检索结果-南通市图书馆

Identifying the dynamic behaviors in complete reconstruction of Co-based complex precatalysts during electrocatalytic oxygen evolution

引用

Journal of Energy Chemistry 2025年第1期100卷 226-233页

作者： Jingfang Zhang Danyang Wu Linke Cai Youluan Lu Fanpeng Cheng Lijuan Shi Qun Yi Yao Liu Yi Huang key laboratory of Green Chemical Engineering Process of Ministry of Education Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education Hubei Key Laboratory of Novel Reactor and Green Chemical Technology School of Chemical Engineering & Pharmacy Wuhan Institute of Technology key laboratory of Pesticide & Chemical Biology of Ministry of Education Engineering Research Center of Photoenergy Utilization for Pollution Control and Carbon Reduction of Ministry of Education College of Chemistry Central China Normal University College of Materials Science and Engineering Fuzhou University

Transition metal-based nanomaterials have emerged as promising electrocatalysts for oxygen evolution reaction（OER）. Considerable research efforts have shown that self-reconstruction occurs on these nanomaterials under operating conditions of OER process. However, most of them undergo incomplete reconstruction with limited thickness of reconstruction layer, leading to low component utilization and arduous exploration of real catalytic mechanism. Herein, we identify the dynamic behaviors in complete reconstruction of Co-based complexes during OER. The hollow phytic acid（PA） cross-linked CoFe-based complex nanoboxes with porous nanowalls are designed because of their good electrolyte penetration and mass transport ability, in favor of the fast and complete reconstruction. A series of experiment characterizations demonstrate that the reconstruction process includes the fast substitution of PA by OH-to form Co（Fe）(OH)xand subsequent potential-driven oxidation to Co（Fe）OOH. The obtained CoFeOOH delivers a low overpotential of 290 mV at a current density of 10 mA cm-2and a long-term *** experiment results together with theory calculations reveal that the Fe incorporation can result in the electron rearrangement of reconstructed CoFeOOH and optimization of their electronic structure,accounting for the enhanced OER activity. The work provides new insights into complete reconstruction of metal-based complexes during OER and offers guidelines for rational design of high-performance electrocatalysts.

关键词： Complete reconstruction Co-based complex Precatalysts Oxygen evolution reaction

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Metal nanoparticles decorated CoFe-(oxy)hydroxysulfides nanosheets fabricated by a general strategy for electrocatalytic water splitting

引用

Journal of Energy Chemistry 2025年第1期100卷 26-38页

作者： Xiaodong Yang Haochen Shen Xiaoming Xiao Zhichao Li Qi Zhou Wei Yang Bin Jiang Yongli Sun Luhong Zhang Zhenhua Yan School of Chemical Engineering and technology Tianjin University key laboratory of Advanced Energy Materials Chemistry (Ministry of Education) Nankai University

This study presents a novel method to fabricate metal-decorated,sulfur-doped layered double hydroxides （M/SLDH） through spontaneous redox and sulfurization *** developed Ag/SLDH and Pt/SLDH catalysts with abundant heterogeneous interfaces and hierarchical nanostructures demonstrated outstanding oxygen evolution reaction （OER） and hydrogen evolution reaction （HER） performance,achieving low overpotentials of 212 and 35 mV at 10 mA cm-2in 1 M KOH,*** both anode and cathode in water splitting,they required only 1.47 V to reach 10 mA cm-2and exhibited high structural robustness,maintaining stability at 1000 mA cm-2for 300 ***-situ Raman analysis revealed that the synergistic effects of metal nanoparticles and S doping significantly promote the transformation into the S-Co1-xFexOOH layer,which serves as the active phase for water ***,ultraviolet photoelectron spectroscopy （UPS） and density functional theory （DFT） analyses indicated that incorporating metal nanoparticles and S doping increase electron density near the Fermi level and reduce reaction energy barriers,thus enhancing intrinsic OER and HER *** study provides a scalable strategy for synthesizing high-performance electrocatalysts for water splitting,with promising potential for broader applications.

关键词： LDH Spontaneous reaction Metal nanoparticles Water splitting

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Crowdsourcing label quality: a theoretical analysis

引用

Science China(Information Sciences) 2015年第11期58卷 113-124页

作者： WANG Wei ZHOU Zhi-Hua national key laboratory for novel software technology Nanjing University

Crowdsourcing has been an effective and efficient paradigm for providing labels for large-scale unlabeled data. In the past few years, many methods have been developed for inferring labels from the crowd,but few theoretical analyses have been presented to support this popular human-machine interaction *** this paper, we theoretically study the quality of labels inferred from crowd workers by majority voting and provide an analysis of label quality that shows that the label error rate decreases exponentially with the number of workers selected for each task. We also study the problem of eliminating low-quality workers from the crowd,and provide a conservative condition for eliminating low-quality workers without eliminating any non-low-quality worker with high probability. We also provide an aggressive condition for eliminating all low-quality workers with high probability.

关键词： unlabeled data crowdsourcing majority voting label quality worker selection

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

A Region-Based Analysis for the Feature Concatenation in Deep Forests

引用

Chinese Journal of Electronics 2022年第6期31卷 1072-1080页

作者： LYU Shen-Huan CHEN Yi-He ZHOU Zhi-Hua national key laboratory for novel software technology Nanjing University

Deep forest is a tree-based deep model made up of non-differentiable modules that are trained without backpropagation. Despite the fact that deep forests have achieved considerable success in a variety of tasks, feature concatenation, as the ingredient for forest representation learning, still lacks theoretical understanding. In this paper, we aim to understand the influence of feature concatenation on predictive performance. To enable such theoretical studies, we present the first mathematical formula of feature concatenation based on the two-stage structure, which regards the splits along new features and raw features as a region selector and a region classifier respectively. Furthermore, we prove a region-based generalization bound for feature concatenation,which reveals the trade-off between Rademacher complexities of the two-stage structure and the fraction of instances that are correctly classified in the selected *** a consequence, we show that compared with the prediction-based feature concatenation(PFC), the advantage of interaction-based feature concatenation(IFC) is that it obtains more abundant regions through distributed representation and alleviates the overfitting risk in local regions. Experiments confirm the correctness of our theoretical results.

关键词： trees (mathematics) raw features pattern classification deep learning (artificial intelligence) deep forest prediction-based feature concatenation forest representation learning tree-based deep model learning (artificial intelligence) region-based generalization feature extraction region-based analysis backpropagation interaction-based feature concatenation

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

software Defect Detection with ROCUS

引用

Journal of Computer Science & technology 2011年第2期26卷 328-342页

作者：姜远黎铭周志华 national key laboratory for novel software technology Nanjing University

software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system. Although many machine learning methods have been successfully applied to the task, most of them fail to consider two practical yet important issues in software defect detection. First, it is rather difficult to collect a large amount of labeled training data for learning a well-performing model; second, in a software system there are usually much fewer defective modules than defect-free modules, so learning would have to be conducted over an imbalanced data set. In this paper~ we address these two practical issues simultaneously by proposing a novel semi-supervised learning approach named Rocus. This method exploits the abundant unlabeled examples to improve the detection accuracy, as well as employs under-sampling to tackle the class-imbalance problem in the learning process. Experimental results of real-world software defect detection tasks show that Rocus is effective for software defect detection. Its performance is better than a semi-supervised learning method that ignores the class-imbalance nature of the task and a class-imbalance learning method that does not make effective use of unlabeled data.

关键词： machine learning data mining semi-supervised learning class-imbalance software defect detection

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Rts: learning robustly from time series data with noisy label

引用

Frontiers of Computer Science 2024年第6期18卷 186332-186332页

作者： ZHOU Zhi JIN Yi-Xuan Li Yu-Feng national key laboratory for novel software technology Nanjing University

Significant progress has been made in machine learning with large amounts of clean labels and static data. However, in many real-world applications, the data often changes with time and it is difficult to obtain massive clean annotations, that is, noisy labels and time series are faced simultaneously. For example, in product-buyer evaluation, each sample records the daily time behavior of users, but the long transaction period brings difficulties to analysis, and salespeople often erroneously annotate the user’s purchase behavior. Such a novel setting, to our best knowledge, has not been thoroughly studied yet, and there is still a lack of effective machine learning methods. In this paper, we present a systematic approach RTS both theoretically and empirically, consisting of two components, Noise-Tolerant Time Series Representation and Purified Oversampling Learning. Specifically, we propose reducing label noise’s destructive impact to obtain robust feature representations and potential clean samples. Then, a novel learning method based on the purified data and time series oversampling is adopted to train an unbiased model. Theoretical analysis proves that our proposal can improve the quality of the noisy data set. Empirical experiments on diverse tasks, such as the house-buyer evaluation task from real-world applications and various benchmark tasks, clearly demonstrate that our new algorithm robustly outperforms many competitive methods.

关键词： weakly-supervised learning time-series classification class-imbalanced learning

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Learnware:small models do big

引用

Science China(Information Sciences) 2024年第1期67卷 39-50页

作者： Zhi-Hua ZHOU Zhi-Hao TAN national key laboratory for novel software technology Nanjing University

There are complaints about current machine learning techniques such as the requirement of a huge amount of training data and proficient training skills,the difficulty of continual learning,the risk of catastrophic forgetting,and the leaking of data privacy/*** research efforts have been focusing on one of those concerned issues separately,paying less attention to the fact that most issues are entangled in *** prevailing big model paradigm,which has achieved impressive results in natural language processing and computer vision applications,has not yet addressed those issues,whereas becoming a serious source of carbon *** article offers an overview of the learnware paradigm,which attempts to enable users not to need to build machine learning models from scratch,with the hope of reusing small models to do things even beyond their original purposes,where the key ingredient is the specification which enables a trained model to be adequately identified to reuse according to the requirement of future users who know nothing about the model in advance.

关键词： artificial intelligence machine learning learnware

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Enhancing unsupervised domain adaptation by exploiting the conceptual consistency of multiple self-supervised tasks

引用

Science China(Information Sciences) 2023年第4期66卷 126-139页

作者： Hui SUN Ming LI national key laboratory for novel software technology Nanjing University

Unsupervised domain adaptation(UDA) aims to transfer the knowledge from a label-rich source domain to an unlabeled target domain. Current approaches mainly focus on aligning the target domain’s data distribution with the source domain, but lacking attention to allowing target data to guide which features need to be captured. Source data dominates feature extraction. As a result, target embedding would lose some vital discriminative features and limit UDA in complex tasks. In this paper, we argue that utilizing auxiliary tasks to capture target-intrinsic patterns, which do not depend on source supervision, could further enhance UDA. Multiple auxiliary tasks understand the instance concept from different *** findings show that exploiting the conceptual consistency of multiple auxiliary tasks to characterize the common part of these various understandings should reveal the target’s hidden ground truth. Furthermore, we propose a novel method named multiple self-supervision conceptual consistency domain adaptation(MSCC). Experiments and analysis on benchmark datasets show the effectiveness of our idea and method.

关键词： deep learning transfer learning domain adaptation domain shift self-supervised

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Deep forest

引用

national Science Review 2019年第1期6卷 74-86页

作者： Zhi-Hua Zhou Ji Feng national key laboratory for novel software technology Nanjing University

Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting machines,we conjecture that the success of deep neural networks owes much to three characteristics, ***-by-layer processing, in-model feature transformation and sufficient model complexity. On one hand,our conjecture may offer inspiration for theoretical understanding of deep learning; on the other hand, to verify the conjecture, we propose an approach that generates deep forest holding these characteristics. This is a decision-tree ensemble approach, with fewer hyper-parameters than deep neural networks, and its model complexity can be automatically determined in a data-dependent way. Experiments show that its performance is quite robust to hyper-parameter settings, such that in most cases, even across different data from different domains, it is able to achieve excellent performance by using the same default setting. This study opens the door to deep learning based on non-differentiable modules without gradient-based adjustment, and exhibits the possibility of constructing deep models without backpropagation.

关键词： deep forest deep learning machine learning ensemble methods decision trees

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Open-environment machine learning

引用

national Science Review 2022年第8期9卷 211-221页

作者： Zhi-Hua Zhou national key laboratory for novel software technology Nanjing University

Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning in this article, are present to the ***, it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research,focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions and varied learning objectives, and discusses some theoretical issues.

关键词： machine learning artificial intelligence open-environment machine learning open ML

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：