To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions tha...To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.展开更多
In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited ...In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited navigation accuracy,a novel approach for multi-type fusion visual navigation is proposed.This method aims to overcome the limitations of single-type features and enhance navigation accuracy.Analytical criteria for selecting multi-type features are introduced,which simultaneously improve computational efficiency and system navigation accuracy.Concerning pose estimation,both absolute and relative pose estimation methods based on multi-type feature fusion are proposed,and multi-type feature normalization is established,which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation.The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.展开更多
Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management pl...Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.展开更多
Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distribu...Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.展开更多
The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogene...The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.展开更多
Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in t...Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.展开更多
Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biom...Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.展开更多
Canopy and branch architectures in high-density orchards can be crucial in production and fruit quality. The influence of two canopy orientations (Upright and Tilted) in combination with two arm (branch) architectures...Canopy and branch architectures in high-density orchards can be crucial in production and fruit quality. The influence of two canopy orientations (Upright and Tilted) in combination with two arm (branch) architectures (Shortened or Overlapped) on tree growth, yield components, fruit quality, and leaf mineral nutrients in an “Aztec Fuji” apple (Malus domestica Bork.) high-density orchard was studied over five years. Tilted trees with shortened arm configuration (TilShArm) always had significantly larger trunk cross-sectional area (TCSA) than Upright trees with an Overlapped arm configuration (UpOverArm) every year from 2012 to 2016. Trees with a TilShArm system had more cumulative fruit per tree than those with an Upright orientation. Trees with a tilted canopy (TilShArm and TilOverArm) tended to have higher yield per tree and yield per hectare than those with an upright system. Trees with a TilShArm system were more precocious and had more yield per tree than those with an upright canopy orientation in 2012. When values were polled over five years, trees with an upright canopy-shortened arm system (UpShArm) treatment had a lower biennial bearing index (BBI) than those with an upright canopy-overlapped system (UpOverArm). Trees receiving an arm shortening (UpShArm or TilShArm) configuration often had larger fruits than those with overlapped arms (UpOverArm and TilOverArm). Fruit from trees receiving an UpOverArm had higher fruit firmness than those from trees with other canopy-branch arrangements at harvest due to their smaller size. Fruit from trees with a TilShArm and TilOverArm had significantly higher water core and bitter pit but lower sunburn than trees with an upright canopy (UpShArm and UpOverArm). Leaves from trees with an UpOverArm canopy-branch configuration had the lowest leaf Ca but the highest leaf K and Fe concentrations among all treatments.展开更多
Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and m...Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and migration of CBM and control the high production and enrichment of CBM.Therefore,fracture prediction in deep CBM reservoirs is of great significance for the exploration and development of CBM.First,the basic principles of calculating texture attributes by gray-level cooccurrence matrix(GLCM)and gray-level run-length matrix(GLRLM)were introduced.A geological model of the deep CBM reservoirs with fractures was then constructed and subjected to seismic forward simulation.The seismic texture attributes were extracted using the GLCM and GLRLM.The research results indicate that the texture attributes calculated by both methods are responsive to fractures,with the 45°and 135°gray level inhomogeneity texture attributes based on the GLRLM showing better identification effects for fractures.Fracture prediction of a deep CBM reservoir in the Ordos Basin was carried out based on the GLRLM texture attributes,providing an important basis for the efficient development and utilization of deep CBM.展开更多
Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production exp...Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.展开更多
The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evo...The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.展开更多
This resolution 5 (25−1 factorial) study aimed to ascertain an understanding of the interactions between different geometries on the resulting Radar Cross Section (RCS) of a target. The results of the study are in lin...This resolution 5 (25−1 factorial) study aimed to ascertain an understanding of the interactions between different geometries on the resulting Radar Cross Section (RCS) of a target. The results of the study are in line with the general understanding of the impact different geometries have on RCS but show that geometries can also influence the variance of measured RCS, and typical attributes that reduce RCS increase the variance of the measured RCS. Notably, an increased angle between the front face of a plate and the direction of the radar signal decreased RCS but increased the variance of the RCS measured.展开更多
The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess fo...The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess for this articulation to take place adequately.Architecture,as the discipline in charge of satisfying the specific spatial needs of those who inhabit these spaces and,in a broader sense,as a concrete contribution to society,must address this relationship in all its complexity and generate concrete responses that incorporate the appropriate spatial attributes during the design processes.The design processes that shape living spaces confront this dialectic,and the manner in which they do so brings identity and character to them.It is believed that the higher the level of variables that are contemplated and weighted,the greater the adequacy of spaces to the changing dynamics of the people who inhabit them.This article focuses on a thorough analysis of these spatial attributes,in parallel to the definition of each one as a particular condition for design,based on their conceptualization,breakdown,and articulation.Conceptually,the following attributes are addressed:flexibility,adaptability,variability,versatility,multiplicity,plurality,integrality,gradualness,incrementality,progressiveness,independence,connectivity,intimacy,and privacy.Each of these attributes is valued as a contribution to creating adequate habitability in contextual terms,with consideration to possible integrations and combinations.展开更多
As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,in...As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.展开更多
BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AI...BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AIM To understand the attribution of psychiatric manifestations in a cohort of Chinese patients with SLE.METHODS This retrospective single-center study analyzed 160 inpatient medical records.Clinical diagnosis,which is considered the gold standard,was used to divide the subjects into a psychiatric SLE(PSLE)group(G1)and a secondary psychiatric symptoms group(G2).Clinical features were compared between these two groups.The sensitivity and specificity of the Italian attribution model were explored.RESULTS A total of 171 psychiatric syndromes were recorded in 138 patients,including 87 cases of acute confusional state,40 cases of cognitive dysfunction,18 cases of psychosis,and 13 cases each of depressive disorder and mania or hypomania.A total of 141(82.5%)syndromes were attributed to SLE.In contrast to G2 patients,G1 patients had higher SLE Disease Activity Index-2000 scores(21 vs 12,P=0.001),a lower prevalence of anti-beta-2-glycoprotein 1 antibodies(8.6%vs 25.9%,P=0.036),and a higher prevalence of anti-ribosomal ribonucleoprotein particle(rRNP)antibodies(39.0%vs 22.2%,P=0.045).The Italian attribution model exhibited a sensitivity of 95.0%and a specificity of 70.0%when the threshold value was set at 7.CONCLUSION Patients with PSLE exhibited increased disease activity.There is a correlation between PSLE and anti-rRNP antibodies.The Italian model effectively assesses multiple psychiatric manifestations in Chinese SLE patients who present with NP symptoms.展开更多
Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rul...Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to ...Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to 2021,and predict the disease burden trend for 2035.Methods:Data related to knee osteoarthritis in China from 1990 to 2021,including the number of incident cases,incidence rate,number of prevalent cases,prevalence rate,and years lived with disability(YLDs),were collected from the Global Burden of Disease Study(GBD2021)database.Joinpoint regression analysis was used to assess time trends,and the Bayesian-Age-Period-Cohort(BAPC)regression model was employed for future predictions.Results:From 1990 to 2021,the number of incident cases of knee osteoarthritis among Chinese residents increased from 3.65 million to 8.51 million,a rise of 133.16%,with an average annual increase of 3.15%.The incidence rate increased from 310.33 per 100,000 to 598.31 per 100,000,a rise of 92.80%,with an average annual increase of 2.55%.The number of prevalent cases increased from 41.04 million to 110 million,a rise of 166.97%,with an average annual increase of 3.61%.The prevalence rate increased from 3488.78 per 100,000 to 7701.69 per 100,000,a rise of 120.76%,with an average annual increase of 3.00%.The number of YLDs increased from 1.34 million to 3.55 million,a rise of 165.32%,with an average annual increase of 3.59%.The YLD rate increased from 113.86 per 100,000 to 249.81 per 100,000,a rise of 119.39%,with an average annual increase of 2.99%.High BMI was the only significant attributable risk factor,with the proportion of YLDs it caused continuing to rise.Predictions for 2035:The number of incident cases is expected to decline slightly from 5.89 million in 2022 to 5.72 million in 2035.The number of prevalent cases is expected to peak at 72.42 million in 2029 and be around 72.69 million in 2035.The number of YLDs is expected to increase year by year,from 2.35 million in 2022 to 2.35 million in 2035.Conclusion:The study reveals the increasing prevalence and disease burden of knee osteoarthritis among Chinese residents,emphasizing the importance of interventions targeting controllable risk factors.Although the prediction shows a slight decline in the number of incident cases in 2035,the number of prevalent cases and years of disability are expected to remain high,indicating the need for continued monitoring and intervention.展开更多
Seismic attributes encapsulate substantial reservoir characterization information and can effectively support reservoir prediction.Given the high-dimensional nonlinear between sandbodies and seismic attributes,this st...Seismic attributes encapsulate substantial reservoir characterization information and can effectively support reservoir prediction.Given the high-dimensional nonlinear between sandbodies and seismic attributes,this study employs the RFECV method for seismic attribute selection,inputting the optimized attributes into a LightGBM model to enhance spatial delineation of sandbody identification.By constructing training datasets based on optimized seismic attributes and well logs,followed by class imbalance correction as input variables for machine learning models,with sandbody probability as the output variable,and employing grid search to optimize model parameters,a high-precision sandbody prediction model was established.Taking the 3D seismic data of Block F3 in the North Sea of Holland as an example,this method successfully depicted the three-dimensional spatial distribution of target formation sandstones.The results indicate that even under strong noise conditions,the multi-attribute sandbody identification method based on LightGBM effectively characterizes the distribution features of sandbodies.Compared to unselected attributes,the prediction results using selected attributes have higher vertical resolution and inter-well conformity,with the prediction accuracy for single wells reaching 80.77%,significantly improving the accuracy of sandbody boundary delineation.展开更多
Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA)...Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.展开更多
基金supported by the Beijing Natural Science Foundation(No.L212029)the National Natural Science Foundation of China(No.62271043).
文摘To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.
基金supported by the National Natural Science Foundation of China(No.U2037602)。
文摘In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited navigation accuracy,a novel approach for multi-type fusion visual navigation is proposed.This method aims to overcome the limitations of single-type features and enhance navigation accuracy.Analytical criteria for selecting multi-type features are introduced,which simultaneously improve computational efficiency and system navigation accuracy.Concerning pose estimation,both absolute and relative pose estimation methods based on multi-type feature fusion are proposed,and multi-type feature normalization is established,which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation.The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.
基金funded by NARI Group’s Independent Project of China(Grant No.524609230125)the Foundation of NARI-TECH Nanjing Control System Ltd.of China(Grant No.0914202403120020).
文摘Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.
基金supported by the Spanish Ministry of Science and Innovation project GREEN-RISK(Evaluation of past changes in ecosystem services and biodiversity in forests and restoration priorities under global change impacts-PID2020-119933RB-C21)A.C.received a pre-doctoral fellowship funded by the Spanish Ministry of Science and Innovation(PRE2021-099642).
文摘Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.
文摘The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.
文摘Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.
基金supported by the Guangxi Key R&D Program (project No. AB16380254)a research project of Guangxi Forestry Department (Guilinkezi [2015] No.5)supported a grant for Bagui Senior Fellow (C33600992001)。
文摘Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.
文摘Canopy and branch architectures in high-density orchards can be crucial in production and fruit quality. The influence of two canopy orientations (Upright and Tilted) in combination with two arm (branch) architectures (Shortened or Overlapped) on tree growth, yield components, fruit quality, and leaf mineral nutrients in an “Aztec Fuji” apple (Malus domestica Bork.) high-density orchard was studied over five years. Tilted trees with shortened arm configuration (TilShArm) always had significantly larger trunk cross-sectional area (TCSA) than Upright trees with an Overlapped arm configuration (UpOverArm) every year from 2012 to 2016. Trees with a TilShArm system had more cumulative fruit per tree than those with an Upright orientation. Trees with a tilted canopy (TilShArm and TilOverArm) tended to have higher yield per tree and yield per hectare than those with an upright system. Trees with a TilShArm system were more precocious and had more yield per tree than those with an upright canopy orientation in 2012. When values were polled over five years, trees with an upright canopy-shortened arm system (UpShArm) treatment had a lower biennial bearing index (BBI) than those with an upright canopy-overlapped system (UpOverArm). Trees receiving an arm shortening (UpShArm or TilShArm) configuration often had larger fruits than those with overlapped arms (UpOverArm and TilOverArm). Fruit from trees receiving an UpOverArm had higher fruit firmness than those from trees with other canopy-branch arrangements at harvest due to their smaller size. Fruit from trees with a TilShArm and TilOverArm had significantly higher water core and bitter pit but lower sunburn than trees with an upright canopy (UpShArm and UpOverArm). Leaves from trees with an UpOverArm canopy-branch configuration had the lowest leaf Ca but the highest leaf K and Fe concentrations among all treatments.
文摘Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and migration of CBM and control the high production and enrichment of CBM.Therefore,fracture prediction in deep CBM reservoirs is of great significance for the exploration and development of CBM.First,the basic principles of calculating texture attributes by gray-level cooccurrence matrix(GLCM)and gray-level run-length matrix(GLRLM)were introduced.A geological model of the deep CBM reservoirs with fractures was then constructed and subjected to seismic forward simulation.The seismic texture attributes were extracted using the GLCM and GLRLM.The research results indicate that the texture attributes calculated by both methods are responsive to fractures,with the 45°and 135°gray level inhomogeneity texture attributes based on the GLRLM showing better identification effects for fractures.Fracture prediction of a deep CBM reservoir in the Ordos Basin was carried out based on the GLRLM texture attributes,providing an important basis for the efficient development and utilization of deep CBM.
基金the financially supported by the National Natural Science Foundation of China(Grant No.52104013)the China Postdoctoral Science Foundation(Grant No.2022T150724)。
文摘Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.
基金key technology project for the prevention and control of major workplace safety accidents in 2017 from the State Administration of Work Safety of China-the research on the identification and assessment technology and control system of major risks of enterprises for the prevention and control of severe accidents(Hubei-0002-2017AQ)supported by the Department of Emergency Management of Hubei Province,Wuhan 430064,China.
文摘The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.
文摘This resolution 5 (25−1 factorial) study aimed to ascertain an understanding of the interactions between different geometries on the resulting Radar Cross Section (RCS) of a target. The results of the study are in line with the general understanding of the impact different geometries have on RCS but show that geometries can also influence the variance of measured RCS, and typical attributes that reduce RCS increase the variance of the measured RCS. Notably, an increased angle between the front face of a plate and the direction of the radar signal decreased RCS but increased the variance of the RCS measured.
文摘The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess for this articulation to take place adequately.Architecture,as the discipline in charge of satisfying the specific spatial needs of those who inhabit these spaces and,in a broader sense,as a concrete contribution to society,must address this relationship in all its complexity and generate concrete responses that incorporate the appropriate spatial attributes during the design processes.The design processes that shape living spaces confront this dialectic,and the manner in which they do so brings identity and character to them.It is believed that the higher the level of variables that are contemplated and weighted,the greater the adequacy of spaces to the changing dynamics of the people who inhabit them.This article focuses on a thorough analysis of these spatial attributes,in parallel to the definition of each one as a particular condition for design,based on their conceptualization,breakdown,and articulation.Conceptually,the following attributes are addressed:flexibility,adaptability,variability,versatility,multiplicity,plurality,integrality,gradualness,incrementality,progressiveness,independence,connectivity,intimacy,and privacy.Each of these attributes is valued as a contribution to creating adequate habitability in contextual terms,with consideration to possible integrations and combinations.
文摘As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.
基金Supported by STI2030-Major Projects,No.2021ZD0202001National Natural Science Foundation of China,No.T2341003Capital Funds for Health Improvement and Research,No.CFH 2022-2-4012.
文摘BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AIM To understand the attribution of psychiatric manifestations in a cohort of Chinese patients with SLE.METHODS This retrospective single-center study analyzed 160 inpatient medical records.Clinical diagnosis,which is considered the gold standard,was used to divide the subjects into a psychiatric SLE(PSLE)group(G1)and a secondary psychiatric symptoms group(G2).Clinical features were compared between these two groups.The sensitivity and specificity of the Italian attribution model were explored.RESULTS A total of 171 psychiatric syndromes were recorded in 138 patients,including 87 cases of acute confusional state,40 cases of cognitive dysfunction,18 cases of psychosis,and 13 cases each of depressive disorder and mania or hypomania.A total of 141(82.5%)syndromes were attributed to SLE.In contrast to G2 patients,G1 patients had higher SLE Disease Activity Index-2000 scores(21 vs 12,P=0.001),a lower prevalence of anti-beta-2-glycoprotein 1 antibodies(8.6%vs 25.9%,P=0.036),and a higher prevalence of anti-ribosomal ribonucleoprotein particle(rRNP)antibodies(39.0%vs 22.2%,P=0.045).The Italian attribution model exhibited a sensitivity of 95.0%and a specificity of 70.0%when the threshold value was set at 7.CONCLUSION Patients with PSLE exhibited increased disease activity.There is a correlation between PSLE and anti-rRNP antibodies.The Italian model effectively assesses multiple psychiatric manifestations in Chinese SLE patients who present with NP symptoms.
基金supported by the National Natural Science Foundation of China under Grant 61972057Hunan Provincial Natural Science Foundation of China under Grant 2022JJ30623.
文摘Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
文摘Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to 2021,and predict the disease burden trend for 2035.Methods:Data related to knee osteoarthritis in China from 1990 to 2021,including the number of incident cases,incidence rate,number of prevalent cases,prevalence rate,and years lived with disability(YLDs),were collected from the Global Burden of Disease Study(GBD2021)database.Joinpoint regression analysis was used to assess time trends,and the Bayesian-Age-Period-Cohort(BAPC)regression model was employed for future predictions.Results:From 1990 to 2021,the number of incident cases of knee osteoarthritis among Chinese residents increased from 3.65 million to 8.51 million,a rise of 133.16%,with an average annual increase of 3.15%.The incidence rate increased from 310.33 per 100,000 to 598.31 per 100,000,a rise of 92.80%,with an average annual increase of 2.55%.The number of prevalent cases increased from 41.04 million to 110 million,a rise of 166.97%,with an average annual increase of 3.61%.The prevalence rate increased from 3488.78 per 100,000 to 7701.69 per 100,000,a rise of 120.76%,with an average annual increase of 3.00%.The number of YLDs increased from 1.34 million to 3.55 million,a rise of 165.32%,with an average annual increase of 3.59%.The YLD rate increased from 113.86 per 100,000 to 249.81 per 100,000,a rise of 119.39%,with an average annual increase of 2.99%.High BMI was the only significant attributable risk factor,with the proportion of YLDs it caused continuing to rise.Predictions for 2035:The number of incident cases is expected to decline slightly from 5.89 million in 2022 to 5.72 million in 2035.The number of prevalent cases is expected to peak at 72.42 million in 2029 and be around 72.69 million in 2035.The number of YLDs is expected to increase year by year,from 2.35 million in 2022 to 2.35 million in 2035.Conclusion:The study reveals the increasing prevalence and disease burden of knee osteoarthritis among Chinese residents,emphasizing the importance of interventions targeting controllable risk factors.Although the prediction shows a slight decline in the number of incident cases in 2035,the number of prevalent cases and years of disability are expected to remain high,indicating the need for continued monitoring and intervention.
基金co-funded by the China National Nuclear Corporation-State Key Laboratory of Nuclear Resources and Environment(East ChinaUniversity of Technology)Joint Innovation Fund Project(No.2023NRE-LH-08)the Natural Science Foundation of Jiangxi Province,China(No.20252BAC240270)+1 种基金the Funding of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing(2025QZ-YZZ-08)the National Major Science and Technology Project on Deep Earth of China(No.2024ZD 1003300)。
文摘Seismic attributes encapsulate substantial reservoir characterization information and can effectively support reservoir prediction.Given the high-dimensional nonlinear between sandbodies and seismic attributes,this study employs the RFECV method for seismic attribute selection,inputting the optimized attributes into a LightGBM model to enhance spatial delineation of sandbody identification.By constructing training datasets based on optimized seismic attributes and well logs,followed by class imbalance correction as input variables for machine learning models,with sandbody probability as the output variable,and employing grid search to optimize model parameters,a high-precision sandbody prediction model was established.Taking the 3D seismic data of Block F3 in the North Sea of Holland as an example,this method successfully depicted the three-dimensional spatial distribution of target formation sandstones.The results indicate that even under strong noise conditions,the multi-attribute sandbody identification method based on LightGBM effectively characterizes the distribution features of sandbodies.Compared to unselected attributes,the prediction results using selected attributes have higher vertical resolution and inter-well conformity,with the prediction accuracy for single wells reaching 80.77%,significantly improving the accuracy of sandbody boundary delineation.
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
