Located at the northwest continental slope of the South China Sea, the Qiongdongnan Basin bears valley-shaped bathymetry deepening toward east. It is separated from the Yinggehai Basin through NW-trending Indo-China-R...Located at the northwest continental slope of the South China Sea, the Qiongdongnan Basin bears valley-shaped bathymetry deepening toward east. It is separated from the Yinggehai Basin through NW-trending Indo-China-Red River shear zone, and connected with NW subsea basin through the Xisha Trough. Along with the rapid progress of the deepwater exploration, large amounts of high resolution geophysical and geological data were accumulated. Scientific researches about deepwater basins kept revealing brand new tectonic and sedimentary discoveries. In order to summarize the structural features and main controlling factors of the deepwater Qiongdongnan Basin, a series of researches on basin architecture, fault activities, tectonic deformation and evolution were carried out. In reference to analogue modeling experiments, a tectonic situation and a basin formation mechanism were discussed. The researches indicate that：the northern boundary of the Qiongdongnan Basin is strongly controlled by No. 2 fault. The overlapping control of two stress fields from the east and the west made the central depression zone extremely thinned. Combined with the changed stress field, the segmentation of a preexisting weakness zone made the sags in the east experiencing different rifting histories from the west ones. The NE-trending west segment of the Qiongdongnan Basin experienced strong rifting during Eocene, while the roughly EW-trending sags in the east segment show strong rifting during late Eocene and early Oligocene. Local structures such as NW-trending basal fault and inherited uplifts controlled the lateral segmentation. So first order factors such as regional stress field and preexisting weakness zone controlled the basin zonation, while the second order factors determined the segmentation from east to west.展开更多
Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of...Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.展开更多
Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this ana...Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.展开更多
The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity...The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity direction in horizontal wells.Therefore,measuring the mixture flow and water holdup is difficult,resulting in poor interpretation accuracy of the production logging output profile.In this paper,oil–water two-phase flow dynamic simulation logging experiments in horizontal oil–water two-phase fl ow simulation wells were conducted using the Multiple Array Production Suite,which comprises a capacitance array tool(CAT)and a spinner array tool(SAT),and then the response characteristics of SAT and CAT in diff erent fl ow rates and water cut production conditions were studied.According to the response characteristics of CAT in diff erent water holdup ranges,interpolation imaging along the wellbore section determines the water holdup distribution,and then,the oil–water two-phase velocity fi eld in the fl ow section was reconstructed on the basis of the fl ow section water holdup distribution and the logging value of SAT and combined with the rheological equation of viscous fl uid,and the calculation method of the oil–water partial phase fl ow rate in the fl ow section was proposed.This new approach was applied in the experiment data calculations,and the results are basically consistent with the experimental data.The total fl ow rate and water holdup from the calculation are in agreement with the set values in the experiment,suggesting that the method has high accuracy.展开更多
The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for p...The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply.While complex machine learning models can achieve high accuracy,their“blackbox”nature severely limits the model interpretability.In this paper,we explore the trade-off between model accuracy and interpretability for the energy balancing market.Particularly,we take the example of forecasting manual frequency restoration reserve(mFRR)activation price in the balancing market using real market data from different energy price zones.We explore the interpretability of mFRR forecasting using two models:extreme gradient boosting(XGBoost)machine and explainable boosting machine(EBM).We also integrate the two models,and we benchmark all the models against a baseline naive model.Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability.Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price.Importantly,EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics.Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.展开更多
In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflect...In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflection event is neither hyperbolic, nor any simple function. If traditional normal move-out （NMO） and stack imaging technology are still used, it is difficult to get a clear stack image. Based on previous techniques on non-hyperbolic stack, it is thought in this paper that no matter how complex the geological condition is, in order to get an optimized stack image, the stack should be non time move-out stack, and any stacking method limited to some kind of curve will be restricted to application conditions. In order to overcome the above-mentioned limit, a new method called optimized non-hyperbolic stack imaging based on interpretation model is presented in this paper. Based on CMP/CRP （Common-Reflection-Point） gather after NMO or pre-stack migration, this method uses the interpretation model of reflectors as constraint, and takes comparability as a distinguishing criterion, and finally forms a residual move-out correction for the gather of constrained model. Numerical simulation indicates that this method could overcome the non hyperbolic problem and get fine stack image.展开更多
To enhance the prediction accuracy of landslides in in Longyan City,China,this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information ...To enhance the prediction accuracy of landslides in in Longyan City,China,this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information System(GIS)with integrated spatial data,a frequency ratio(FR)model,and a random forest(RF)model(also referred to as the coupled FR-RF model).The coupled FR-RF model was constructed based on the analysis of nine influential factors,including distance from roads,normalized difference vegetation index(NDVI),and slope.The performance of the coupled FR-RF model was assessed using metrics such as Receiver Operating Characteristic(ROC)and Precision-Recall(PR)curves,yielding Area Under the Curve(AUC)values of 0.93 and 0.95,which indicate high predictive accuracy and reliability for geological hazard forecasting.Based on the model predictions,five susceptibility levels were determined in the study area,providing crucial spatial information for geologic hazard prevention and control.The contributions of various influential factors to landslide susceptibility were determined using SHapley Additive exPlanations(SHAP)analysis and the Gini index,enhancing the model interpretability and transparency.Additionally,this study discussed the limitations of the coupled FR-RF model and the prospects for its improvement using new technologies.This study provides an innovative method and theoretical support for geologic hazard prediction and management,holding promising prospects for application.展开更多
This paper analyzes some specific features of the numerical interpretation of high-frequency electromagnetic logging data in vertical, deviated and horizontal boreholes entering oil- and water-saturated formations. Th...This paper analyzes some specific features of the numerical interpretation of high-frequency electromagnetic logging data in vertical, deviated and horizontal boreholes entering oil- and water-saturated formations. The interpretation is based on numerical modeling for signals.展开更多
Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution rem...Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.展开更多
Currently the development of automatic control system is mainly based on manual design. This has made the develop-ment process complicated and has made it difficult to guarantee system requirement. This paper presents...Currently the development of automatic control system is mainly based on manual design. This has made the develop-ment process complicated and has made it difficult to guarantee system requirement. This paper presents a Model in-terpretation development architecture built on meta-models and model interpretation. In this modeling and developing process, different meta-models or domain models may be constructed in terms of various system requirements. Inter-preters are used to transform the meta-model into relevant domain model and generate some other formats from do-main models, typically with different semantic domains. An interpretation extension interface is introduced, which can be accelerated to develop the model interpreter. This development architecture can improve system reusability and en-hance development efficiency. Finally, an example is introduced to explain the advantage of method.展开更多
Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning sy...Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.展开更多
This paper introduces the method of note-taking based on the Gile's Effort Models,exploring how tokeep the balance of memory and note-aking in consecutive interpreting.The paper also analyses some examples tofind ...This paper introduces the method of note-taking based on the Gile's Effort Models,exploring how tokeep the balance of memory and note-aking in consecutive interpreting.The paper also analyses some examples tofind an effective way to balance the memory and note-taking in consecutive interpreting,so as to help interpreters tobetter convey the meaning of speakers accurately and quickly with the help of interpreting notes,thereby improvingthe quality of interpreting.展开更多
For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation...For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.展开更多
In order to detect fault exactly and quickly, cusp catastrophe theory is used to interpret 3D coal seismic data in this paper. By establishing a cusp model, seismic signal is transformed into standard form of cusp cat...In order to detect fault exactly and quickly, cusp catastrophe theory is used to interpret 3D coal seismic data in this paper. By establishing a cusp model, seismic signal is transformed into standard form of cusp catastrophe and catastrophe parameters, including time-domain catastrophe potential, time-domain catastrophe time, frequency-domain catastrophe potential and frequency- domain degree, are calculated. Catastrophe theory is used in 3D seismic structural interpretation in coal mine. The results show that the position of abnormality of the catastrophe parameter profile or curve is related to the location of fault, and the cusp catastrophe theory is effective to automatically pick up geology information and improve the interpretation precision in 3D seismic data.展开更多
This study was conducted to enable prompt classification of malware,which was becoming increasingly sophisticated.To do this,we analyzed the important features of malware and the relative importance of selected featur...This study was conducted to enable prompt classification of malware,which was becoming increasingly sophisticated.To do this,we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified.Initially,the analysis features were extracted using Cuckoo Sandbox,an open-source malware analysis tool,then the features were divided into five categories using the extracted information.The 804 extracted features were reduced by 70%after selecting only the most suitable ones for malware classification using a learning model-based feature selection method called the recursive feature elimination.Next,these important features were analyzed.The level of contribution from each one was assessed by the Random Forest classifier method.The results showed that System call features were mostly allocated.At the end,it was possible to accurately identify the malware type using only 36 to 76 features for each of the four types of malware with the most analysis samples available.These were the Trojan,Adware,Downloader,and Backdoor malware.展开更多
To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new lig...To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.展开更多
Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-...Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.展开更多
Prospectors usually recognize the event with strong amplitude in seismic sections as bright spots. However, such a simple and rough method can’t distinguish whether these bright spots are related to favorable gas lay...Prospectors usually recognize the event with strong amplitude in seismic sections as bright spots. However, such a simple and rough method can’t distinguish whether these bright spots are related to favorable gas layer or water layer directly. In this paper, for the high correlation between reservoir gascontent and amplitude anomaly in research area, based on rock physical analysis of the wells drilled, using forward modeling technique respectively simulates and analyzes the seismic amplitude of gas layer and water layer. Then, combining the simulation result with corresponding statistics amplitude obtains the numerical relationship between each layer amplitude. At last, using the display technique directly recognizes the bright spots of gas layer in seismic profile and gets rid of those false bright spots caused by water layer, which improved the robustness in the bright spots interpretation and provided reliable basis for reducing exploration risks. Moreover, applying the method to the target zone, we obtain huge success.展开更多
Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalizati...Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.展开更多
基金The Major National Science and Technology Programs of China under contract No.2011ZX05025-003-005the Joint Program of the National Science Foundation and Guangdong Province under contract No.U1301233
文摘Located at the northwest continental slope of the South China Sea, the Qiongdongnan Basin bears valley-shaped bathymetry deepening toward east. It is separated from the Yinggehai Basin through NW-trending Indo-China-Red River shear zone, and connected with NW subsea basin through the Xisha Trough. Along with the rapid progress of the deepwater exploration, large amounts of high resolution geophysical and geological data were accumulated. Scientific researches about deepwater basins kept revealing brand new tectonic and sedimentary discoveries. In order to summarize the structural features and main controlling factors of the deepwater Qiongdongnan Basin, a series of researches on basin architecture, fault activities, tectonic deformation and evolution were carried out. In reference to analogue modeling experiments, a tectonic situation and a basin formation mechanism were discussed. The researches indicate that：the northern boundary of the Qiongdongnan Basin is strongly controlled by No. 2 fault. The overlapping control of two stress fields from the east and the west made the central depression zone extremely thinned. Combined with the changed stress field, the segmentation of a preexisting weakness zone made the sags in the east experiencing different rifting histories from the west ones. The NE-trending west segment of the Qiongdongnan Basin experienced strong rifting during Eocene, while the roughly EW-trending sags in the east segment show strong rifting during late Eocene and early Oligocene. Local structures such as NW-trending basal fault and inherited uplifts controlled the lateral segmentation. So first order factors such as regional stress field and preexisting weakness zone controlled the basin zonation, while the second order factors determined the segmentation from east to west.
文摘Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.
基金Supported by the National Natural Science Foundation of China(No.51379006 and No.51009106)the Program for New Century Excellent Talents in University of Ministry of Education of China(No.NCET-12-0404)the National Basic Research Program of China("973"Program,No.2013CB035903)
文摘Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.
基金supported by National Natural Science Foundation of China(41474115,42174155)Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources(Yangtze University)Ministry of Education of China(No K2018-02)。
文摘The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity direction in horizontal wells.Therefore,measuring the mixture flow and water holdup is difficult,resulting in poor interpretation accuracy of the production logging output profile.In this paper,oil–water two-phase flow dynamic simulation logging experiments in horizontal oil–water two-phase fl ow simulation wells were conducted using the Multiple Array Production Suite,which comprises a capacitance array tool(CAT)and a spinner array tool(SAT),and then the response characteristics of SAT and CAT in diff erent fl ow rates and water cut production conditions were studied.According to the response characteristics of CAT in diff erent water holdup ranges,interpolation imaging along the wellbore section determines the water holdup distribution,and then,the oil–water two-phase velocity fi eld in the fl ow section was reconstructed on the basis of the fl ow section water holdup distribution and the logging value of SAT and combined with the rheological equation of viscous fl uid,and the calculation method of the oil–water partial phase fl ow rate in the fl ow section was proposed.This new approach was applied in the experiment data calculations,and the results are basically consistent with the experimental data.The total fl ow rate and water holdup from the calculation are in agreement with the set values in the experiment,suggesting that the method has high accuracy.
基金PriTEM project funded by UiO:Energy Convergence Environments
文摘The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand.Modeling dynamics in the balancing market can provide valuable insights and prognosis for power grid stability and secure energy supply.While complex machine learning models can achieve high accuracy,their“blackbox”nature severely limits the model interpretability.In this paper,we explore the trade-off between model accuracy and interpretability for the energy balancing market.Particularly,we take the example of forecasting manual frequency restoration reserve(mFRR)activation price in the balancing market using real market data from different energy price zones.We explore the interpretability of mFRR forecasting using two models:extreme gradient boosting(XGBoost)machine and explainable boosting machine(EBM).We also integrate the two models,and we benchmark all the models against a baseline naive model.Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability.Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price.Importantly,EBM's interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics.Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.
文摘In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflection event is neither hyperbolic, nor any simple function. If traditional normal move-out （NMO） and stack imaging technology are still used, it is difficult to get a clear stack image. Based on previous techniques on non-hyperbolic stack, it is thought in this paper that no matter how complex the geological condition is, in order to get an optimized stack image, the stack should be non time move-out stack, and any stacking method limited to some kind of curve will be restricted to application conditions. In order to overcome the above-mentioned limit, a new method called optimized non-hyperbolic stack imaging based on interpretation model is presented in this paper. Based on CMP/CRP （Common-Reflection-Point） gather after NMO or pre-stack migration, this method uses the interpretation model of reflectors as constraint, and takes comparability as a distinguishing criterion, and finally forms a residual move-out correction for the gather of constrained model. Numerical simulation indicates that this method could overcome the non hyperbolic problem and get fine stack image.
基金supported by the project of the China Geological Survey(DD20230591).
文摘To enhance the prediction accuracy of landslides in in Longyan City,China,this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information System(GIS)with integrated spatial data,a frequency ratio(FR)model,and a random forest(RF)model(also referred to as the coupled FR-RF model).The coupled FR-RF model was constructed based on the analysis of nine influential factors,including distance from roads,normalized difference vegetation index(NDVI),and slope.The performance of the coupled FR-RF model was assessed using metrics such as Receiver Operating Characteristic(ROC)and Precision-Recall(PR)curves,yielding Area Under the Curve(AUC)values of 0.93 and 0.95,which indicate high predictive accuracy and reliability for geological hazard forecasting.Based on the model predictions,five susceptibility levels were determined in the study area,providing crucial spatial information for geologic hazard prevention and control.The contributions of various influential factors to landslide susceptibility were determined using SHapley Additive exPlanations(SHAP)analysis and the Gini index,enhancing the model interpretability and transparency.Additionally,this study discussed the limitations of the coupled FR-RF model and the prospects for its improvement using new technologies.This study provides an innovative method and theoretical support for geologic hazard prediction and management,holding promising prospects for application.
文摘This paper analyzes some specific features of the numerical interpretation of high-frequency electromagnetic logging data in vertical, deviated and horizontal boreholes entering oil- and water-saturated formations. The interpretation is based on numerical modeling for signals.
基金the National Key R&D Program of China(2019YFC1510700)the Sichuan Science and Technology Program(2022YFS0539)the Geomatics Technology and Application Key Laboratory of Qinghai Province,China(QHDX-2018-07).
文摘Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.
文摘Currently the development of automatic control system is mainly based on manual design. This has made the develop-ment process complicated and has made it difficult to guarantee system requirement. This paper presents a Model in-terpretation development architecture built on meta-models and model interpretation. In this modeling and developing process, different meta-models or domain models may be constructed in terms of various system requirements. Inter-preters are used to transform the meta-model into relevant domain model and generate some other formats from do-main models, typically with different semantic domains. An interpretation extension interface is introduced, which can be accelerated to develop the model interpreter. This development architecture can improve system reusability and en-hance development efficiency. Finally, an example is introduced to explain the advantage of method.
基金the King Salman center for Disability Research for funding this work through Research Group No.KSRG-2024-050.
文摘Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.
文摘This paper introduces the method of note-taking based on the Gile's Effort Models,exploring how tokeep the balance of memory and note-aking in consecutive interpreting.The paper also analyses some examples tofind an effective way to balance the memory and note-taking in consecutive interpreting,so as to help interpreters tobetter convey the meaning of speakers accurately and quickly with the help of interpreting notes,thereby improvingthe quality of interpreting.
基金the National Basic Research Program of China (973 Program) ( 2007CB407206)the National Key Technologies Research and Develop-ment Program in the Eleventh Five-Year Plan of China (2006BAC01A11)
文摘For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.
文摘In order to detect fault exactly and quickly, cusp catastrophe theory is used to interpret 3D coal seismic data in this paper. By establishing a cusp model, seismic signal is transformed into standard form of cusp catastrophe and catastrophe parameters, including time-domain catastrophe potential, time-domain catastrophe time, frequency-domain catastrophe potential and frequency- domain degree, are calculated. Catastrophe theory is used in 3D seismic structural interpretation in coal mine. The results show that the position of abnormality of the catastrophe parameter profile or curve is related to the location of fault, and the cusp catastrophe theory is effective to automatically pick up geology information and improve the interpretation precision in 3D seismic data.
基金supported by the Research Program through the National Research Foundation of Korea,NRF-2018R1D1A1B07050864.
文摘This study was conducted to enable prompt classification of malware,which was becoming increasingly sophisticated.To do this,we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified.Initially,the analysis features were extracted using Cuckoo Sandbox,an open-source malware analysis tool,then the features were divided into five categories using the extracted information.The 804 extracted features were reduced by 70%after selecting only the most suitable ones for malware classification using a learning model-based feature selection method called the recursive feature elimination.Next,these important features were analyzed.The level of contribution from each one was assessed by the Random Forest classifier method.The results showed that System call features were mostly allocated.At the end,it was possible to accurately identify the malware type using only 36 to 76 features for each of the four types of malware with the most analysis samples available.These were the Trojan,Adware,Downloader,and Backdoor malware.
基金support provided by the National Natural Science Foundation of China(22122802,22278044,and 21878028)the Chongqing Science Fund for Distinguished Young Scholars(CSTB2022NSCQ-JQX0021)the Fundamental Research Funds for the Central Universities(2022CDJXY-003).
文摘To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.
基金Supported by the National Natural Science Foundation of China(61374166)the Doctoral Fund of Ministry of Education of China(20120010110010)the Natural Science Fund of Ningbo(2012A610001)
文摘Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.
文摘Prospectors usually recognize the event with strong amplitude in seismic sections as bright spots. However, such a simple and rough method can’t distinguish whether these bright spots are related to favorable gas layer or water layer directly. In this paper, for the high correlation between reservoir gascontent and amplitude anomaly in research area, based on rock physical analysis of the wells drilled, using forward modeling technique respectively simulates and analyzes the seismic amplitude of gas layer and water layer. Then, combining the simulation result with corresponding statistics amplitude obtains the numerical relationship between each layer amplitude. At last, using the display technique directly recognizes the bright spots of gas layer in seismic profile and gets rid of those false bright spots caused by water layer, which improved the robustness in the bright spots interpretation and provided reliable basis for reducing exploration risks. Moreover, applying the method to the target zone, we obtain huge success.
基金Supported by the National Natural Science Foundation of China(61473026,61104131)the Fundamental Research Funds for the Central Universities(JD1413)
文摘Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.
