PCNs banned,unintentional releases have emerged as a significant environmental ***,relevant information is still *** this study,provincial emissions for eight PCNs homologues from 37 sources in the Chinese mainland during the period of 1960-2019 were estimated based on a source-specific and time-varying emission factor *** results showed that the total PCNs emissions in 2019 reached 757.0 kg with Hebei ranked at the top among all the provinces and iron&steel industry as the biggest ***-chlorinated PCNs comprised 90%of emissions by mass,while highly chlorinated PCNs dominated in terms of toxicity,highlighting divergent priorities for mitigating emissions and safeguarding human *** emissions showed an overall upward trend from 1960 to 2019 driven by emission increase from iron&steel industry in terms of source,and from North China and East China in terms of geographic ***-capita emissions followed an inverted U-shaped environmental Kuznets curvewhile emission intensities decreased with increasing per-capita Gross Domestic Product(GDP)following a nearly linear pattern when log-transformed.

关键词： PCNs Emission inventory Emission factor Composition profile Emission intensity Chinese mainland

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Edge-Guided Pixel Level Connected Component Assisted Camouflaged Object Detection

Edge-Guided Pixel Level Connected Component Assisted Camoufl...

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IEEE International Conference on Image Processing

作者： Qingwang Wang Xin Qu Liyao Zhou Pengcheng Jin Chengbiao Fu Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Due to the inherent visual similarity between the camouflaged object and background, camouflaged object detection (COD) is widely recognized as a challenging task in the field of computer vision, and traditional object detection networks often struggle to extract features and accurately identify camouflaged objects. In this paper, we propose an edge-guided pixel level connected component assisted network for COD. Specifically, the edge prior is used to guide object feature extraction and the pixel level connected component obtained from the extracted feature is used to refine the bounding box of the object. We selectively employ a gray-polarization COD dataset to showcase the ability of feature extraction from backgrounds where camouflaged objects may blend in or be occluded. Numerous experiments demonstrate the superiority of our method compared to state-of-the-arts in the case of limited information.

关键词： Visualization Computer vision Image color analysis Image edge detection Collaboration Object detection Feature extraction

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A classification method for compound mass spectrum data based on the Flex-Bootstrap method and Resnet network

A classification method for compound mass spectrum data base...

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第43届中国控制会议

作者： Jiayao P Liping Wu Yong Li Faculty of Information Engineering and Automation Kunming University of Science and Technology

Aiming at the low accuracy of mass spectrum data recognition due to the subjectivity of artificial feature extraction and the limitation of a single sample and unbalanced mass spectrum data in current research,a classification method based on the fusion of the Flex-Bootstrap and Residual Network models is proposed and compared with the DNN model and the fusion model of CNN and *** Flex-Bootstrap method and Resnet fusion model used the Flex-Bootstrap method to expand the combined mass spectrum data perform sparse sequence representation and use the extracted feature vectors to construct the Resnet *** with the traditional classification methods,the proposed method achieves better results in classifying small molecule compounds by mass spectrum *** accuracy of the test set is increased to 98.894%,and the loss function value is reduced to *** model effectively solves the problem of underfitting when using the DNN and the fusion models of CNN and DNN for data classification and improves the prediction accuracy and search efficiency for the mass spectrum database.

关键词： Flex-Bootstrap Residual Network mass spectrum data

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Depth information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing

Depth Information Assisted Collaborative Mutual Promotion Ne...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yafei Zhang Shen Zhou Huafeng Li Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recovering a clear image from a single hazy image is an open inverse problem. Although significant research progress has been made, most existing methods ignore the effect that downstream tasks play in promoting upstream de-hazing. From the perspective of the haze generation mechanism, there is a potential relationship between the depth information of the scene and the hazy image. Based on this, we propose a dual-task collaborative mutual promotion framework to achieve the dehazing of a single image. This framework integrates depth estimation and de-hazing by a dual-task interaction mechanism and achieves mutual enhancement of their performance. To realize the joint optimization of the two tasks, an alternative imple-mentation mechanism with the difference perception is developed. On the one hand, the difference perception between the depth maps of the dehazing result and the ideal image is proposed to promote the dehazing network to pay attention to the non-ideal areas of the dehazing. On the other hand, by improving the depth estimation performance in the difficult-to-recover areas of the hazy image, the de-hazing network can explicitly use the depth information of the hazy image to assist the clear image recovery. To promote the depth estimation, we propose to use the difference between the dehazed image and the ground truth to guide the depth estimation network to focus on the de-hazed unideal areas. It allows dehazing and depth estimation to leverage their strengths in a mutually reinforcing manner. Experimental results show that the proposed method can achieve better performance than that of the state-of-the-art approaches. The source code is released at https://***/zhoushen/IDCMPNet.

关键词： Computer vision Accuracy Inverse problems Source coding Estimation Collaboration Pattern recognition

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A novel deep domain-adversarial method based on DRN-DATL for rolling bearing

A novel deep domain-adversarial method based on DRN-DATL for...

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第43届中国控制会议

作者： Zhuorui Li Xiaodong Wang Bao Ma Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In recent years,a variety of rolling bearing fault diagnosis methods based on deep learning has become an emerging research ***,there is still a gap between the existing diagnostic model and the practical application,i.e.,1) due to the variability of working conditions,the domain shift problem between source domain and the target domain have led to the obvious degradation of the diagnostic performance.2) It is time-consuming and expensive to label a mass of monitoring data in practical *** address this problem,a domain-adversarial transfer learning method based on deep residual network(DRN-DATL) for rolling bearing fault diagnosis is ***,DRN is designed to extract features from raw vibration signal,which can avoid gradient ***,the APReLU activation function is adopted to improved diagnostic accuracy under different working ***,domain-adversarial training is utilized to minimize distribution differences between source domain and target ***,seven comparison methods are selected for systematic analysis,and the results show that the proposed method can effectively improve the overall performance of the diagnosis model,which has achieved higher classification accuracy.

关键词： Domain adversarial transfer learning Adaptively parametric rectifier linear units Deep residual network Rolling bearing Fault diagnosis

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Optimization of Microwave Heating Temperature Field Uniformity Based on DDPG Algorithm

Optimization of Microwave Heating Temperature Field Uniformi...

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第35届中国过程控制会议

作者： Biao Yang Yang Chen Xiucai Chen Haoran Zhang Faculty of Information Engineering and Automation Kunming University of Science and Technology

This study employs the Deep Deterministic Policy Gradient(DDPG) algorithm in deep reinforcement learning to explore its potential in improving uniformity during microwave heating processes. Microwave heating is a crucial technology in the field of thermal processing, yet it often encounters issues with uneven temperature distribution in practical applications, which can affect product quality and processing efficiency. Traditional methods of microwave heating control often struggle to address this challenge. The advantage of the DDPG algorithm lies in its ability to learn policies in continuous action spaces, making it suitable for scenarios in microwave heating where continuous adjustment of power and radiation direction is necessary. In this study, we model the microwave heating system as a reinforcement learning environment, where an intelligent agent optimizes temperature distribution uniformity by adjusting microwave power and radiation direction. The agent uses temperature distribution uniformity as a reward signal and continuously optimizes its policy through the DDPG algorithm to achieve more uniform heating effects. Experimental results demonstrate significant performance advantages of the DDPG algorithm in microwave heating. Compared to traditional microwave heating control methods, we observe that DDPG agents converge more quickly to uniform temperature distributions, reducing energy waste and enhancing product quality and production efficiency. This provides new methods and insights for uniformity control in the field of microwave heating, highlighting the broad prospects of deep reinforcement learning in industrial heating processes.

关键词： microwave heating deep reinforcement learning temperature uniformity

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