Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecas...Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.展开更多
In an era of rising trade protectionism and frequent antiglobalization events,strengthening regional economic connections has important practical significance for resisting external economic shocks,improving economic ...In an era of rising trade protectionism and frequent antiglobalization events,strengthening regional economic connections has important practical significance for resisting external economic shocks,improving economic resilience,and promoting regional economic development.Based on input-output analysis,value-added decomposition,and network analysis,this paper uses long-term,multiregional input-output data to measure the spatiotemporal patterns of Asian production networks(APNs)and the influence of the Belt and Road Initiative(BRI).The results demonstrate that Asian countries account for a high and growing proportion,with a weak ability to capture value in global production networks(GPNs).The BRI has significantly strengthened production cooperation among Asian nations,promoting participation and strengthening abilities to capture value in GPNs.The continuous stability and strengthening of internal cooperation of APNs improves resilience from external shocks.Inside APNs,the proportions of East Asia and Southeast Asia show an overall downward trend,while South,West,and Central Asia show an increasing trend.China has also replaced Japan as the largest participant,and the rise of South Asian countries,led by India,has transformed APNs from a binary to a triple structure.In addition,China’s upstream degree index increased significantly,whereas Japan experienced the largest decline,causing a level of high-end vacancy in APNs.We propose that the most urgent task for the Asian countries to enhance APNs is to achieve stratified development and build more complete production circles.展开更多
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method...Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.展开更多
Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed an...Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.展开更多
Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results f...Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.展开更多
In order to improve production and operation ability of medium and small-sized enterprises, an assessment-index system of production and operation ability was proposed, and a corresponding assessment model was establi...In order to improve production and operation ability of medium and small-sized enterprises, an assessment-index system of production and operation ability was proposed, and a corresponding assessment model was established based on BP neural network. The conjunction weights of the neural network were continuously modified from output layer to input layer in the process of neural network training to reduce the errors between the anticipated and actual outputs. The results from an example show that this method is reliable and feasible. The production and operation abilitv of an enterorise with assessed result of 0.833 is fairly oowerful, and that with assessed result of 0.644 is average.展开更多
Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutua...Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.展开更多
As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this met...As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.展开更多
Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe...Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
This paper develops an extended newsboy model and presents a formula- tion for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be u...This paper develops an extended newsboy model and presents a formula- tion for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be used to describe the status of entrepreneurial network construction. We use the Lagrange multiplier procedure to deal with our problem, but it is too complicated to get the exact solu-tion. So we introduce the homotopy method to deal with it. We give the flow chart to describe how to get the solution via the homotopy method. We also illustrate our model in both the classical procedure and the homotopy method. Comparing the two methods, we can see that the homotopy method is more exact and efficient.展开更多
This paper presents the game of production technical standards between downstream and upstream suppliers on a manufacturing supply chain network when the two parties have different partnership densities, namely, the n...This paper presents the game of production technical standards between downstream and upstream suppliers on a manufacturing supply chain network when the two parties have different partnership densities, namely, the numbers of replaceable and mature manufacturing partners. We firstly constructed a manufacturing chain network and analyzed its three relationship structures among suppliers with the presence of different relationship densities, and found that all the three relationships brought about the game of production technical standards between partnership-rich and partnership-scanty suppliers. Then we built a two-party payoff matrix, and analyzed the two-party game and evolutionary stable strategy, based on replication dynamic equation and asymmetric evolutionary game theory. The evolutionary stable strategies of two parties under varying payoff parameters were validated through numerical simulation. Finally, we proposed some suggestions for both those manufacturers with more partners and fewer partners, respectively.展开更多
The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have p...The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types,using methods such as remote sensing or statistical metrological analysis.In this study,we characterize the agricultural production space(APS)by bipartite network connecting agricultural products and provinces,to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China.The results show that core products are cereal,pork,melon,and pome fruit;meanwhile the milk,grape,and fiber crop show an upward trend in centrality,which is in line with diet structure changes in China over the past decades.The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable.Additionally,identified provincial communities closely resemble China's agricultural natural zones.Furthermore,the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production.Despite the superior production capa-bilities of southern provinces,recent years have witnessed a notable decline,warranting special attentions.The findings provide a comprehensive perspective for understanding the complex relationship of agricultural prod-ucts'relatedness,production capabilities and production patterns,which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.展开更多
From the perspective of intra-product specialization and with in-depth analysis of trade statistics,this paper investigates the influence of China's rise on the East Asian production network.Our conclusions sugges...From the perspective of intra-product specialization and with in-depth analysis of trade statistics,this paper investigates the influence of China's rise on the East Asian production network.Our conclusions suggest that in integrating into the East Asian production network,China has gradually emerged as the manufacturing center of East Asia,weakening the regional influence of the Four Asian Tigers.Meanwhile,the competitive effect of China's rise has helped promote the specialization levels of the network's members and even the network as a whole.With cooperation in various processes of intra-product specialization,internal connections of the East Asian production network were further strengthened.In addition,China became an export platform of East Asia,transforming the export pattern of the East Asian production network to world markets from "bilateral trade" into "triangular trade," trade via China.展开更多
This paper presents not only practical but also instructive mathematical models to simulate tree network formation using the Poisson equation and the Finite Difference Method (FDM). Then, the implications for entropic...This paper presents not only practical but also instructive mathematical models to simulate tree network formation using the Poisson equation and the Finite Difference Method (FDM). Then, the implications for entropic theories are discussed from the viewpoint of Maximum Entropy Production (MEP). According to the MEP principle, open systems existing in the state far from equilibrium are stabilized when entropy production is maximized, creating dissipative structures with low entropy such as the tree-shaped network. We prepare two simulation models: one is the Poisson equation model that simulates the state far from equilibrium, and the other is the Laplace equation model that simulates the isolated state or the state near thermodynamic equilibrium. The output of these equations is considered to be positively correlated to entropy production of the system. Setting the Poisson equation model so that entropy production is maximized, tree network formation is advanced. We suppose that this is due to the invocation of the MEP principle, that is, entropy of the system is lowered by emitting maximal entropy out of the system. On the other hand, tree network formation is not observed in the Laplace equation model. Our simulation results will offer the persuasive evidence that certifies the effect of the MEP principle.展开更多
China apparel industry, which is deeply embedded in the global production network (GPN), faces two urgent issues, social upgrading and economic upgrading. The study of GPN places great emphasis on the two issues. Base...China apparel industry, which is deeply embedded in the global production network (GPN), faces two urgent issues, social upgrading and economic upgrading. The study of GPN places great emphasis on the two issues. Based on the survey of Ningbo apparel industry, four key components of decent work in China apparel industry are discussed. The role of buyers in promoting decent work in suppliers can't be neglected. There are significant correlations between business type and some indicators of decent work. Though the majority of the apparel firms are engaging in processing, more and more firms are involved in marketing and branding. The upgrading trajectory of China apparel industry leads to the economic and social performances.展开更多
This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating t...This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.展开更多
Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the ca...Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.展开更多
Under global production network,export cannot represent a country's gains from trade,and territory-based gains from trade refer to the remainder of export after deducting the input of intermediate goods and re-exp...Under global production network,export cannot represent a country's gains from trade,and territory-based gains from trade refer to the remainder of export after deducting the input of intermediate goods and re-export after value-added return.Ownership-based gains from trade refer to the remainder of territory-based gains from trade after further deducting the trade in value added(TVA) realized through the inflow of foreign factors.By creating a multicountry input and output model,this paper calculates the territory-based gains from trade,ownership-based gains from trade,and territory-based gains from trade for foreign countries realized through China's export,as well as valueadded return and territory-based gains from trade for foreign countries realized through China's import.This paper has arrived at the following conclusions:behind China's status as the largest goods exporting country in the world,most of Chinese exports contribute to the gains of foreign countries;value addition for foreign countries realized through China's export and value-added return realized through China's import mostly come from Taiwan region,Japan and South Korea in East Asia;a considerable part of gains from trade for the United States realized through China-US trade is achieved through indirect trade.展开更多
With the development of database and computer network technology, traditional TV news production mode (TVNPM) faces great challenge. Up to now, evolution of TVNPM has experienced two stages: In the beginning, TV news ...With the development of database and computer network technology, traditional TV news production mode (TVNPM) faces great challenge. Up to now, evolution of TVNPM has experienced two stages: In the beginning, TV news is produced completely by hand, named as pipelining TVNPM in this paper. This production mode is limited to space and time, so its production cycle is very time-consuming, and it requires a lot of harmony in different departments; Subsequently, thanks to applications of database technology, a new TVNPM appears, which is named as pooled information resource TVNPM. Compared with pipelining TVNPM, this mode promotes information sharing. However, with the development of network technology, especially the Intranet and the Internet, the pooled information resource TVNPM receives strong impact, and it is referred to contrive a new TVNPM. This new TVNPM must support information sharing, remote collaboration, and interaction in communications so as to improve group work efficiency. In this paper, we present such a new TVNPM, namely, Network TVNPM, give a suit of system solution to support the new TVNPM, introduce the new workflow, and in the end analyze the advantages of Network TVNPM.展开更多
基金funded by National Natural Science Foundation of China,grant number 62071491.
文摘Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.
基金Under the auspices of National Natural Science Foundation of China(No.42201188)。
文摘In an era of rising trade protectionism and frequent antiglobalization events,strengthening regional economic connections has important practical significance for resisting external economic shocks,improving economic resilience,and promoting regional economic development.Based on input-output analysis,value-added decomposition,and network analysis,this paper uses long-term,multiregional input-output data to measure the spatiotemporal patterns of Asian production networks(APNs)and the influence of the Belt and Road Initiative(BRI).The results demonstrate that Asian countries account for a high and growing proportion,with a weak ability to capture value in global production networks(GPNs).The BRI has significantly strengthened production cooperation among Asian nations,promoting participation and strengthening abilities to capture value in GPNs.The continuous stability and strengthening of internal cooperation of APNs improves resilience from external shocks.Inside APNs,the proportions of East Asia and Southeast Asia show an overall downward trend,while South,West,and Central Asia show an increasing trend.China has also replaced Japan as the largest participant,and the rise of South Asian countries,led by India,has transformed APNs from a binary to a triple structure.In addition,China’s upstream degree index increased significantly,whereas Japan experienced the largest decline,causing a level of high-end vacancy in APNs.We propose that the most urgent task for the Asian countries to enhance APNs is to achieve stratified development and build more complete production circles.
文摘Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.
基金Major Unified Construction Project of Petro China(2019-40210-000020-02)。
文摘Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.
文摘Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.
基金Project 2001FJJ036 supported by Society Science Foundation of Henan Province
文摘In order to improve production and operation ability of medium and small-sized enterprises, an assessment-index system of production and operation ability was proposed, and a corresponding assessment model was established based on BP neural network. The conjunction weights of the neural network were continuously modified from output layer to input layer in the process of neural network training to reduce the errors between the anticipated and actual outputs. The results from an example show that this method is reliable and feasible. The production and operation abilitv of an enterorise with assessed result of 0.833 is fairly oowerful, and that with assessed result of 0.644 is average.
文摘Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.
文摘As the conventional prediction methods for production of waterflooding reservoirs have some drawbacks, a production forecasting model based on artificial neural network was proposed, the simulation process by this method was presented, and some examples were illustrated. A workflow that involves a physics-based extraction of features was proposed for fluid production forecasting to improve the prediction effect. The Bayesian regularization algorithm was selected as the training algorithm of the model. This algorithm, although taking longer time, can better generalize oil, gas and water production data sets. The model was evaluated by calculating mean square error and determination coefficient, drawing error distribution histogram and the cross-plot between simulation data and verification data etc. The model structure was trained, validated and tested with 90% of the historical data, and blindly evaluated using the remaining. The predictive model consumes minimal information and computational cost and is capable of predicting fluid production rate with a coefficient of determination of more than 0.9, which has the simulation results consistent with the practical data.
基金This work was supported by the Pilot Seed Grant(Grant No.RES0049944)the Collaborative Research Project(Grant No.RES0043251)from the University of Alberta.
文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
文摘This paper develops an extended newsboy model and presents a formula- tion for this model. This new model has solved the budget contained multi-product newsboy problem with the reactive production. This model can be used to describe the status of entrepreneurial network construction. We use the Lagrange multiplier procedure to deal with our problem, but it is too complicated to get the exact solu-tion. So we introduce the homotopy method to deal with it. We give the flow chart to describe how to get the solution via the homotopy method. We also illustrate our model in both the classical procedure and the homotopy method. Comparing the two methods, we can see that the homotopy method is more exact and efficient.
基金Supported by the Science and Technology Development Strategy Research Project of Tianjin(13ZLZLZF08900)
文摘This paper presents the game of production technical standards between downstream and upstream suppliers on a manufacturing supply chain network when the two parties have different partnership densities, namely, the numbers of replaceable and mature manufacturing partners. We firstly constructed a manufacturing chain network and analyzed its three relationship structures among suppliers with the presence of different relationship densities, and found that all the three relationships brought about the game of production technical standards between partnership-rich and partnership-scanty suppliers. Then we built a two-party payoff matrix, and analyzed the two-party game and evolutionary stable strategy, based on replication dynamic equation and asymmetric evolutionary game theory. The evolutionary stable strategies of two parties under varying payoff parameters were validated through numerical simulation. Finally, we proposed some suggestions for both those manufacturers with more partners and fewer partners, respectively.
基金supported by the Institute of Atmospheric Environment,China Meteorological Administration,Shenyang(Grant No.2021SYIAEKFMS27)Key Laboratory of Farm Building in Structure and Construction,Ministry of Agriculture and Rural Affairs,P.R.China(Grant No.202003)the National Foundation of China Scholarship Council(Grant No.202206040102).
文摘The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types,using methods such as remote sensing or statistical metrological analysis.In this study,we characterize the agricultural production space(APS)by bipartite network connecting agricultural products and provinces,to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China.The results show that core products are cereal,pork,melon,and pome fruit;meanwhile the milk,grape,and fiber crop show an upward trend in centrality,which is in line with diet structure changes in China over the past decades.The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable.Additionally,identified provincial communities closely resemble China's agricultural natural zones.Furthermore,the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production.Despite the superior production capa-bilities of southern provinces,recent years have witnessed a notable decline,warranting special attentions.The findings provide a comprehensive perspective for understanding the complex relationship of agricultural prod-ucts'relatedness,production capabilities and production patterns,which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.
基金This research project received the support of "Research on the Creation of China Foreign Trade Innovation System" under the Research Program of Philosophical and Social Sciences of Shanghai (Approval No.:2007BJL009),together with Open Economy and Trade,which is a key research task of Shanghai Municipal Commission of Education.
文摘From the perspective of intra-product specialization and with in-depth analysis of trade statistics,this paper investigates the influence of China's rise on the East Asian production network.Our conclusions suggest that in integrating into the East Asian production network,China has gradually emerged as the manufacturing center of East Asia,weakening the regional influence of the Four Asian Tigers.Meanwhile,the competitive effect of China's rise has helped promote the specialization levels of the network's members and even the network as a whole.With cooperation in various processes of intra-product specialization,internal connections of the East Asian production network were further strengthened.In addition,China became an export platform of East Asia,transforming the export pattern of the East Asian production network to world markets from "bilateral trade" into "triangular trade," trade via China.
文摘This paper presents not only practical but also instructive mathematical models to simulate tree network formation using the Poisson equation and the Finite Difference Method (FDM). Then, the implications for entropic theories are discussed from the viewpoint of Maximum Entropy Production (MEP). According to the MEP principle, open systems existing in the state far from equilibrium are stabilized when entropy production is maximized, creating dissipative structures with low entropy such as the tree-shaped network. We prepare two simulation models: one is the Poisson equation model that simulates the state far from equilibrium, and the other is the Laplace equation model that simulates the isolated state or the state near thermodynamic equilibrium. The output of these equations is considered to be positively correlated to entropy production of the system. Setting the Poisson equation model so that entropy production is maximized, tree network formation is advanced. We suppose that this is due to the invocation of the MEP principle, that is, entropy of the system is lowered by emitting maximal entropy out of the system. On the other hand, tree network formation is not observed in the Laplace equation model. Our simulation results will offer the persuasive evidence that certifies the effect of the MEP principle.
基金Zhejiang Union of Social Science,China(No.08Z24)Science Foundation of Zhejiang Sci-Tech University,China(No.1105807-Y)
文摘China apparel industry, which is deeply embedded in the global production network (GPN), faces two urgent issues, social upgrading and economic upgrading. The study of GPN places great emphasis on the two issues. Based on the survey of Ningbo apparel industry, four key components of decent work in China apparel industry are discussed. The role of buyers in promoting decent work in suppliers can't be neglected. There are significant correlations between business type and some indicators of decent work. Though the majority of the apparel firms are engaging in processing, more and more firms are involved in marketing and branding. The upgrading trajectory of China apparel industry leads to the economic and social performances.
基金National Natural Science Foundation of China grants no.41972326 and 51774258.
文摘This study compared the predictive performance and processing speed of an artificial neural network(ANN)and a hybrid of a numerical reservoir simulation(NRS)and artificial neural network(NRS-ANN)models in estimating the oil production rate of the ZH86 reservoir block under waterflood recovery.The historical input variables:reservoir pressure,reservoir pore volume containing hydrocarbons,reservoir pore volume containing water and reservoir water injection rate used as inputs for ANN models.To create the NRS-ANN hybrid models,314 data sets extracted from the NRS model,which included reservoir pressure,reservoir pore volume containing hy-drocarbons,reservoir pore volume containing water and reservoir water injection rate were used.The output of the models was the historical oil production rate(HOPR in m^(3) per day)recorded from the ZH86 reservoir block.Models were developed using MATLAB R2021a and trained with 25 models in three replicate conditions(2,4 and 6),each at 1000 epochs.A comparative analysis indicated that,for all 25 models,the ANN outperformed the NRS-ANN in terms of processing speed and prediction performance.ANN models achieved an average of R^(2) and MAE of 0.8433 and 8.0964 m^(3)/day values,respectively,while NRS-ANN hybrid models achieved an average of R^(2) and MAE of 0.7828 and 8.2484 m^(3)/day values,respectively.In addition,ANN models achieved a processing speed of 49 epochs/sec,32 epochs/sec,and 24 epochs/sec after 2,4,and 6 replicates,respectively.Whereas the NRS-ANN hybrid models achieved lower average processing speeds of 45 epochs/sec,23 epochs/sec and 20 epochs/sec.In addition,the ANN optimal model outperforms the NRS-ANN model in terms of both processing speed and accuracy.The ANN optimal model achieved a speed of 336.44 epochs/sec,compared to the NRS-ANN hybrid optimal model,which achieved a speed of 52.16 epochs/sec.The ANN optimal model achieved lower RMSE and MAE values of 7.9291 m^(3)/day and 5.3855 m^(3)/day in the validation dataset compared with the hybrid ANS optimal model,which achieved 13.6821 m^(3)/day and 9.2047 m^(3)/day,respectively.The study also showed that the ANN optimal model consistently achieved higher R^(2) values:0.9472,0.9284 and 0.9316 in the training,test and validation data sets.Whereas the NRS-ANN hybrid optimal yielded lower R^(2) values of 0.8030,0.8622 and 0.7776 for the training,testing and validation datasets.The study showed that ANN models are a more effective and reliable tool,as they balance both processing speed and accuracy in estimating the oil production rate of the ZH86 reservoir block under the waterflooding recovery method.
文摘Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.
基金the outcome of major program of the National Social Sciences Foundation Research on the Upgraded Objectives and Strategic Innovation for the Transformation and Development of Major Trading Nations(Grant No.13&ZD048)
文摘Under global production network,export cannot represent a country's gains from trade,and territory-based gains from trade refer to the remainder of export after deducting the input of intermediate goods and re-export after value-added return.Ownership-based gains from trade refer to the remainder of territory-based gains from trade after further deducting the trade in value added(TVA) realized through the inflow of foreign factors.By creating a multicountry input and output model,this paper calculates the territory-based gains from trade,ownership-based gains from trade,and territory-based gains from trade for foreign countries realized through China's export,as well as valueadded return and territory-based gains from trade for foreign countries realized through China's import.This paper has arrived at the following conclusions:behind China's status as the largest goods exporting country in the world,most of Chinese exports contribute to the gains of foreign countries;value addition for foreign countries realized through China's export and value-added return realized through China's import mostly come from Taiwan region,Japan and South Korea in East Asia;a considerable part of gains from trade for the United States realized through China-US trade is achieved through indirect trade.
文摘With the development of database and computer network technology, traditional TV news production mode (TVNPM) faces great challenge. Up to now, evolution of TVNPM has experienced two stages: In the beginning, TV news is produced completely by hand, named as pipelining TVNPM in this paper. This production mode is limited to space and time, so its production cycle is very time-consuming, and it requires a lot of harmony in different departments; Subsequently, thanks to applications of database technology, a new TVNPM appears, which is named as pooled information resource TVNPM. Compared with pipelining TVNPM, this mode promotes information sharing. However, with the development of network technology, especially the Intranet and the Internet, the pooled information resource TVNPM receives strong impact, and it is referred to contrive a new TVNPM. This new TVNPM must support information sharing, remote collaboration, and interaction in communications so as to improve group work efficiency. In this paper, we present such a new TVNPM, namely, Network TVNPM, give a suit of system solution to support the new TVNPM, introduce the new workflow, and in the end analyze the advantages of Network TVNPM.
