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Isolated FeN_(3)sites anchored hierarchical porous carbon nanoboxes for hydrazine-assisted rechargeable Zn-CO_(2)batteries with ultralow charge voltage

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Carbon Energy 2025年第1期7卷 121-132页

作者： Sanshuang Gao Hongyi Li Zhansheng Lu Songjie Meng Xue Zhao Xinzhong Wang Xijun Liu Guangzhi Hu Shenzhen Institute of Information Technology Shenzhen 518172China State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Guangxi Key Laboratory of Processing for Nonferrous Metals and Featured MaterialsSchool of ResourcesEnvironment and MaterialsGuangxi UniversityNanning 530004China School of Ecology and Environmental Science Institute for Ecological Research and Pollution Control of Plateau LakesYunnan UniversityKunming650504China State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources College of ChemistryXinjiang UniversityUrumqiChina School of Physics Henan Normal UniversityXinxiangChina Faculty of Chemistry and Chemical Engineering Yunnan Normal UniversityKunmingChina

Zn-CO_(2)batteries(ZCBs)are promising for CO_(2)conversion and electric energy ***,the ZCBs couple the electrochemical CO_(2)reduction(ECO_(2)R)with the oxygen evolution reaction and competitive hydrogen evolution reaction,which normally causes ultrahigh charge voltage and CO_(2)conversion efficiency attenuation,thereby resulting in~90%total power ***,isolated FeN_(3)sites encapsulated in hierarchical porous carbon nanoboxes(Fe-HPCN,derived from the thermal activation process of ferrocene and polydopamine-coated cubic ZIF-8)were proposed for hydrazine-assisted rechargeable ZCBs based on ECO_(2)R(discharging process:CO_(2)+2H+→CO+H_(2)O)and hydrazine oxidation reaction(HzOR,charging process:N_(2)H_(4)+4OH−→N_(2)+4H_(2)O+4e^(−)).The isolated FeN_(3)endows the HzOR with a lower overpotential and boosts the ECO_(2)R with a 96%CO Faraday efficiency(FECO).Benefitting from the bifunctional ECO_(2)R and HzOR catalytic activities,the homemade hydrazine-assisted rechargeable ZCBs assembled with the Fe-HPCN air cathode exhibited an ultralow charge voltage(decreasing by~1.84 V),excellent CO selectivity(FECO close to 100%),and high 89%energy *** situ infrared spectroscopy confirmed that Fe-HPCN can generate rate-determining*N_(2)and*CO intermediates during HzOR and ECO_(2)*** paper proposes FeN_(3)centers for bifunctional ECO_(2)R/HzOR performance and further presents the pioneering achievements of ECO_(2)R and HzOR for hydrazine-assisted rechargeable ZCBs.

关键词： electrochemical CO_(2)reduction Fe single atoms hierarchical porous carbon nanoboxes hydrazine-assisted Zn-CO_(2)batteries hydrazine oxidation reaction

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Stability Analysis of Load Frequency control System with Time-Varying Delays

Stability Analysis of Load Frequency Control System with Tim...

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2023 China Automation Congress, CAC 2023

作者： Fan, Chunling Chen, Chaoyang Zhou, Shaowu Lian, Honghai Chen, Zuguo Li, Pei School of Information and Electrical Engineering Hunan Provincial Key Laboratory of Intelligent Control and Maintenance of Complex Systems Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350303759

In this paper, the stability analysis of Load frequency control (LFC) systems with time-varying delay is conducted. Firstly, an augmented Lyapunov-Krasovskii (L-K) functional is designed to incorporate the relevant information of the delay state. Then, the functional derivatives are estimated using an auxiliary-function-based integral inequality in conjunction with an improved reciprocally convex inequality. Furthermore, the correlation function is processed through a quadratic function negative definite derivation. Finally, case studies established on a one-area LFC system are conducted to demonstrate the superiority of the method, and its accuracy is verified through a simulation case. © 2023 IEEE.

关键词： Electric control equipment

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Cluster routing algorithm based on agglomerative hierarchical clustering in wireless sensor networks

Cluster routing algorithm based on agglomerative hierarchica...

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2023 International Conference on Intelligent systems, Communications, and Computer Networks, ISCCN 2023

作者： Li, Hongbing Cui, Hao Ou, Jun Wang, Yuning Key Laboratory of Intelligent Information Processing Control of Chongqing Three Gorges University China Chongqing Key Laboratory of Geological Environment Monitoring Disaster Early Warning in Three Gorges Reservoir Area China Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center China

ISBN: (纸本)9781510666238

To save the energy and extend the lifetime of the network, Cluster Routing Algorithm based on Agglomerative Hierarchical Clustering (CRAAHC) is proposed in wireless sensor networks. WSN is divided into a certain quantity of ring areas based on the energy consumption model. Clustering process is optimized by constraining the distance between clusters and the diameter of clusters to improve the quality of clustering algorithm. Cluster head selection strategy is improved by considering the residual energy of the node and energy consumption in communication with other nodes in one cluster. Simulation results show that CRAAHC algorithm can achieve energy efficiency and routing reliability. © 2023 SPIE.

关键词： Clustering algorithms

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Semi-supervised Speech Lie Detection Algorithm Based on Multiple Features and Adaptive Threshold

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第6期52卷 2015-2021页

作者： Zheng, Shuailong Tao, Huawei Yu, Hang Xi, Ji Zhu, Chunhua Wang, Xuemei Key Laboratory for Grain Information Processing and Control Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou450001 China Computer Information Engineering Changzhou Institute of Technology Jiangsu213000 China

The limited number of labeled samples can significantly affect the accuracy of speech lie detection systems. However, semi-supervised algorithms have been shown to effectively improve detection performance under such constraints. To address this challenge, this paper proposes a semi-supervised speech lie detection algorithm that utilizes multiple features and adaptive thresholds. First, through comparative analysis, this paper identifies 312-dimensional acoustic features and 768-dimensional self-supervised features as the key elements of lie detection. Then, these acoustic features are processed using a bidirectional long short-term memory network (Bi-LSTM), which obtains comprehensive information by analyzing acoustic statistical features in both forward and backward directions. Subsequently, this paper develops a semi-supervised feature fusion module (BSFM) to extract deeper insights from basic features and achieves adaptive feature fusion through targeted training. In addition, based on a detailed analysis of the distribution characteristics of lie detection samples, this paper proposes an improved Freematch algorithm to better utilize unlabeled data. Experimental results show that the proposed algorithm outperforms most existing methods on the self-built H-Wolf dataset and the Columbia/SRI/Colorado Corpus (CSC). © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Semi-supervised learning

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition 48

A Generalized Subspace Distribution Adaptation Framework for...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Shaokai Song, Peng Ji, Liang Jin, Yun Zheng, Wenming Yantai University School of Computer and Control Engineering China Jiangsu Normal University School of Physics and Electronic Engineering China Research Center for Learning Science Southeast University Key Laboratory of Child Development and Learning Science Southeast University Ministry of Education China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9781728163277

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches. © 2023 IEEE.

关键词： Speech recognition

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Multi-Branch Spatial-Temporal Attention Graph Convolution Network for Skeleton-based Action Recognition 41

Multi-Branch Spatial-Temporal Attention Graph Convolution Ne...

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第41届中国控制会议

作者： Daoshuai Wang Dewei Li Yaonan Guan Gang Wang Haibin Shao Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Skeleton-based human action reco gnition has attracted considerable research interest due to its robustness to dynamic environments and complex *** based on graph neural networks have achieved great success in this ***,most of these graph neural network based methods adopt a single-branch or two-stream structure with limited input *** to the separation of space and time,many methods cannot pay attention to the critical information of human skeleton sequence,and multi-scale spatial-temporal dependent attention modeling becomes the key to *** paper proposes a novel multi-branch Spatio-temporal attention graph convolutional neural network(MB-STAGCN) to recognize human actions from skeleton *** proposed model adopts a multi-branch structure,fuses three-stream features,and then inputs them into the backbone network to extract features from multiple perspectives to a greater ***,we design a new temporal convolutional block for multi-scale extraction of information in the backbone *** also propose an attention block named Spatio-temporal Concat Attention(STCatAtt) to capture critical *** on benchmark datasets show remarkable performance for human action reco gnition,demonstrating the effectiveness of our method.

关键词： Skeleton-based human action recognition MB-STAGCN STCatAtt

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Learning Knowledge Evolution with Time Duration from Finer-Time-Granularity Temporal Knowledge Graph

Learning Knowledge Evolution with Time Duration from Finer-T...

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2023 China Automation Congress, CAC 2023

作者： Du, Shaochong Chen, Kang Yang, Shijie Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

How to represent the temporal information corresponding to each fact in temporal knowledge graphs (TKGs) effectively is always challenging. Most existing representation learning methods usually map the timelines of knowledges into multiple points and ignore the underlying continuous time property and periodic characteristics behind those dense timestamps in finer-time granularity temporal knowledge graphs. A novel Knowledge Evolution with Time Duration (KETD) model is proposed for knowledge representation of temporal knowledge graphs with finer-time granularities. It represents the entity embedding as a time-varying nonlinear function to find the specific continuous time property from dense timestamps. To the best of our knowledge, it is the first attempt to learn embeddings of time durations and combines them into embeddings of entities and relations to predict potential periodic facts. It also uses the temporal point process to capture the impact of historical facts on the current fact. The experiments on self-built finer-time granularity TKG of Satellite-to-Earth Communication (STEC) and two public datasets have demonstrated the superiority of KETD compared to some baseline approaches. © 2023 IEEE.

关键词： Knowledge graph

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An Integrated Scheduling Algorithm of Satellite Observation and Data Download Based on Multi-Agent Deep Reinforcement Learning

An Integrated Scheduling Algorithm of Satellite Observation ...

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2023 China Automation Congress, CAC 2023

作者： Chen, Kang Du, Shaochong Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China

ISBN: (纸本)9798350303759

The earth observation satellite scheduling has always been critical for the maximum use of limited satellite resources, which basically includes scheduling ground target observation and observation data downloading. Due to the complexity of the mathematical model and its NP-hardness, current research usually splits it into two problems to simplify the model. In this paper, we propose a model of the integrated scheduling problem of satellite observation and data downloading (ISP-SODD) based on the decentralized partially observable Markov decision process (Dec-POMDP), treating each satellite as an agent with autonomous decision-making capability. A multi-agent deep reinforcement learning-based (MADRL-based) algorithm is proposed to solve it. A series of simulation experiments of different scales validate the effectiveness of the method and its advantages compared with the traditional heuristic algorithms. To the best of our knowledge, this study is the first attempt to apply multi-agent reinforcement learning to a multi-satellite scheduling scenario with downloading considerations. © 2023 IEEE.

关键词： Reinforcement learning

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Day-ahead Chance-constrained Energy Management of Energy Hubs:A Distributionally Robust Approach

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CSEE Journal of Power and Energy systems 2022年第3期8卷 812-825页

作者： Jiaxin Cao Bo Yang Shanying Zhu Chao Ning Xinping Guan Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of Education of China Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853 USA

The day-ahead management schedules of hybrid energy hubs are intricate and usually exposed to various uncertainties with the penetration of renewable sources and different ***,it is difficult to access to precise probability distribution functions and exact moment information of uncertain *** cope with these issues,an energy management scheme based on the distributionally robust optimization approach is developed for the energy *** makes no assumptions of certain probability distributions and can be implemented with limited empirical data and partial information of underlying *** operational strategy can provide decision makers with a preliminary and robust optimal solution in the day-ahead *** results illustrate the economical benefit of the energy model,and the effectiveness of the proposed approach in chance-constrained energy management is demonstrated by comparing with other *** Terms-Chance constraint,distributionally robust optimization,energy hub,energy management.

关键词： Chance constraint distributionally robust optimization energy hub energy management

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Neural Network Based Electrical Dispersion Pre-Compensation for Intensity-modulation and Direct-detection systems

Neural Network Based Electrical Dispersion Pre-Compensation ...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Rong, Limin Chen, Zhiwei Yin, Mingzhu Tang, Xianfeng Li, Fan Sun Yat-Sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Guangzhou510275 China Zhuhai519000 China Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing100876 China

Based on deep neural network (DNN), intensity modulation and direct detection (IM/DD) system is demonstrated by simulation and 56-GBaud OOK signal is successfully transmitted over 80-km single mode fiber (SMF) with 3-... 详细信息

ISBN: (纸本)9781665462136

关键词： Chromatic dispersion

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