Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep ...Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.展开更多
The main body of household operation in the rural household contract responsibility system of our country has developed to the present stage,and has formed the situation that three kinds of rural households coexist,na...The main body of household operation in the rural household contract responsibility system of our country has developed to the present stage,and has formed the situation that three kinds of rural households coexist,namely,ordinary rural households,major professional households and family farms. The agricultural production and operation of three kinds of household plays an important role in supporting the rapid development of modern agriculture in China. Under the new situation of deepening the rural reform and realizing the goal of well-off society in an all-round way,it is of great practical significance to make a thorough investigation and study on the present situation and approaches relating to the construction of the agricultural production and operation capacity for the three kinds of rural households.展开更多
Bulk mineral resources of iron ores, copper ores, bauxite, lead ores, zinc ores and potassium salt play a pivotal role on the world's and China's economic development. This study analyzed and predicted their resourc...Bulk mineral resources of iron ores, copper ores, bauxite, lead ores, zinc ores and potassium salt play a pivotal role on the world's and China's economic development. This study analyzed and predicted their resources base and potential, development and utilization and their world's and China's supply and demand situation in the future 20 years. The supply and demand of these six bulk mineral products are generally balanced, with a slight surplus, which will guarantee the stability of the international mineral commodity market supply. The six mineral resources (especially iron ores and copper ores) are abundant and have a great potential, and their development and utilization scale will gradually increase. Till the end of 2014, the reserve- production ratio of iron, copper, bauxite, lead, zinc ores and potassium salt was 95 years, 42 years, 100 years, 17 years, 37 years and 170 years, respectively. Except lead ores, the other five types all have reserve-production ratio exceeding 20 years, indicative of a high resources guarantee degree. If the utilization of recycled metals is counted in, the supply of the world's six mineral products will exceed the demand in the future twenty years. In 2015-2035, the supply of iron ores, refined copper, primary aluminum, refined lead, zinc and potassium salt will exceed their demand by 0.4-0.7 billion tons (Gt), 5.0-6.0 million tons (Mt), 1.1-8.9 Mt, 1.0-2.0 Mt, 1.2-2.0 Mt and 4.8-5.6 Mt, respectively. It is predicted that there is no problem with the supply side of bulk mineral products such as iron ores, but local or structural shortage may occur because of geopolitics, monopoly control, resources nationalism and trade friction. Affected by China's compressed industrialized development model, the demand of iron ores (crude steel), potassium salt, refined lead, refined copper, bauxite (primary aluminum) and zinc will gradually reach their peak in advance. The demand peak of iron ores (crude steel) will reach around 2015, 2016 for potassium salt, 2020 for refined lead, 2021 for bauxite (primary aluminum), 2022 for refined copper and 2023 for zinc. China's demand for iron ores (crude steel), bauxite (primary aluminum) and zinc in the future 20 years will decline among the world's demand, while that for refined copper, refined lead and potassium salt will slightly increase. The demand for bulk mineral products still remains high. In 2015-2035, China's accumulative demand for iron ores (crude steel) will be 20.313 Gt (13.429 Gt), 0.304 Gt for refined copper, 2.466 Gt (0.616 Gt) of bauxite (primary aluminum), 0.102 Gt of refined lead, 0.138 Gt of zinc and 0.157 Gt of potassium salt, and they account for the world's YOY (YOY) accumulative demand of 35.17%, 51.09%, 48.47%, 46.62%, 43.95% and 21.84%, respectively. This proportion is 49.40%, 102.52%, 87.44%, 105.65%, 93.62% and 106.49% of that in 2014, respectively. From the supply side of China's bulk mineral resources, it is forecasted that the accumulative supply of primary (mine) mineral products in 2015-2035 is 4.046 Gt of iron ores, 0.591 Gt of copper, 1.129 Gt of bauxite, 63.661 Mt of (mine) lead, 0.109 Gt of (mine) zinc and 0.128 Gt of potassium salt, which accounts for 8.82%, 13.92%, 26.67%, 47.09%, 33.04% and 15.56% of the world's predicted YOY production, respectively. With the rapid increase in the smelting capacity of iron and steel and alumina, the rate of capacity utilization for crude steel, refined copper, alumina, primary aluminum and refined lead in 2014 was 72.13%, 83.63%, 74.45%, 70.76% and 72.22%, respectively. During 2000-2014, the rate of capacity utilization for China's crude steel and refined copper showed a generally fluctuating decrease, which leads to an insufficient supply of primary mineral products. It is forecasted that the supply insufficiency of iron ores in 2015-2035 is 17.44 Gt, 0.245 Gt of copper in copper concentrates, 1.337 Gt of bauxite, 38.44 Mt of lead in lead concentrates and 29.19 Mt of zinc in zinc concentrates. China has gradually raised the utilization of recycled metals, which has mitigated the insufficient supply of primary metal products to some extent. It is forecasted that in 2015-2035 the accumulative utilization amount of steel scrap (iron ores) is 3.27 Gt (5.08 Gt), 70.312 Mt of recycled copper, 0.2 Gt of recycled aluminum, 48 Mt of recycled lead and 7.7 Mt of recycled zinc. The analysis on the supply and demand situation of China's bulk mineral resources in 2015-2035 suggests that the supply-demand contradiction for these six types of mineral products will decrease, indicative of a generally declining external dependency. If the use of recycled metal amount is counted in, the external dependency of China's iron, copper, bauxite, lead, zinc and potassium salt will be 79%, 65%, 26%, 8%, 16% and 18% in 2014, respectively. It is predicted that this external dependency will decrease to 62%, 64%, 20%, -0.93%, 16% and 14% in 2020, respectively, showing an overall decreasing trend. We propose the following suggestions correspondingly. (1) The demand peak of China's crude steel and potassium salt will reach during 2015-2023 in succession. Mining transformation should be planned and deployed in advance to deal with the arrival of this demand peak. (2) The supply-demand contradiction of China's bulk mineral resources will mitigate in the future 20 years, and the external dependency will decrease accordingly. It is suggested to adjust the mineral resources management policies according to different minerals and regions, and regulate the exploration and development activities. (3) China should further establish and improve the forced mechanism of resolving the smelting overcapacity of steel, refined copper, primary aluminum, lead and zinc to really achieve the goal of "reducing excess production capacity". (4) In accordance with the national strategic deployment of "One Belt One Road", China should encourage the excess capacity of steel, copper, alumina and primary aluminum enterprises to transfer to those countries or areas with abundant resources, high energy matching degree and relatively excellent infrastructure. Based on the national conditions, mining condition and geopolitics of the resources countries, we will gradually build steel, copper, aluminum and lead-zinc smelting bases, and potash processing and production bases, which will promote the excess capacity to transfer to the overseas orderly. (5) It is proposed to strengthen the planning and management of renewable resources recycling and to construct industrial base of renewable metal recycling. (6) China should promote the comprehensive development and utilization of paragenetic and associated mineral species to further improve the comprehensive utilization of bulk mineral resources.展开更多
Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend...Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.展开更多
A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates f...A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.展开更多
Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,auton...Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.展开更多
A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out....A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.展开更多
This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vege...This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.展开更多
The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused ...The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling.The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.The approach includes description of the proposed probabilistic models,optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale robot actions in uncertainty conditions.The approach means practically a proactive commitment to excellence in uncertainty conditions.A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.展开更多
基金supported by Hunan Province Enterprise Technology Innovation and Entrepreneurship Team Support Program Project,Hunan Province Science and Technology Innovation Leading Talent Project[2023RC1088]Hunan Province Science and Technology Talent Support Project[2023TJ-Z10].
文摘Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.
基金Supported by Social Science Fund Project of Hubei Province in 2016(2016106)
文摘The main body of household operation in the rural household contract responsibility system of our country has developed to the present stage,and has formed the situation that three kinds of rural households coexist,namely,ordinary rural households,major professional households and family farms. The agricultural production and operation of three kinds of household plays an important role in supporting the rapid development of modern agriculture in China. Under the new situation of deepening the rural reform and realizing the goal of well-off society in an all-round way,it is of great practical significance to make a thorough investigation and study on the present situation and approaches relating to the construction of the agricultural production and operation capacity for the three kinds of rural households.
基金supported by the China geological survey subproject of Dynamic Track and Evaluation of the Guarantee Degree of the Main Mineral Resources in China(No.121201103000150112,N1618-8)
文摘Bulk mineral resources of iron ores, copper ores, bauxite, lead ores, zinc ores and potassium salt play a pivotal role on the world's and China's economic development. This study analyzed and predicted their resources base and potential, development and utilization and their world's and China's supply and demand situation in the future 20 years. The supply and demand of these six bulk mineral products are generally balanced, with a slight surplus, which will guarantee the stability of the international mineral commodity market supply. The six mineral resources (especially iron ores and copper ores) are abundant and have a great potential, and their development and utilization scale will gradually increase. Till the end of 2014, the reserve- production ratio of iron, copper, bauxite, lead, zinc ores and potassium salt was 95 years, 42 years, 100 years, 17 years, 37 years and 170 years, respectively. Except lead ores, the other five types all have reserve-production ratio exceeding 20 years, indicative of a high resources guarantee degree. If the utilization of recycled metals is counted in, the supply of the world's six mineral products will exceed the demand in the future twenty years. In 2015-2035, the supply of iron ores, refined copper, primary aluminum, refined lead, zinc and potassium salt will exceed their demand by 0.4-0.7 billion tons (Gt), 5.0-6.0 million tons (Mt), 1.1-8.9 Mt, 1.0-2.0 Mt, 1.2-2.0 Mt and 4.8-5.6 Mt, respectively. It is predicted that there is no problem with the supply side of bulk mineral products such as iron ores, but local or structural shortage may occur because of geopolitics, monopoly control, resources nationalism and trade friction. Affected by China's compressed industrialized development model, the demand of iron ores (crude steel), potassium salt, refined lead, refined copper, bauxite (primary aluminum) and zinc will gradually reach their peak in advance. The demand peak of iron ores (crude steel) will reach around 2015, 2016 for potassium salt, 2020 for refined lead, 2021 for bauxite (primary aluminum), 2022 for refined copper and 2023 for zinc. China's demand for iron ores (crude steel), bauxite (primary aluminum) and zinc in the future 20 years will decline among the world's demand, while that for refined copper, refined lead and potassium salt will slightly increase. The demand for bulk mineral products still remains high. In 2015-2035, China's accumulative demand for iron ores (crude steel) will be 20.313 Gt (13.429 Gt), 0.304 Gt for refined copper, 2.466 Gt (0.616 Gt) of bauxite (primary aluminum), 0.102 Gt of refined lead, 0.138 Gt of zinc and 0.157 Gt of potassium salt, and they account for the world's YOY (YOY) accumulative demand of 35.17%, 51.09%, 48.47%, 46.62%, 43.95% and 21.84%, respectively. This proportion is 49.40%, 102.52%, 87.44%, 105.65%, 93.62% and 106.49% of that in 2014, respectively. From the supply side of China's bulk mineral resources, it is forecasted that the accumulative supply of primary (mine) mineral products in 2015-2035 is 4.046 Gt of iron ores, 0.591 Gt of copper, 1.129 Gt of bauxite, 63.661 Mt of (mine) lead, 0.109 Gt of (mine) zinc and 0.128 Gt of potassium salt, which accounts for 8.82%, 13.92%, 26.67%, 47.09%, 33.04% and 15.56% of the world's predicted YOY production, respectively. With the rapid increase in the smelting capacity of iron and steel and alumina, the rate of capacity utilization for crude steel, refined copper, alumina, primary aluminum and refined lead in 2014 was 72.13%, 83.63%, 74.45%, 70.76% and 72.22%, respectively. During 2000-2014, the rate of capacity utilization for China's crude steel and refined copper showed a generally fluctuating decrease, which leads to an insufficient supply of primary mineral products. It is forecasted that the supply insufficiency of iron ores in 2015-2035 is 17.44 Gt, 0.245 Gt of copper in copper concentrates, 1.337 Gt of bauxite, 38.44 Mt of lead in lead concentrates and 29.19 Mt of zinc in zinc concentrates. China has gradually raised the utilization of recycled metals, which has mitigated the insufficient supply of primary metal products to some extent. It is forecasted that in 2015-2035 the accumulative utilization amount of steel scrap (iron ores) is 3.27 Gt (5.08 Gt), 70.312 Mt of recycled copper, 0.2 Gt of recycled aluminum, 48 Mt of recycled lead and 7.7 Mt of recycled zinc. The analysis on the supply and demand situation of China's bulk mineral resources in 2015-2035 suggests that the supply-demand contradiction for these six types of mineral products will decrease, indicative of a generally declining external dependency. If the use of recycled metal amount is counted in, the external dependency of China's iron, copper, bauxite, lead, zinc and potassium salt will be 79%, 65%, 26%, 8%, 16% and 18% in 2014, respectively. It is predicted that this external dependency will decrease to 62%, 64%, 20%, -0.93%, 16% and 14% in 2020, respectively, showing an overall decreasing trend. We propose the following suggestions correspondingly. (1) The demand peak of China's crude steel and potassium salt will reach during 2015-2023 in succession. Mining transformation should be planned and deployed in advance to deal with the arrival of this demand peak. (2) The supply-demand contradiction of China's bulk mineral resources will mitigate in the future 20 years, and the external dependency will decrease accordingly. It is suggested to adjust the mineral resources management policies according to different minerals and regions, and regulate the exploration and development activities. (3) China should further establish and improve the forced mechanism of resolving the smelting overcapacity of steel, refined copper, primary aluminum, lead and zinc to really achieve the goal of "reducing excess production capacity". (4) In accordance with the national strategic deployment of "One Belt One Road", China should encourage the excess capacity of steel, copper, alumina and primary aluminum enterprises to transfer to those countries or areas with abundant resources, high energy matching degree and relatively excellent infrastructure. Based on the national conditions, mining condition and geopolitics of the resources countries, we will gradually build steel, copper, aluminum and lead-zinc smelting bases, and potash processing and production bases, which will promote the excess capacity to transfer to the overseas orderly. (5) It is proposed to strengthen the planning and management of renewable resources recycling and to construct industrial base of renewable metal recycling. (6) China should promote the comprehensive development and utilization of paragenetic and associated mineral species to further improve the comprehensive utilization of bulk mineral resources.
文摘Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.
文摘A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.
文摘Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.
基金Huo Yingdong Education Foundation Young Teachers Fund for Higher Education Institutions(171043)Sichuan Outstanding Young Science and Technology Talent Project(2019JDJQ0036)。
文摘A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.
基金Under the auspices of National Natural Science Foundation of China(No.41001363)
文摘This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.
文摘The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling.The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.The approach includes description of the proposed probabilistic models,optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale robot actions in uncertainty conditions.The approach means practically a proactive commitment to excellence in uncertainty conditions.A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.