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(AuAg)_(44)(SPhtBu)_(26) versus(AuAg)_(44)(SPhF_(2))_(30):Tailoring the Geometric Structures and Optical Properties of Nanocluster Analogues

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Precision Chemistry 2023年第3期1卷 139-145页

作者： Jiaojiao Han Hao Li Honglei Shen Ying Xu Xuejuan Zou Xi Kang Manzhou Zhu Department of Chemistry and Centre for Atomic Engineering of Advanced Materials Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information Technology and Anhui Province Key Laboratory of Chemistry for Inorganic/Organic Hybrid Functionalized MaterialsAnhui UniversityHefeiAnhui 230601China

Monolayer-protected nanoclusters are ideal models for understanding the correlations between structures and properties of inorganic nanoparticles,especially for those molecules with identical sizes but comparable ***,... 详细信息

关键词： metal nanoclusters alloy structural analogues geometric structures optical properties

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Laplacian Welsch Regularization for Robust Semisupervised Learning

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IEEE Transactions on Cybernetics 2022年第1期52卷 164-177页

作者： Ke, Jingchen Gong, Chen Liu, Tongliang Zhao, Lin Yang, Jian Tao, Dacheng PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China UBTECH Sydney Artificial Intelligence Centre University of Sydney DarlingtonNSW Australia

Semisupervised learning (SSL) has been widely used in numerous practical applications where the labeled training examples are inadequate while the unlabeled examples are abundant. Due to the scarcity of labeled examples, the performances of the existing SSL methods are often affected by the outliers in the labeled data, leading to the imperfect trained classifier. To enhance the robustness of SSL methods to the outliers, this article proposes a novel SSL algorithm called Laplacian Welsch regularization (LapWR). Specifically, apart from the conventional Laplacian regularizer, we also introduce a bounded, smooth, and nonconvex Welsch loss which can suppress the adverse effect brought by the labeled outliers. To handle the model nonconvexity caused by the Welsch loss, an iterative half-quadratic (HQ) optimization algorithm is adopted in which each subproblem has an ideal closed-form solution. To handle the large datasets, we further propose an accelerated model by utilizing the Nyström method to reduce the computational complexity of LapWR. Theoretically, the generalization bound of LapWR is derived based on analyzing its Rademacher complexity, which suggests that our proposed algorithm is guaranteed to obtain satisfactory performance. By comparing LapWR with the existing representative SSL algorithms on various benchmark and real-world datasets, we experimentally found that LapWR performs robustly to outliers and is able to consistently achieve the top-level results. © 2013 IEEE.

关键词： Large dataset

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A data-assisted adaptive detection algorithm for shortwave burst signals 5

A data-assisted adaptive detection algorithm for shortwave b...

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5th International Conference on Advanced Electronic Materials, Computers and Software engineering, AEMCSE 2022

作者： Yi Wei, Feng Wen Zhong, Qu Yan, Zhao Yu Mei, Zhou Pla Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

For shortwave signals with unique code frame structure, a data-assisted adaptive burst detection algorithm is proposed in this paper. The algorithm weakens the high bottom-noise undulation of signal correlation values due to shortwave channel fading by using a locally normalized sliding differential correlation. Also based on this, an adaptive gate limit detection strategy based on sliding window comparison judgments is used to improve the burst detection accuracy. This paper focuses on the effects of signal-to-noise ratio and shortwave channel environment on the performance of this algorithm, and simulation experiments are conducted under different conditions. The simulation results show that the algorithm has good robustness and can still achieve good detection results in the case of low signal-to-noise ratio as well as short-wave poor channels. © 2022 IEEE.

关键词： Fading channels

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Learning to Update Engine Models with Deep Reinforcement Learning 6

Learning to Update Engine Models with Deep Reinforcement Lea...

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6th International Symposium on Autonomous systems, ISAS 2023

作者： Yu, RuoYan Li, Bei Shi, SuMu Peng, Cong Zhou, YuNan Du, XiangYu Nanjing University of Aeronautics and Astronautics School of Automation Nanjing China Nanjing Engineering Institute of Aircraft Systems Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing China

ISBN: (纸本)9798350316155

Model updating can reduce the reality gap between the digital space and physical space, which is essential for accurately building digital models of aviation equipment. However, with data requirements and the complexity of application environments continue to increase, traditional update techniques show great limitations. The main reason is that traditional update technologies are limited by computational efficiency, resulting in the inability to balance accuracy and real-time. In this work, we propose a model update method based on reinforcement learning to infer model parameters. The proposed method does not require any ground truth parameters. Experimental results on the twin-rotor turbofan engine model verify the superiority of the proposed method compared with other state-of-the-art methods. © 2023 IEEE.

关键词： Reinforcement learning

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A Robust Camera-LiDAR Fusion Framework for 3D Object Detection in High-Dust Environments 22

A Robust Camera-LiDAR Fusion Framework for 3D Object Detecti...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Mingyuan Liu, Wentao Zhou, Bin Wang, Zhangyu Liu, Runsen Wang, Hanyu School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China Sate 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 Vehickes Ministry of Industry and Information Technology Beihang University Beijing China Technology and Development Center of Hefei Metro Research Institute Co. Ltd. Hefei Metro Research Institute Co. Ltd Hefei China

ISBN: (纸本)9798331527471

The fusion of camera and LiDAR features in the Bird's Eye View (BEV) perspective has become a prevalent solution for 3D detection in autonomous driving due to its simplicity and efficiency. However, in challenging environments like mining areas with dusty roads, existing BEV-based fusion networks struggle due to dust occlusion and misidentification of dust as obstacles. To address this, we propose a robust fusion framework that integrates fine-grained depth supervision and channel-wise attention mechanisms. Combined with a temporal multi-frame mechanism, our framework effectively mitigates issues caused by dust occlusion and misidentification. We validated our method's detection accuracy on a self-constructed mining road dataset, achieving 89.6% mAP, surpassing BEVFusion's 88.3% mAP. Tests on sensor occlusion and failure further demonstrated its robustness in adverse conditions typical of unstructured road scenarios. © 2024 IEEE.

关键词： Mine dust

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Confidence-Driven Semi-Supervised Partial Label Learning

Confidence-Driven Semi-Supervised Partial Label Learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Chengkun Liu Jun Zhang Jing Chai School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming City China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Semi-supervised Partial Label Learning (SPLL) aims to learn from a dataset comprised of both partial label examples each of which is associated with a candidate label set and unlabeled examples. The mainstream of SPLL methods usually construct a confidence matrix for training examples and by which to operate label disambiguation and classifier training. However, they treat examples with different confidence levels in the same strategy during training, which might result in degenerated learning performance. In this work, a novel method named COnfidence-DRiven (CODR) is proposed to deal with the above drawback. In specific, we iteratively update the confidence matrix and predictive network, and employ different strategies to deal with high-confidence and remaining low-confidence examples. Extensive experiments on real-world datasets demonstrate the superiority of CODR in classification accuracy compared with several other state-of-the-art methods.

关键词： Training Measurement Accuracy Sensitivity analysis Noise Neural networks

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First demonstration of an 8-node Mbps quantum access network based on passive optical distribution network facilities

First demonstration of an 8-node Mbps quantum access network...

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Frontiers in Optics + Laser science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser science 2023

作者： Bian, Yiming Pan, Yan Ma, Li Wang, Heng Dou, Jiayi Pi, Yaodi Yang, Jie Li, Yang Huang, Wei Yu, Song Zhang, Yichen Xu, Bingjie State Key Laboratory of Information Photonics and Optical Communications School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China Science and Technology on Communication Security Laboratory Institute of Southwestern Communication Chengdu610041 China

Quantum access network with 8 nodes and a secret key rate of over 6 Mbps per user is firstly demonstrated using passive optical distribution network facilities, paving the way of high-performance and scalable quantum-... 详细信息

ISBN: (纸本)9781957171296

关键词： Frequency division multiplexing Optical networks Passive optical networks Quantum key distribution Signal processing Wavelength division multiplexing

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In-situ preparation of Cu-BTC modified with organic amines for H_(2)S removal under ambient conditions

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Resources Chemicals and Materials 2023年第2期2卷 143-150页

作者： Zhelin Su Mingxian Duan Yeshuang Wang Huiling Fan Zhen Tian Zhaohui Chen Chao Yang Ju Shangguan State Key Laboratory of Clean and Efficient Coal Utilization Co-founded by Shanxi Province and the Ministry of Science and TechnologyTaiyuan University of TechnologyWest Yingze Street Number 79Taiyuan030024China School of Resources and Environmental Engineering Shanghai Polytechnic UniversityShanghai 201209China State Key Laboratory of Multi-phase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China College of Environmental Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China

Amine modification is an effective strategy to improve the H_(2)S removal performance of *** order to avoid the problem of pore blockage after amine modification in post synthesis impregnation,herein a series of Cu-BTC modified with organic amine adsorbents were prepared via an in-situ one-pot method and the H_(2)S removal performance under ambient conditions was *** is found that the introduced organic amines,depending on the types of amine selected,have a significant influence on the growth of Cu-BTC and its textural *** H_(2)S removal performance on the as-prepared materials suggested that the amine modified samples remarkably improved the H_(2)S removal capacities with an order of BA-Cu-BTC>TEA-Cu-BTC>TEOA-Cu-BTC>*** the enlarged surface area and the increased mesopores volumes,BA has the advantages of smaller steric hindrance and-NH_(2)groups,among which the former increased the accessibility of Cu active sites while the latter acted as additional active sites for H_(2)S capturing,thus affording BA-Cu-BTC highest breakthrough capacity of 77.3 mg S/***,this study elaborates the effect of organic amines in-situ modification on the Cu-BTC structure and desulfurization.

关键词： In-situ preparation Organic amines Cu-BTC H_(2)S adsorption

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A Novel Multi-Scale Network based on Class Attention for Diabetes Retinopathy 9

A Novel Multi-Scale Network based on Class Attention for Dia...

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9th IEEE Smart World Congress, SWC 2023

作者： Chen, HongYu Wu, RongHua Tao, Chen Xu, WenJing Yu, Hui Liu, HongZhe Xu, Cheng Jian, MuWei Linyi University School of Information Science and Technology Linyi China University of Portsmouth School of Creative Technologies Portsmouth United Kingdom Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China Shandong University of Finance and Economics School of Computer Science and Technology Jinan China

ISBN: (纸本)9798350319804

Diabetes Retinopathy (DR) is a common eye disease, which brings irreversible blindness risk to patients in severe cases. Due to the scarcity of professional ophthalmologists, developing computer-aided diagnostic systems to participate in DR grading diagnosis has become increasingly important. However, the current mainstream deep learning methods still cannot accurately classify the severity of DR, and their unreliable results are difficult to serve as a reference for clinicians. To tackle this problem, we propose two novel modules to improve the accuracy of DR classification. Specifically, we designed a multi-scale feature extraction module (MFEM) to capture tiny lesions in fundus images and differentiate similar lesions simultaneously. In addition, we also created a class attention module (CAM) to alleviate the adverse impact of intra-class similarity on DR grading. Experiment on the APTOS2019 blind detection dataset show that our proposed two modules have made significant improvements to the designed model, achieving state-of-the-art performance with 95.98% for ACC and 97.12% for QWK, respectively. © 2023 IEEE.

关键词： Attention mechanism DR grading Fundus images Multi-scale

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