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RGB-guided hyperspectral image super-resolution with deep progressive learning

CAAI Transactions on Intelligence Technology 2024年第3期9卷 679-694页

作者： Tao zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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Braille Character Segmentation Algorithm Based on Gaussian Diffusion

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Computers, Materials & Continua 2024年第4期79卷 1481-1496页

作者： Zezheng Meng Zefeng Cai Jie Feng Hanjie Ma Haixiang zhang Shaohua Li School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these methodsneed improvement in accuracy and generalizability, especially in densely dotted braille image environments. Thispaper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithmbased on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. Thisis applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining thecentral coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditionalpost-processing algorithms to recognize braille character information. Experimental results demonstrate that thisframework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille charactersin complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dotdetection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods,these represent improvements of 0.08% and 0.02., respectively.

关键词： Computer vision image recognition optical braille recognition

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An Elastoplastic Fracture Model Based on Bond-Based Peridynamics

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2349-2371页

作者： Liping Zu Yaxun Liu Haoran zhang Lisheng Liu Xin Lai Hai Mei Department of Engineering Structure and Mechanics Wuhan University of TechnologyWuhan430070China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics Wuhan University of TechnologyWuhan430070China

Fracture in ductile materials often occurs in conjunction with plastic ***,in the bond-based peridynamic(BB-PD)theory,the classic mechanical stress is not defined *** makes it difficult to describe plasticity directly using the classical plastic *** address the above issue,a unified bond-based peridynamics model was proposed as an effective tool to solve elastoplastic fracture *** to the existing models,the proposed model directly describes the elastoplastic theory at the bond level without the need for additional calculation *** results obtained in the context of this model are shown to be consistent with FEM results in regard to force-displacement curves,displacement fields,stress fields,and plastic deformation *** model exhibits good capability of capturing crack propagation in ductile material failure problems.

关键词： Ductile materials plastic deformation bond-based peridynamics isotropic hardening fracture analysis

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Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 226-238页

作者： Hui XIE Jianfang zhang Lijuan DING Tao TAN Qing LI Department of Radiation Oncology Affiliated Hospital(Clinical College)of Xiangnan UniversityChenzhou 423000China Faulty of Applied Sciences Macao Polytechnic UniversityMacao 999078China Department of Physical examination Beihu Centers for Disease Control and PreventionChenzhou 423000China Department of Radiation Oncology the Third People's Hospital of Chenzhou CityChenzhou 423000China School of Medical Imaging Laboratory Science and RehabilitationXiangnan UniversityChenzhou 423000China

Background The prognosis and survival of patients with lung cancer are likely to deteriorate with *** deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and *** In total,62. eligible patients were recruited from two medical *** deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological *** dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection *** machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final *** diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive *** and decision-curve analyses were also *** The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung *** performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.82.,and 0.776,*** validation set of the

关键词： Machine learning Deep transfer learning Evaluation Mediastinal lymph node lung cancer patie

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Callovian-Oxfordian sedimentary microfacies in the middle of Block B on the right bank of the Amu Darya Basin,Turkmenistan

Energy Geoscience

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Energy Geoscience 2024年第2期5卷 249-262页

作者： Chongyang Wu Chuanjie Cheng Liangjie zhang Bingsong Yu Hongjun Wang Petroleum Exploration and Development Research Institute SinopecBeijing100083China China University of Geosciences(Beijing) Beijing100083China Research Institute of Petroleum Exploration and Development PetroChinaBeijing100083China

The right bank of the Amu Darya Basin enjoys abundant natural gas resources,on which the Callovian-Oxfordian strata in the middle of Block B serve as the major horizons for natural gas ***,the characteristics and distribution patterns of the sedimentary microfacies in these strata are yet to be further *** on the analysis of data on drilling,logging,cores,and thin sections from 2. typical wells,as well as the regional sedimentary background,this study inferred that the middle of Block B evolved from the Callovian ramp platform into the Oxfordian rimmed ***,this study determined that the inner-ramp intertidal-subtidal shallow-water subfacies mainly developed during the Callovian and transitioned into the shallow shelf subfacies during the *** study identified eight sedimentary microfacies,namely reef knoll,reef-shoal complex,bioclastic shoal,psammitic shoal,bioherm,lime mud mound,intershoal(intermound),and static-water *** on research into the high-precision sequence-sedimentary microfacies framework,this study built a geological model for the development of sedimentary microfacies in the study *** to this geological model,the sedimentary microfacies in the study area are characterized by vertical alternation of reef-shoal complex,bioclastic(psammitic)shoal,bioherm,and intershoal ***,they show the development of reef knoll,reef-shoal complex,bioclastic(psammitic)shoal,and bioherm(or lime mud mound)laterally from west to east,with the physical properties of the reservoirs deteriorating from west to east *** microfacies of reef-shoal complex and the bioclastic(psammitic)shoal predominate in the study area,and their deposition and development are controlled by sequence boundaries and are also affected by *** Oxfordian reef-shoal complexes were largely inherited from the Callovian uplifts and show lateral seaward progradation.

关键词： Carbonate rock Sedimentary microfacies Evolutionary pattern Jurassic Amu Darya Basin Turkmenistan

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Low-complexity model predictive control of a four-level active neutral point clamped inverter without weighting factors

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Transportation Safety and Environment 2024年第2期6卷 96-103页

作者： Chaoqun Xiang Ziyin Fan Songyang Jiang Xinan zhang Shu Cheng School of Traffic and Transportation Engineering Central South UniversityChangsha 410075HunanChina CRRC Dalian Electric Traction R&D Center Co. LtdDalian 116200LiaoningChina School of Engineering The University of Western AustraliaPerthWA 6009Australia

The four-level active neutral point clamped(ANPC)inverter has become increasingly widely used in the renewable energy indus-try since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC *** model predictive current control(MPCC)is a promising control method for multi-level ***,the conven-tional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications.A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this *** computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage *** proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage *** efficacy of the proposed MPCC is verified by simulation and experimental results.

关键词： four-level active neutral point clamped(ANPC)inverter model predictive current control(MPCC) low complexity without weighting factors

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Quantitative identification and prediction of mixed lithology, Bohai Sea, China

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Energy Geoscience 2024年第3期5卷 212-220页

作者： Shaopeng Wang Longtao Cui Li'an zhang Chao Ma Hebing Tang Tianjin Branch of CNOOC Ltd. Tianjin 300459China

The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.

关键词： Mixed carbonate-siliciclastics Waveform clustering Quantitative identification of lithology Bohai Bay Basin

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The Correlation between Nutrition and Transport Mechanism under Abiotic Stress in Plants: A Comprehensive Review

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Phyton-International Journal of Experimental Botany 2024年第6期93卷 1325-1344页

作者： Muhammad Saleem Jianhua zhang Muhammad Qasim Rashid Iqbal Li Song Joint International Research Laboratory of Agriculture and Agri-Product Safety The Ministry of Education of ChinaJiangsu Key Laboratory of Crop Genomics and Molecular BreedingYangzhou UniversityYangzhou225009China Microelement Research Center College of Resources and EnvironmentHuazhong Agricultural UniversityWuhan430070China Department of Agronomy Faculty of Agriculture and EnvironmentThe Islamia University of BahawalpurBahawalpur63100Pakistan

Variations in the nutrients and water that plants require for metabolism,development,and the maintenance of cellular homeostasis are the main causes of abiotic stress in *** has,however,hardly ever been studied how these transporter proteins,such as aquaporin which is responsible for food and water intake in cell plasma mem-branes,interact with one *** review aims to explore the interactions between nutrient transporters and aquaporins during water and nutrient *** also investigates how symbiotic relationships inﬂuence the plant genome’s responses to regulatory processes such as photoperiodism,senescence,and nitrogenﬁ*** responses are observed in reaction to various abiotic *** instance,plasma membrane transporters are upregulated during macronutrient insufﬁciency,tonoplast transporters are overexpressed,and aquaporins are downregulated in micronutrient deﬁ***,tolerant plants often exhibit increased expression of nutrient transporters and aquaporins in response to drought,salt,and cold *** better comprehend plant stress tolerance to abiotic challenges including starvation,K famine,salt,and freezing temperatures,both classes of nutrient and water transporters should be considered at the same time.

关键词： Nutrients abiotic stress aquaporin transporters

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Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning

Energy and AI

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Energy and AI 2024年第3期17卷 295-312页

作者： Xuanang zhang Xuan Wang Ping Yuan Hua Tian Gequn Shu State Key Laboratory of Engines Tianjin UniversityNo.92Weijin RoadNankai DistrictTianjin300072China Department of Thermal Science and Energy Engineering University of Science and Technology of ChinaHefei230027China

Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the organic Rankine cycle further enhances engine efficiency and provides effective thermal management. However, the powertrain greatly increases the complexity of energy management system. In order to design an energy management system with high efficiency and robustness, this study proposes a deep reinforcement learning embedded rule-based energy management system. This method optimises the key parameters of the rule-based energy management system by inserting deep reinforcement learning into it. Therefore, this scheme combines the good optimization effect of deep reinforcement learning and the excellent robustness of rule. In order to verify the feasibility of this scheme, this study builds the system dynamic model and carries out a simulation study. Subsequently, a hybrid powertrain semi physical experimental bench was constructed and a rapid control prototype experimental study was carried out. The simulation results show that the deep reinforcement learning embedded rule-based energy management system can reduce the energy consumption by 4.31 % compared with the rule-based energy management system under the C-WTVC driving cycle. In addition, energy saving and safe operation can also be achieved under other unfamiliar untrained driving cycles. The rapid control prototype experimental study shows that the deep reinforcement learning embedded rule-based energy management system has good agreement in experiment and simulation, which demonstrates the potential for real vehicle engineering applications and promotes the engineering application of deep reinforcement learning.

关键词： Deep reinforcement learning Hybrid electric vehicle Organic Rankine cycle Energy management system Rapid control prototype

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Deep reinforcement learning using least-squares truncated temporal-difference

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 425-439页

作者： Junkai Ren Yixing Lan Xin Xu Yichuan zhang Qiang Fang Yujun Zeng College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control CenterXi'anChina

Policy evaluation(PE)is a critical sub-problem in reinforcement learning,which estimates the value function for a given policy and can be used for policy ***,there still exist some limitations in current PE methods,such as low sample efficiency and local convergence,especially on complex *** this study,a novel PE algorithm called Least-Squares Truncated Temporal-Difference learning(LST2.)is *** LST2.,an adaptive truncation mechanism is designed,which effectively takes advantage of the fast convergence property of Least-Squares Temporal Difference learning and the asymptotic convergence property of Temporal Difference learning(TD).Then,two feature pre-training methods are utilised to improve the approximation ability of ***,an Actor-Critic algorithm based on LST2. and pre-trained feature representations(ACLPF)is proposed,where LST2. is integrated into the critic network to improve learning-prediction *** simulation studies were conducted on four robotic tasks,and the corresponding results illustrate the effectiveness of *** proposed ACLPF algorithm outperformed DQN,ACER and PPO in terms of sample efficiency and stability,which demonstrated that LST2. can be applied to online learning control problems by incorporating it into the actor-critic architecture.

关键词： Deep reinforcement learning policy evaluation temporal difference value function approximation

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