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New hollow nanorods of C-Na2Ti3O7 derived from MAX phase for highly efficient methylene blue removal

Ceramics International 2025年第17期51卷 24089-24099页

作者： Xu, Yanjie Liu, Yating Zhang, Lan Gao, Yahui Wu, Chunlai Miao, Yalei Li, Na Pang, Xianjuan Dong, Feiyun Ren, Kemei Zheng, Bainan Tian, Wenjie School of Environmental Engineering and Chemistry Luoyang Institute of Science and Technology Luoyang471023 China National United Engineering Laboratory for Advanced Bearing Tribology Henan University of Science and Technology Luoyang471023 China Henan Key Laboratory of Green Building Materials Manufacturing and Intelligent Equipment Luoyang471023 China

Adsorption is one of the effective strategies to treat organic pollution in wastewater. Herein, a novel hollow nanorod of C-Na2Ti3O7 (CNTO) were fabricated from the MAX phase Ti2AlC by a facile method. The CNTO nanorods own a unique hollow structure and abundant -OH and Ti-O groups. Particularly, the as-prepared CNTO sample possesses a large specific surface area of 238.49 m2/g, which is 100 times larger than that of bulk Ti2AlC (2.11 m2/g). In MB removal, CNTO nanorods perform an excellent property with 97 % removal efficiency in 5 min and a maximal adsorption capacity of 159.1 mg/g. This ultrafast removal ability can attribute to the large specific surface area, negative zeta potential and hollow nanorod structure of CNTO samples. Kinetic experiments implied that the adsorption process conforms to the pseudo second-order kinetic model, and the adsorption form on CNTO is consistent with the Langmuir adsorption model. The mechanism studies revealed that CNTO samples adsorbed MB mainly by electrostatic adsorption and hydrogen bond adsorption. Furthermore, CNTO samples possess high structural stabilities and excellent recycling capacities in MB adsorption. This research highlights the great prospect of MAX phase derived CNTO nanorods for treating wastewater and pollutants. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Wastewater treatment

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An anomaly detection method for spacecraft solar arrays based on the ILS-SVM model

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Journal of Systems engineering and Electronics 2023年第2期34卷 515-529页

作者： WANG Yu ZHANG Tao HUI Jianjiang LIU Yajie College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology Changsha 410073China Beijing Institute of Tracking and Telecommunication Technology Beijing 100094China

Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit *** a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage *** damage makes solar array power generation unable to fully meet the energy demand of a ***,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of *** this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar *** use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.

关键词： spacecraft solar array anomaly detection integrated least squares support vector machine(ILS-SVM) induced ordered weighted average(IOWA)operator integrated model

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Classification of Pigmented Skin Lesions using Early Fusion of Multi-Deep Features and Support Vector Machine

Classification of Pigmented Skin Lesions using Early Fusion ...

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2024 International Conference on intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Chowdam, Jayanth Panigrahi, Sibarama National Institute of Technology Intelligent Data Analytics Laboratory Department of Computer Science and Engineering Odisha Rourkela India

ISBN: (纸本)9798350369175

One of the primary organ is the skin. It protects and separates humans from various dangers. Nevertheless, the skin is susceptible to damage and can develop pigmented lesions. Manual classification of Pigmented Skin Lesions(PSL) is a major challenge due to availability and bias of physician, time consuming, invasive methods are painful and with elevated expenses. To overcome this, in this paper, two automated computer vision based approaches (using and without using dimensionality reduction) are employed by modelling multi-deep features using Support Vector Machine(SVM) for classification of PSL. The suggested system comprises of two operational stages. Initially, the performance of six Convolutional Neural Network models (VGG16, VGG19, AlexNet, Xception, ResNet50, and EfficientB3) in deep feature extraction and three types of hand-crafted features extraction methods (HOG, LBP, and SIFT) employing SVM are evaluated. The top three CNN models (AlexNet, ResNet50, and EfficientB3) based on accuracy are used to form binary and ternary early fusion hybrid models to classify the skin lesions. In the second stage, combining multiple CNN models resulted in increased dimensionality and number of features. These features are then classified using SVM once in the higher dimension and again by reducing the dimensionality by applying Principal Component Analysis(PCA). The accuracies of the combined models are then compared. It is observed that the ternary early fusion model with SVM (Alexnet+Resnet50+EfficientB3-SVM) produces the highest accuracy of 82.22% with higher number of features whereas the same model while using PCA produces the accuracy of 81.57%. Consequently, the findings indicate the suggested techniques are useful for classification of PSL. © 2024 IEEE.

关键词： Dimensionality reduction

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Diversified Critical-Scenario-Search for Defect Detection of Autonomous Driving System

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-15页

作者： Xie, Yizhou Zhang, Yong Yin, Chengliang Huang, Hongcheng Dai, Kunpeng National Engineering Laboratory for Automotive Electronic Control Technology Shanghai Jiao Tong University Shanghai China Shanghai Intelligent and Connected Vehicle R&D Center Shanghai China

Scenario-based test is recognized as an effective way to evaluate and verify the Safety of the Intended Functionality (SOTIF) of an autonomous driving system, and a critical-scenario is beneficial to foreground the system defect more obviously. However, for the state of the art, time consumption of critical-scenario-search in a real-time test is still high, especially for searching critical-scenarios with dissimilarities, which assists designers in discovering more failure modes of autonomous in limited time. To address above challenges, in this paper, we improve our previous scenario-searching approach considering dissimilarity assessments in iterations, we present a searching framework which combines control and exploration approaches, and implement the efficient evolution of scenarios with a backward target-tree search. We carry out a Vehicle-in-the-Loop (VIL) test with a real vehicle to verify our new approach, and the results show that the approach is able to search for scenarios directly against a black-box system in a real-time test, and achieve scenarios with both high criticality and dissimilarity, including unknown-unsafe cases. In our time-limited test, compared with different methods, our approach can achieve 1.9 times the amount of diversified scenarios with the almost same (slightly higher) criticality, either achieve 1.5 times the amount attached with 75% improvement in criticality. IEEE

关键词： Real time systems

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Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task learning

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Solar evaporation for simultaneous oil-water separation and electricity generation with Janus wood-based absorbers

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Frontiers of Environmental Science & engineering 2024年第2期18卷 13-23页

作者： Yue Yang Ze Fu Qi Zhang Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse School of Environmental and Biological EngineeringNanjing University of Science&TechnologyNanjing 210094China Key Laboratory of Industrial Ecology and Environmental Engineering(Ministry of Education) School of Environmental Science and TechnologyDalian University of TechnologyDalian 116024China National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesKey Laboratory of Intelligent Optical Sensing and Manipulation(Ministry of Education)Jiangsu Key Laboratory of Artificial Functional MaterialsNanjing UniversityNanjing 210093China

Oily wastewater from ocean oil spills endangers marine ecosystems and human health. Therefore, developing an effective and sustainable solution for separating oil-water mixtures is urgent. Interfacial solar photothermal evaporation is a promising approach for the complete separation of two-phase mixtures using only solar energy. Herein, we report a carbonized wood-based absorber with Janus structure of comprising a hydrophobic top-layer and an oleophobic bottom-layer for simultaneous solar-driven oil-water separation and electricity generation. Under sunlight irradiation, the rapid evaporation of seawater will induce a separation of oil-water mixtures, and cause a high salt concentration region underlying the interface, while the bottom “bulk water” maintains in a low salt concentration, thus forming a salinity gradient. Electricity can be generated by salinity gradient power. Therefore, oil-water separation efficiency of > 99% and derived extra electricity power of ~0.1 W/m2 is achieved under solar radiation, demonstrating the feasibility of oil-water separation and electricity production synchronously directly using solar energy. This work provides a green and cost-effective path for the separation of oil-water mixtures.

关键词： Oily wastewater Carbonized wood Salinity gradient Electricity generation Solar irradiation

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Graphene-confined ultrafast radiant heating for high-loading subnanometer metal cluster catalysts

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national Science Review 2023年第6期10卷 191-199页

作者： Ye-Chuang Han Jun Yi Beibei Pang Ning Wang Xu-Cheng Li Tao Yao Kostya S.Novoselov Zhong-Qun Tian State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Graphene Industry and Engineering Research Institute School of Electronic Science and Engineering Xiamen University Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) National Synchrotron Radiation Laboratory University of Science and Technology of China Faculty of Environment and Life Beijing University of Technology Institute for Functional Intelligent Materials National University of Singapore

Thermally activated ultrafast diffusion,collision and combination of metal atoms comprise the fundamental processes of synthesizing burgeoning subnanometer metal clusters for diverse ***,so far,no method has allowed the kinetically controllable synthesis of subnanometer metal clusters without compromising metal ***,we have developed,for the first time,a graphene-confined ultrafast radiant heating(GCURH) method for the synthesis of high-loading metal cluster catalysts in microseconds,where the impermeable and flexible graphene acts as a diffusion-constrained nanoreactor for high-temperature *** from graphene-mediated ultrafast and efficient laser-to-thermal conversion,the GCURH method is capable of providing a record-high heating and cooling rate of～10～9℃/s and a peak temperature above 2000 ℃,and the diffusion of thermally activated atoms is spatially limited within the confinement of the graphene *** a result,due to the kinetics-dominant and diffusion-constrained condition provided by GCURH,subnanometer Co cluster catalysts with high metal loading up to 27.1 wt% have been synthesized by pyrolyzing a Co-based metal-organic framework(MOF)in microseconds,representing one of the highest size-loading combinations and the quickest rate for MOF pyrolysis in the reported *** obtained Co cluster catalyst not only exhibits an extraordinary activity similar to that of most modern multicomponent noble metal counterparts in the electro catalytic oxygen evolution reaction,but is also highly convenient for catalyst recycling and refining due to its single metal *** a novel GCURH technique paves the way for the kinetically regulated,limited diffusion distance of thermally activated atoms,which in turn provides enormous opportunities for the development of sophisticated and environmentally sustainable metal cluster catalysts.

关键词： photothermal conversion ultrafast radiant heating graphene-confined high-loading metal clusters solid catalysts

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Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

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Journal of Systems engineering and Electronics 2024年第2期35卷 495-508页

作者： YANG Luyi LI Haiyang ZHANG Jin ZHU Yuehe College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China China Astronauts Research and Training Center Beijing 100094China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Changsha 410073China

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

关键词： manned lunar mission free return orbit reachable domain(RD) deep neural network computation efficiency

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Robust Multi-UAV Placement Optimization for AOA-Based Cooperative Localization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-15页

作者： Zhou, Lingyun Ning, Xinzhi You, Ming-Yi Zhang, Rongqing Shi, Qingjiang Department of Hubei Key Laboratory of Intelligent Wireless Communications Hubei Engineering Research Center of Intelligent Internet of Things Technology College of Electronics and Information Engineering South-Central Minzu University Wuhan China School of Software Engineering Tongji University Shanghai China National Key Laboratory of Electromagnetic Space Security No.36 Research Institute of CETC Jiaxing China

Unmanned aerial vehicle (UAV) swarm-based localization technology has become increasingly popular due to its exceptional maneuverability, versatile coverage capabilities, and reliable line-of-sight (LoS) connectivity. However, the accuracy improvement by exploiting UAVs' placement poses a new challenge on multi-UAV localization system. This paper investigates the placement optimization problem for a multi-UAV localization network, where each UAV is equipped with a uniform circular array (UCA) to locate the target by adopting the angle-ofarrival (AOA) measurement. To measure the accuracy of the UCA-based localization system, we characterize the measurement error to be dependent on target-to-UAV distance and present a comprehensive derivation of the Cramer-Rao lower bound ´ (CRLB). Next, we formulate the problem as a non-convex semi-infinite optimization, aiming to minimize the worst-case squared position error bound (SPEB) by designing the robust placement strategy under some stringent geometric position constraints. We address this problem by alternatively solving two sub-problems, corresponding to a slave problem for calculating the worstcase SPEB of the uncertainty region under a fixed deployment of UAVs, as well as a master problem for optimizing UAVs' placement. By virtue of the particular problem structure, we develop the gradient projection-based method for the slave problem. Meanwhile, considering the lack of a closed expression for the maximum SPEB concerning UAVs' deployment in the master problem, we devise an iterative algorithm to acquire an efficient solution with the aid of Gibbs Sampling approach. Extensive simulation results demonstrate the efficacy of the proposed algorithms. IEEE

关键词： Unmanned aerial vehicles (UAV)

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Failure evolution and disaster prediction of rock under uniaxial compression based on non-extensive statistical analysis of electric potential

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International Journal of Mining Science and technology 2024年第7期34卷 975-993页

作者： Tiancheng Shan Zhonghui Li Haishan Jia Enyuan Wang Xiaoran Wang Yue Niu Xin Zhang Dong Chen Shan Yin Quancong Zhang State Key Laboratory of Coal Mine Disaster Prevention and Control China University of Mining and TechnologyXuzhou 221116China National Engineering Research Center for Coal Gas Control China University of Mining and TechnologyXuzhou 221116China School of Safety Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyXuzhou 221116China

Rock failure can cause serious geological disasters,and the non-extensive statistical features of electric potential(EP)are expected to provide valuable information for disaster *** this paper,the uniaxial compression experiments with EP monitoring were carried out on fine sandstone,marble and granite samples under four displacement *** Tsallis entropy q value of EPs is used to analyze the selforganization evolution of rock *** the influence of displacement rate and rock type on q value are explored by mineral structure and fracture modes.A self-organized critical prediction method with q value is *** results show that the probability density function(PDF)of EPs follows the q-Gaussian *** displacement rate is positively correlated with q *** the displacement rate increasing,the fracture mode changes,the damage degree intensifies,and the microcrack network becomes *** influence of rock type on q value is related to the burst intensity of energy release and the crack fracture *** q value of EPs can be used as an effective prediction index for rock failure like b value of acoustic emission(AE).The results provide useful reference and method for the monitoring and early warning of geological disasters.

关键词： Electric potential Non-extensive statistical feature Displacement rate q-Gaussian distribution Precursor prediction Rock materials

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