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Hyperspectral image super resolution using deep internal and self-supervised learning

CAAI Transactions on Intelligence technology 2024年第1期9卷 128-141页

作者： Zhe Liu Xian-Hua Han graduate school of science and technology for Innovation Yamaguchi UniversityYamaguchiJapan graduate school of Artificial Intelligence and science Rikkyo UniversityTokyoJapan

By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)image.With previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth data.Thus,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in reality.The learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse environments.To handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR problem.The authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS version.However,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction performance.To solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlabelled training samples.Specifically,the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for measuring the network modelling performance.By cons

关键词： computer vision deep learning deep neural networks hyperspectral image enhancement

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong school of Electrical Engineering and Computer science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)graduate school Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual devices.However,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural designs.Here,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer nanofilm.These nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission colors.Here,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible spectrum.Furthermore,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

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Modifying Lyapunov exponent of chaotic map by self-cascading

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science China Technological sciences 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and Information Engineering Nanjing University of Information Science & Technology school of Artificial Intelligence Nanjing University of Information Science & Technology school of Computer science and technology Harbin Institute of Technology Shenzhen Graduate School

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos maps.Additionally, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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Efficient flexible perovskite solar cells and modules using a stable SnO_(2)-nanocrystal isopropanol dispersion

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Nano Research 2024年第4期17卷 2704-2711页

作者： Zhiwei Su Jing Li Ruixuan Jiang Shujie Zhang Chengkai Jin Feng Ye Bingcan Ke Mengjun Zhou Jinhui Tong Hyesung Park Fuzhi Huang Yi-Bing Cheng Tongle Bu State Key Laboratory of Advanced technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China school of Material science and Engineering Wuhan University of TechnologyWuhan 430070China Department of Materials science and Engineering Graduate School of Semiconductor Materials and Devices EngineeringGraduate School of Carbon NeutralityLow Dimensional Carbon Materials CenterUlsan National Institute of Science and TechnologyUlsan 44919Republic of Korea Xianhu Laboratory of the Advanced Energy science and technology Guangdong Laboratory Foshan 528200China

The outstanding advantages of lightweight and flexibility enable flexible perovskite solar cells(PSCs)to have great application potential in mobile energy devices.Due to the low cost,low-temperature processibility,and high electron mobility,SnO_(2) nanocrystals have been widely employed as the electron transport layer in flexible PSCs.To prepare high-quality SnO_(2) layers,a monodispersed nanocrystal solution is normally used.However,the SnO_(2) nanocrystals can easily aggregate,especially after long periods of storage.Herein,we develop a green and cost-effective strategy for the synthesis of high-quality SnO_(2) nanocrystals at low temperatures by introducing small molecules of glycerol,obtaining a stable and well-dispersed SnO_(2)-nanocrystal isopropanol dispersion successfully.Due to the enhanced dispersity and super wettability of this alcohol-based SnO_(2)-nanocrystal solution,large-area smooth and dense SnO_(2) films are easily deposited on the plastic conductive substrate.Furthermore,this contributes to effective charge transfer and suppressed non-radiative recombination at the interface between the SnO_(2) and perovskite layers.As a result,a greatly enhanced power conversion efficiency(PCE)of 21.8%from 19.2%is achieved for small-area flexible PSCs.A large-area 5 cm×5 cm flexible perovskite solar mini-module with a champion PCE of 16.5%and good stability is also demonstrated via this glycerol-modified SnO_(2)-nanocrystal isopropanol dispersion approach.

关键词： tin oxide isopropanol dispersion colloid stability flexible perovskite solar cells

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Numerical insight into heat transfer enhancement by granules inner-migration in a moving bed:A performance comparison between slanted-stick and plow-shaped agitator

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Particuology 2024年第6期89卷 109-116页

作者： Binfan Jiang Hao Pei Dehong Xia Haiyang Wang school of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China Shunde graduate school of University of science and technology Beijing Guangdong 528399China Jianlong Group&USTB Joint Laboratory Beijing 100083China

Waste heat recovery of high-temperature granules is one of the most promising sustainable energy supply and carbon reduction ways for industry.A moving bed indirect heat exchanger(MBIHE)with inner-migration was proposed for granular heat recovery.Granule migration and the enhanced heat transfer induced by two types of agitators(i.e.,agitator with slanted stick A_(s)and with plow-shaped surface A_(p))in the MBIHE are analyzed based on DEM coupled with CFD.Owing to the effective agitation,the average heat transfer coefficient in the granule side is enhanced to∼3 times compared to that without agitation.The heat recovery efficiency in the moving bed reached more than 70%with the agitations of either A_(s)or A_(p).The heat efficiency of A_(s)is∼7%higher than that of A_(p),but with at least 60%greater rotational torque.To ensure reliable agitation,the A_(p)is suggested to be adopted in the MBIHE to induce granule migration.

关键词： Waste heat recovery High-temperature granule Granule migration Heat transfer enhancement Industry energy saving

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Fast Cross-Platform Binary Code Similarity Detection Framework Based on CFGs Taking Advantage of NLP and Inductive GNN

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Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU school of Computer science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Control flow graph Natural language processing Inductive graph neural network Binary code similarity detection

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Effect of strain on structure and electronic properties of monolayer C_(4)N_(4)

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Chinese Physics B 2024年第5期33卷 595-600页

作者：陈昊徐瑛赵家石周丹 school of Physics Changchun University of Science and TechnologyChangchun 130022China school of Computer science and technology Changchun University of Science and TechnologyChangchun 130022China

The first-principles calculations are performed to examine structural,mechanical,and electronic properties at large strain for a monolayer C_(4)N_(4),which has been predicted as an anchoring promising material to attenuate shuttle effect in Li–S batteries stemming from its large absorption energy and low diffusion energy barrier.Our results show that the ideal strengths of C_(4)N_(4)under tension and pure shear deformation conditions reach 13.9 GPa and 12.5 GPa when the strains are 0.07 and 0.28,respectively.The folded five-membered rings and diverse bonding modes between carbon and nitrogen atoms enhance the ability to resist plastic deformation of C_(4)N_(4).The orderly bond-rearranging behaviors under the weak tensile loading path along the[100]direction cause the impressive semiconductor–metal transition and inverse semiconductor–metal transition.The present results enrich the knowledge of the structure and electronic properties of C_(4)N_(4)under deformations and shed light on exploring other two-dimensional materials under diverse loading conditions.

关键词： two-dimensional materials strain effect structural evolution electronic properties

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Truly optimal semi-active damping to control free vibration of a single degree of freedom system

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Theoretical & Applied Mechanics Letters 2024年第2期14卷 142-145页

作者： Viet Duc La Ngoc Tuan Nguyen Institute of Mechanics Vietnam Academy of Science and TechnologyHanoi 10000Vietnam graduate University of science and technology Vietnam Academy of Science and TechnologyHanoi 10000Vietnam

This paper studies a single degree of freedom system under free vibration and controlled by a general semiactive damping.A general integral of squared error is considered as the performance index.A one-time switching damping controller is proposed and optimized.The pontryagin maximum principle is used to prove that no other form of semi-active damping can provide the better performance than the proposed one-time switching damping.

关键词： Analytical optimization Quadratic integral Lyapunov equation Pontryagin maximum principle

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Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

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Nuclear science and Techniques 2024年第3期35卷 216-227页

作者： Deng Liu Alam Noor A Zhen-Zhen Qin Yang Lei school of Mathematics and Physics Southwest University of Science and TechnologyMianyang 621010China school of Nuclear science and technology Southwest University of Science and TechnologyMianyang 621010China

The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output predictions.The quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the TBRE.However,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.

关键词： Neural network Two-body random ensemble Spin distribution of nuclear ground state

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Interface interaction between SiO_(2) and magnetite under high temperature:particle migration and inhibition mechanism

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Journal of Iron and Steel Research(International) 2024年第3期31卷 561-572页

作者： Yao-zu Wang Jian-liang Zhang Qiang Cheng Hui-qing Jiang Zheng-jian Liu Yu-bo Tan school of Intelligent science and technology University of Science and Technology BeijingBeijing 100083China school of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China school of Advanced Engineering University of Science and Technology BeijingBeijing 100083China

Silicon is one of the main gangue components in iron ore,usually in the form of quartz and olivine.Numerous studies have shown that SiO_(2) has a two-sided effect on the consolidation of pellets during high-temperature oxidation roasting of magnetite.However,it is very difficult to capture the structural evolution and migration mechanisms during high-tem-perature roasting process by existing experimental methods.Therefore,the influence of SiO_(2) on the consolidation behavior of magnetite was studied through a series of roasting experiments and molecular dynamic simulation.The results show that the consolidation index and particle growth index decrease with the increase in SiO_(2) content in the particles.At 1573 K,the liquid phase promotes the recrystallization growth of hematite at high temperature.Molecular dynamic study shows that it is difficult for quartz SiO_(2) to form sintering neck with Fe_(2)O_(3).When the calcination temperature is higher than 1400 K,the Fe_(2)O_(3)-Fe_(2)SiO_(4) system produces a considerable sintering neck structure after relaxation.The atomic migration ability of Fe_(2)SiO_(4) is much higher than that of Fe_(2)O_(3).The higher atomic migration ability of Fe_(2)SiO_(4) is the main reason for the formation of the sintering neck.

关键词： Sintering neck Crystal growth Consolidation Molecular dynamics SiO_(2)

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