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Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

The Crop Journal 2024年第2期12卷 614-629页

作者： Bingwen Qiu Linhai Yu Peng Yang Wenbin Wu Jianfeng Chen Xiaolin Zhu Mingjie Duan key laboratory of spatial data mining&information sharing of Ministry of Education Academy of Digital China(Fujian)Fuzhou UniversityFuzhou 350116FujianChina key laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural AffairsBeijing 100081China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityKowloon 999077Hong KongChina

Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural *** UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural ***,datasets describing cropping systems are limited in spatial coverage and crop *** UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large *** describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)*** indicator,the VV backscatter range,was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed *** RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic *** study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)*** performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92%.There were7348 km^(2) areas of UCR,roughly one-half of them located in *** UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69%and 15%of UCR *** patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice *** to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.

关键词： Cropping-pattern mapping Paddy rice Sentinel-1/2 China Sustainable intensification

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Learning-based counterfactual explanations for recommendation

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Science China(information Sciences) 2024年第8期67卷 115-129页

作者： Jingxuan WEN Huafeng LIU Liping JING Jian YU School of Computer and information Technology Beijing Jiaotong University Beijing key Lab of Traffic data Analysis and mining Beijing Jiaotong University

Counterfactual explanations provide explanations by exploring the changes in effect caused by changes in cause. They have attracted significant attention in recommender system research to explore the impact of changes in certain properties on the recommendation mechanism. Among several counterfactual recommendation methods, item-based counterfactual explanation methods have attracted considerable attention because of their flexibility. The core idea of item-based counterfactual explanation methods is to find a minimal subset of interacted items(i.e., short length) such that the recommended item would topple out of the top-K recommendation list once these items have been removed from user interactions(i.e., good quality). Usually, explanations are generated by ranking the precomputed importance of items, which fails to characterize the true importance of interacted items due to separation from the explanation generation. Additionally, the final explanations are generated according to a certain search strategy given the precomputed importance. This indicates that the quality and length of counterfactual explanations are deterministic;therefore, they cannot be balanced once the search strategy is fixed. To overcome these obstacles, this study proposes learning-based counterfactual explanations for recommendation(LCER) to provide counterfactual explanations based on personalized recommendations by jointly modeling the factual and counterfactual preference. To achieve consistency between the computation of importance and generation of counterfactual explanations, the proposed LCER endows an optimizable importance for each interacted item, which is supervised by the goal of counterfactual explanations to guarantee its credibility. Because of the model's flexibility, the trade-off between quality and length can be customized by setting different proportions. The experimental results on four real-world datasets demonstrate the effectiveness of the proposed LCER over several

关键词： recommender system explainable recommendation item-based explanation counterfactual inference counterfactual explanation

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L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

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Computers, Materials & Continua 2024年第5期79卷 1975-1994页

作者： Chuandong Qin Yu Cao Liqun Meng School of Mathematics and information Science North Minzu UniversityYinchuan750021China Ningxia key laboratory of Intelligent information and Big data Processing Yinchuan750021China

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous *** tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment *** learning models have become key players in automating brain tumor *** descent methods are the mainstream algorithms for solving machine learning *** this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor ***,the smooth hinge loss is introduced to be used as the loss function of *** avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent ***,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the ***,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth *** computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of *** DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core *** to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence *** the perspective of loss reduction,DPGD converges faster than *** experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor ***-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

关键词： Support vector machine proximal stochastic gradient descent brain tumor detection distributed computing

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Machine Learning for Signal Demodulation in Underwater Wireless Optical Communications

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China Communications 2024年第5期21卷 297-313页

作者： Ma Shuai Yang Lei Ding Wanying Li Hang Zhang Zhongdan Xu Jing Li Zongyan Xu Gang Li Shiyin Department of Strategic and Advanced Interdisciplinary Research Peng Cheng LaboratoryShenzhen 518055China School of information and Control Engineering China University of Mining and TechnologyXuzhou 221116China data-driven Intelligent information System laboratory Shenzhen Research Institute of Big DataShenzhen 518172China Ocean College Zhejiang UniversityZhoushan 316021China State key laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication *** other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental *** on such a platform,we first construct a real signal dataset with ten modulation ***,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature *** also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated ***,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other *** also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.

关键词： AdaBoost DBN machine learning signal demodulation

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Ultra-highly sensitive dual gases detection based on photoacoustic spectroscopy by exploiting a long-wave,high-power,wide-tunable,singlelongitudinal-mode solid-state laser

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Light(Science & Applications) 2024年第5期13卷 941-956页

作者： Shunda Qiao Ying He Haiyue Sun Pietro Patimisco Angelo Sampaolo Vincenzo Spagnolo Yufei Ma National key laboratory of Laser spatial information Harbin Institute of TechnologyHarbinChina PolySense Lab Dipartimento Interateneo di FisicaUniversity and Politecnico of BariVia AmendolaBariItaly

Photoacoustic spectroscopy(PAS)as a highly sensitive and selective trace gas detection technique has extremely broad application in many ***,the laser sources currently used in PAS limit the sensing *** to diode laser and quantum cascade laser,the solid-state laser has the merits of high optical power,excellent beam quality,and wide tuning *** we present a long-wave,high-power,wide-tunable,singlelongitudinal-mode solid-state laser used as light source in a PAS sensor for trace gas *** self-built solid-state laser had an emission wavelength of~2μm with Tm:YAP crystal as the gain material,with an excellent wavelength and optical power stability as well as a high beam *** wide wavelength tuning range of 9.44 nm covers the absorption spectra of water and ammonia,with a maximum optical power of~130 mW,allowing dual gas detection with a single laser *** solid-state laser was used as light source in three different photoacoustic detection techniques:standard PAS with microphone,and external-and intra-cavity quartz-enhanced photoacoustic spectroscopy(QEPAS),proving that solid-state laser is an attractive excitation source in photoacoustic spectroscopy.

关键词： power tunable tuning

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Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

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Computers, Materials & Continua 2024年第8期80卷 3193-3219页

作者： Linghong Kuang Wenlong Shi Jing Zhang School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China Fujian Provincial key laboratory of Big data mining and Applications Fuzhou350118China

The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and ***,with the advancement of information and communication technology,new security and privacy challenges have emerged for *** address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this *** proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart *** methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection ***,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user *** results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.

关键词： AMI cloud and fog assistance fuzzy comprehensive analysis hierarchical threshold encryption

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An Improved Northern Goshawk Optimization Algorithm for Feature Selection

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Journal of Bionic Engineering 2024年第4期21卷 2034-2072页

作者： Rongxiang Xie Shaobo Li Fengbin Wu State key laboratory of Public Big data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China State key laboratory of Public Big data Guizhou UniversityGuiyang550025China

Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a *** work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS *** NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern *** order to overcome the disadvantages that NGO is prone to local optimum trap,slow convergence speed and low convergence accuracy,two strategies are introduced in the original NGO to boost the effectiveness of ***,a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population ***,a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and *** prove the effectiveness of the suggested DENGO,it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions,and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and ***,the proposed DENGO is used for FS,and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods,and therefore,DENGO is considered to be one of the most prospective FS ***'s code can be obtained at https://***/matlabcentral/fileexchange/158811-project1.

关键词： Northern goshawk optimization Learning strategy Hybrid differential strategy Numerical optimization Feature selection

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Long-term(2013-2022)mapping of winter wheat in the North China Plain using Landsat data:classification with optimal zoning strategy

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Big Earth data 2024年第3期8卷 494-521页

作者： Yifei Liu Xuehong Chen Jin Chen Yunze Zang Jingyi Wang Miao Lu Liang Sun Qi Dong Bingwen Qiu Xiufang Zhu State key laboratory of Remote Sensing Science Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina Beijing Engineering Research Center for Global Land Remote Sensing Products Institute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijingChina State key laboratory of Crop Gene Resources and Breeding Institute of Crop SciencesChinese Academy of Agricultural Sciences(CAAS)BeijingChina State key laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina key laboratory of spatial data mining&information sharing of Ministry of Education Academy of Digital China(Fujian)Fuzhou UniversityFuzhouChina

security and formulating agricultural *** data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this *** various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse *** learning is an effective method for crop classification using Landsat ***,applying machine learn-ing for winter wheat mapping in the North China Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this *** address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop ***,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification *** each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI *** sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different *** derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial *** results underscore the reliability of the produced annual winter wheat *** experiments demonstrate that

关键词： Winter wheat mapping Landsat machine learning North China Plain optimal zoning

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Analysis of phase preservation and interferometric offset test in sparse SAR imaging

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Science China(information Sciences) 2024年第2期67卷 219-231页

作者： Zhongqiu XU Bingchen ZHANG Guangzuo LI Xueli ZHAN Yanfei BAO Yirong WU key laboratory of spatial information Processing and Application System Technology Chinese Academy of Sciences Aerospace information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences PLA Academy of Military Sciences

Sparse synthetic aperture radar(SAR) imaging has emerged as a reliable microwave imaging scheme in the recent decade and excels in down-sampling reconstruction and full-sampling performance improvements such as noise, sidelobe, speckle, and ambiguity suppression. To utilize complex image products of sparse reconstruction for improvement in polarimetric, interferometric, and tomographic SAR imaging, it is necessary to evaluate the phase preservation of sparse SAR imaging. In this study, we first introduce the general alternating direction method of multipliers(ADMM) as the universal framework for sparse reconstruction algorithms and adopt chirp scaling algorithm(CSA)-based azimuth-range decouple operators to avoid expensive data storage and processing. Further, we theoretically analyze the phase preservation of the sparse reconstruction algorithm through a comparison with the reconstruction results of CSA. Finally,we conduct the interferometric offset test on the sparse reconstruction results of simulated and real Gaofen-3(GF-3) SAR data, demonstrating the phase-preserving ability of sparse methods.

关键词： sparse SAR imaging phase preservation interferometric offset test general ADMM azimuth-range decouple

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An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2201-2237页

作者： Thi-Kien Dao Trong-The Nguyen Fujian Provincial key laboratory of Big data mining and Applications Fujian University of TechnologyFuzhou350118China Multimedia Communication Lab University of Information TechnologyHo Chi Minh CityVietnam

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

关键词： Node localization whale optimization algorithm wireless sensor networks siege whale optimization algorithm optimization

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