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Organic stoichiometric cocrystals with a subtle balance of charge-transfer degree and molecular stacking towards high-efficiency NIR photothermal conversion

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Chinese Chemical Letters 2024年第10期35卷 180-186页

作者： Jieqiong Xu Wenbin Chen Shengkai Li Qian Chen Tao Wang Yadong Shi Shengyong Deng Mingde Li Peifa Wei Zhuo Chen Institutes of Physical Science and Information Technology Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationAnhui Graphene Engineering LaboratoryAnhui UniversityHefei 230601China Molecular Science and Biomedicine Laboratory(MBL).State Key Laboratory of Chemo/Biosensing and Chemometrics College of Chemistry and Chemical EngineeringCollege of BiologyAptamer Engineering Center of Hunan ProvinceHunan UniversityChangsha 410082China Department of Chemistry and Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province Shantou UniversityShantou515063China

Charge-transfer(CT)stoichiometric cocrystals are promising choice of organic materials for unveiling the structure-property ***,due to the contradiction between large CT degree required for strong NIR absorption and flexible molecular stacking,construction of stoichiomorphism-based cocystals with near-infrared(NIR)photothermal property remains ***,the first example of stoichiomorphism-based photothermal cocrystals were accomplished through the adaptive assembly of 3,3,5,5-tetramethylbenzidine(TMB)donor and 1,2,4,5-tetracyanobenzene(TCNB)*** selective cocrystallization could be controlled by varying the donor-acceptor stoichiometries via a surfactantassisted method,resulting in two cocrystals with 1:1(T1C1)and 1:2(T2C1)*** absorbance intensity of T1C1 at 808 nm was nearly twice that of T2C1,while the photothermal conversion efficiency(PCE)of the former was 60.3%±0.6%,approximately 80%of that for the latter(75.5%±2.6%),which might be caused by the different intermolecular interactions in distinct molecular stacking ***,both excellent PCEs of stoichiometric cocrystals were attributed to the nonradiative transition process,including internal conversion and charge dissociation processes,as elucidated by femtosecond transient absorption spectroscopy ***,T1C1 was used as an NIR heater for preparing agarose-based photothermal hydrogel,showing great potential for light-controlled in-situ *** strategy of balancing the CT degree and molecular packing orientation not only uncovered the relationship between stoichiometric stacking and photothermal property,but also provided an opportunity to develop advanced organic optoelectronic materials.

关键词： Charge-transfer cocrystal Stoichiometric stacking Crystal growth Photothermal conversion In-situ gelation

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Heterostructure:application of absorption-catalytic center in lithium-sulfur batteries

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Rare Metals 2024年第4期43卷 1461-1487页

作者： Fei Wang Chun-Man Yang Yong-Qi Wang Dan You Wen-Hao Yang Rong-Wei Huang Ji-Yue Hou Qian Wang Ying-Jie Zhang Ying Wang Yue-Jing Zeng Yi-Yong Zhang Xue Li National Local Joint Engineering Research Center for Lithium-Ion Batteries and Materials Preparation Technology Key Laboratory of Advanced Batteries Materials of Yunnan ProvinceFaculty of Metallurgical and Energy EngineeringKunming University of Science and TechnologyKunming650093China College of Electrical Information Engineering Panzhihua UniversityPanzhihua617000China State Key Laboratory of Physical Chemistry of Solid Surfaces Collaborative Innovation Centre of Chemistry for Energy MaterialsState-Province Joint Engineering Laboratory of Power Source Technology for New Energy VehicleEngineering Research Center of Electrochemical TechnologyMinistry of EducationCollege of Chemistry and Chemical EngineeringXiamen UniversityXiamen361005China

Due to the high theoretical specific capacity(1675 mAh·g^(-1)),low cost,and high safety of the sulfur cathodes,they are expected to be one of the most promising rivals for a new generation of energy storage ***,the shuttle effect,low conductivity of sulfur and its discharge products,volume expansion,and other factors hinder the commercialization of lithium-sulfur batteries(LSBs).The development of sulfur-fixing materials and the design of multifunctional materials to enhance the electrochemical performance of LSBs have been the main research priorities in recent *** of the advantages of high conductivity,built-in electric field,and good synergism,more and more researchers have employed heterostructure into sulfur-fixing materials to enhance the catalytic and absorption ability for *** this review,the principle of heterostructure and the mechanism of enhancing the performance of lithium-sulfur batteries are *** applications of heterostructure in cathode and interlayer of LSBs in the latest years are ***,the cutting-edge troubles and possibilities of heterostructures in LSBs are briefly presented.

关键词： Lithium-sulfur batteries(LSBs) Heterostructure Built-in electric field Catalytic Absorption

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Coexistence of Distributed Sensing and Ultra-simple Coherent RoF based Front-haul for Radio Access Network

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Journal of Lightwave technology 2025年第12期43卷 5669-5675页

作者： Liu, Maoqi Wang, Jingchuan Liu, Chen Yu, Changyuan Lu, Chao Huazhong University of Science and Technology National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Wuhan430074 China The Hong Kong Polytechnic University Photonics Research Institute Department of Electrical and Electronic Engineering Hong Kong

The radio access network requires high-speed transmission and multifunctionality to support the increasing demand for advanced communication services. Radio-over-fiber (RoF) technology fulfills these requirements by enabling the delivery of millimeter-wave (MMW) band wireless signals via optical fiber front-haul. This paper presents an ultra-simple RoF system that achieves the coexistence of distributed sensing and telecommunication. A commercially available coherent modulator achieves signal modulation and remote up-conversion by the beat between the residual carrier and the modulated radio signal. The linear-frequency modulated carrier enables distributed sensing while simultaneously serving as the beat tone of RoF communication, showcasing the potential for integrated sensing and communication (ISAC) applications in next-generation radio access networks. We demonstrate the sensing and transmission performance over a 10-km fiber link and a 1-m wireless MMW channel. The system achieves a distributed sensing detection sensitivity of 28 pΕ/√ Hz and supports 1.6 GBaud 16QAM radio transmission in the Ka-band of 28 GHz. © 1983-2012 IEEE.

关键词： Radio access networks

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Research on Hybrid Intelligence Wargame Method 5

Research on Hybrid Intelligence Wargame Method

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5th IEEE International Conference on Computer and Communication engineering technology, CCET 2022

作者： Jin, Xin Wang, Xinnian Ding, Ran Wu, Yunchao Science and Technology on Information System Engineering Laboratory Nanjing China

ISBN: (数字)9781665467353

ISBN: (纸本)9781665467353

Wargame, as a tool to generate sample data for analysis and model training, has vast application in fields of training, command & control, and tactical research. Traditional wargame technologies greatly rely on human wisdom in the loop, impossible to generate large scale sample data. Reinforcement learning technology can generate large scale sample data, but it is not competent for the decision complexity above campaign level. This paper proposes a hybrid intelligence wargame method, which can generate large scale sample data using AI algorithms under the guidance of human wisdom. It has wide applications, which provides data analysis functions that existing wargame methods cannot provide. Prototype software has been developed based on the method, with feasibility and effectiveness verified through experiments, which has certain reference value. © 2022 IEEE.

关键词： Reinforcement learning

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Facial expression recognition under occlusion conditions based on multi-feature cross-attention

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Journal of Intelligent and Fuzzy systems 2024年第4期46卷 9841-9856页

作者： Guo, Sheng Tan, Mian Cai, Shan Zhang, Zaijun Liang, Yihui Feng, Hongxi Zou, Xue Wang, Lin School of Data Science and Information Engineering Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China School of Mathematics and Statistics Qiannan Normal University for Nationalities Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Aerospace Tianma Electromechanical Technology Co LTD Zunyi China

Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. © 2024 – IOS Press.

关键词： Convolution

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A LiDAR-Camera Fusion Network for Small Object Detection in Open-pit Mining Areas 22

A LiDAR-Camera Fusion Network for Small Object Detection in ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Liu, Muzhuo Wang, Jie Zhou, Bin Wang, Zhangyu Liu, Wentao Wang, Mingyuan School of Transportation Science and Engineering Ministry of Industr and Information Technology Beihang University Beijing China State Key Lab of Intellgent Transportation System Ministry of Industr and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industr and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

As intelligent and autonomous driving technologies advance, the secure and productive functioning of mining trucks during autonomous operations is critically dependent on precise object detection within open-pit mining environments. Due to the sparse nature of point cloud data for small objects such as stones, cones, and equipment parts, they are susceptible to generating false negatives and false positives. Although camera images are rich in semantic content, the absence of precise depth cues renders them vulnerable to interference from background noise. Currently, the technology of fusing LiDAR point cloud and camera image data for 3D object detection is gaining popularity in this field. In this paper, we introduce a query feature generation strategy based on multi-scale image features in the cross-attention-based feature fusion module to generate high-quality fusion features for small objects. Additionally, within the traditional non-maximum suppression strategy, we introduce size factors and shape factors to alleviate the sensitivity of small objects to bounding box offsets. Notably, our network model has achieved an mAP of 88.0 and an NDS of 77.9 on the mining dataset, significantly enhancing the precision of detecting small objects over previous approaches. © 2024 IEEE.

关键词： Open pit mining

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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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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

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

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network 22

Prediction of Driving Departure of Mining Autonomous Transpo...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Lecong Yu, Guizhen Li, Han Xia, Qi Zhang, Xiangyu Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms. © 2024 IEEE.

关键词： Mine transportation

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Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

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Plant Phenomics 2023年第4期5卷 779-789页

作者： Jianqing Zhao Yucheng Cai Suwan Wang Jiawei Yan Xiaolei Qiu Xia Yao Yongchao Tian Yan Zhu Weixing Cao Xiaohu Zhang National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095China Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture and Rural AffairsNanjing 210095China Jiangsu Key Laboratory for Information Agriculture Nanjing 210095China Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing 210095China

Accurate wheat spike detection is crucial in wheat field phenotyping for precision *** in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat ***,wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the *** models for wheat spike detection are typically designed for a specific growth *** adaptability to other growth stages or field scenes is *** models cannot detect wheat spikes accurately caused by the difference in color,size,and morphological features between growth *** paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity *** constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection ***,a Detection Network is designed to improve wheat spike detection capability.A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery.A new micro-scale detection layer is added to the network to extract the features of small *** loss is improved by Complete Intersection over Union to reduce the impact of the *** results show that WheatNet can achieve greater accuracy than classical detection *** detection accuracy with average precision of spike detection at the filling stage is 90.1%,while it is 88.6%at the maturity *** suggests that WheatNet is a promising tool for detection of wheat spikes.

关键词： Wheat maturity wheat

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