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Constructing a stable interface on Ni-rich LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) cathode via lactic acid-assisted engineering strategy

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Journal of Energy Chemistry 2024年第3期90卷 412-422,I0010页

作者： Weijian Tang Chengzhi Hu AFei Li Xiaoqin Huang Zhangxian Chen Jianhui Su Weixin Zhang Institute of Energy Hefei Comprehensive National Science CenterHefei 230031AnhuiChina School of Chemistry and Chemical Engineering Hefei University of Technology and Anhui Key Laboratory of Controllable Chemical Reaction&Material Chemical EngineeringHefei 230009AnhuiChina School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009AnhuiChina

Ni-rich layered oxides are potential cathode materials for next-generation high energy density Li-ion batteries due to their high capacity and low ***,the inherently unstable surface properties,including high levels of residual Li compounds,dissolution of transition metal cations,and parasitic side reactions,have not been effectively addressed,leading to significant degradation in their electrochemical *** this study,we propose a simple and effective lactic acid-assisted interface engineering strategy to regulate the surface chemistry and properties of Ni-rich LiNi_(0.8)Co_(0.1)Mr_(0.1)O_(2) *** novel surface treatment method successfully eliminates surface residual Li compounds,inhibits structural collapse,and mitigates cathode-electrolyte interface film *** a result,the lactic acidtreated LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) achieved a remarkable capacity retention of 91.7% after 100 cycles at 0.5 C(25℃) and outstanding rate capability of 149.5 mA h g^(-1) at 10 C,significantly outperforming the pristine ***,a pouch-type full cell incorporating the modified LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) cathode demonstrates impressive long-term cycle life,retaining 81.5% of its capacity after 500 cycles at 1 *** importantly,the thermal stability of the modified cathode is also dramatically *** study offers a valuable surface modification strategy for enhancing the overall performance of Ni-rich cathode materials.

关键词： Residual Li Lactic acid Surface modification Carbon coating Layered cathode Ni-rich

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Analyzing the performance of geometric mean optimization-based artificial neural networks for cryptocurrency forecasting

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International Journal of Information technology (Singapore) 2024年 1-13页

作者： Behera, Sudersan Kumar, A V S Pavan Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering and Technology GIET University Odisha Gunupur India Department of Computer Science and Engineering School of Technology GITAM University Hyderabad India

In this study, we utilize a recently proposed non-parametric metaheuristic algorithm known as geometric mean optimization (GMO) to adjust the hidden layer input weights and bias of six ANN variants, namely PSNN, SPNN, JPSNN, FLANN, RBFN, and MLP, thereby creating six hybrid models. Later on, we engage all these hybrid models to predict the closing price of four widely used cryptocurrencies. For comparison purposes, we also engage the widely popular traditional GD algorithm to fix the hidden layer input weights and biases of the same set of ANNs, creating six alternate models and employing those models for the same prediction task. In order to assess the effectiveness of each model, we employ the mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) statistics. The experimental result shows the superiority of GMO-based PSNN over others. We then compare the GMO-based PSNN's outcomes with some of the existing hybrid models in the literature. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ANN Cryptocurrency forecasting Financial time series forecasting GMO Higher order neural networks

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Ontology-Based Semantic Reasoning for Multi-Source Heterogeneous Industrial Devices Using OPC UA

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IEEE Internet of Things Journal 2025年第13期12卷 25020-25032页

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2014 IEEE.

关键词： Structured Query Language

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Addressing Transport Issues in Non‑Aqueous Li–air Batteries to Achieving High Electrochemical Performance

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Electrochemical Energy Reviews 2023年第1期6卷 187-233页

作者： Zhuojun Zhang Xu Xiao Xingbao Zhu Peng Tan Department of Thermal Science and Energy Engineering University of Science and Technology of China(USTC)Hefei 230026AnhuiChina School of Physics Harbin Institute of TechnologyHarbin 150080HeilongjiangChina

Li–air batteries are a promising type of energy storage technology because of the ultra-high theoretical specific *** advances are made in recent years,including the illustration of reaction mechanisms,development of effective catalyst materials,and design of battery structures accelerating species ***,the application still suffers from low rate capability,poor round-trip efficiency,and unsatisfactory cycling ***,we mainly focus on the species transport issues of non-aqueous Li–air batteries,including Li^(+) across the solid surfaces and the electrolyte,O_(2)solubility and diffusivity,distribution of intermediates and products,and side reactions by other components from the ***,considerable emphasis is paid to expound the approaches for enhancing species transport and accelerating reactions,among which the realization of well-designed electrode structures and flowing electrolytes is of great significance for the rapid migration of O_(2)and Li^(+)and mitigating the negative effects by solid insoluble Li_(2)O_(2).Moreover,optimizing reaction interfaces and operating conditions is an attractive alternative to promote reaction *** work aims to identify the mechanism of transport issues and corresponding challenges and perspectives,guiding the structure design and material selection to achieve high-performance Li–air batteries.

关键词： Li-air battery Solubility Species transport Key issues Enhancement

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Knowledge Contrast-Enhanced Continuous Prompt Tuning for few-shot learning

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Neural Computing and Applications 2025年第19期37卷 14151-14169页

作者： Li, Fei Huang, Youzhi Wang, Yanyan Chen, Zhengyi Xu, Yin Li, Xiangyang School of Computer Science and Technology University of Science and Technology of China Fuxing Road 100 Anhui Hefei230031 China Innovation + Research Institute GuoChuang Cloud Technology Ltd. Wenqu Road 355 Anhui Hefei230031 China The CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Fuxing Road 100 Anhui Hefei230031 China

Prompt tuning for pre-trained language models (PLMs) has shown its effectiveness and superiority in few-shot learning. However, its success heavily depends on prompt engineering which designs different prompts for specific tasks. Continuous prompt templates can avoid this compared to discrete prompt templates, but how to obtain an optimal continuous prompt template is a question worth studying. In order to overcome this challenge, we focus on eliciting knowledge from PLMs and propose a novel Knowledge Contrast-Enhanced Continuous Prompt Tuning method (KCPT) based on contrastive learning. This method aims to obtain more meaningful and distinguishable prompt embeddings by introducing a contrastive learning strategy, enabling the model to better capture subtle differences between different prompts. Extensive experiments on 10 datasets demonstrate that our KCPT improves accuracy by 1–3% and stability by 26–40% on average compared to SOTA prompt tuning methods in low-resource settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Contrastive Learning

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Dust-Mamba: An Efficient Dust Storm Detection Network with Multiple Data Sources 39

Dust-Mamba: An Efficient Dust Storm Detection Network with M...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Bai, Cong Lin, Zhonghao Zhang, Jinglin Chen, Shengyong College of Computer Science Zhejiang University of Technology China School of Control Science and Engineering Shangdong University China School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)157735897X

Accurate detection of dust storms is challenging due to complex meteorological interactions. With the development of deep learning, deep neural networks have been increasingly applied to dust storm detection, offering better learning and generalization capabilities compared to traditional physical modeling. However, existing methods face some limitations, leading to performance bottlenecks in dust storm detection. From the task perspective, existing research focuses on occurrence detection while neglecting intensity detection. From the data perspective, existing research fails to explore the utilization of multi-source data. From the model perspective, most models are built on convolutional neural networks, which have an inherent limitation in capturing long-range dependencies. To address the challenges mentioned, this study proposes Dust-Mamba. To the best of our knowledge, this study is the first attempt to accomplish both the occurrence and intensity detection of dust storms with advanced deep learning technology. In Dust-Mamba, multi-source data is introduced to provide a comprehensive perspective, Mamba and attention are applied to boost feature selection while maintaining long-range modeling capability. Additionally, this study proposes Structure Sharing Transfer Learning Strategies for intensity detection, which further enhances the performance of Dust-Mamba with minimal time cost. As shown by experiments, Dust-Mamba achieves Dice scores of 0.963 for occurrence detection and 0.560 for intensity detection, surpassing several baseline models. In conclusion, this study offers valuable baselines for dust storm detection, with significant reference value and promising application potential. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Seeking Nash Equilibrium for Linear Discrete-time Systems via Off-policy Q-learning

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IAENG International Journal of Applied Mathematics 2024年第11期54卷 2477-2483页

作者： Ni, Haohan Ji, Yuxiang Yang, Yuxiao Zhou, Jianping School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215000 China

This paper considers a non-zero-sum game for linear discrete-time systems involving two players. Based on a quadratic value function, we derive coupled algebraic Riccati equations. Then, we propose both on-policy and off-policy Qlearning algorithms, which operate without prior knowledge of the system dynamics, to achieve Nash equilibrium. These algorithms necessitate the inclusion of probing noise to ensure the persistence of excitation. We show that the on-policy Qlearning algorithm may introduce bias to the Nash equilibrium due to the probing noise, while the off-policy Q-learning algorithm maintains an unbiased property. Finally, we offer a numerical example to validate the effectiveness of the presented on-policy and off-policy Q-learning algorithms. © (2024), (International Association of Engineers). All rights reserved.

关键词： Riccati equations

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DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

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Neural Computing and Applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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computer Modeling in engineering & sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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Constructing prediction intervals to explore uncertainty based on deep neural networks

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10441-10456页

作者： Yang, Jingling Chen, Liren Chen, Huayou Liu, Jinpei Han, Bing School of Big Data and Statistics Anhui University Hefei China School of Marine Science and Technology Tianjin University Tianjin China School of Business Anhui University Anhui Hefei China Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States School of Mathematical Science Anhui University Hefei China

The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of 'high coverage and narrow width'. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of 'high coverage, narrow width, and small bias' in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. © 2024 - IOS Press. All rights reserved.

关键词： Forecasting

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