Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. ...Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. So it is believable that integrating the knowledge embedded in data and those possessed by experts can lead to a superior modeling approach. Aiming at the classification problems, a novel integrated knowledge-based modeling methodology, oriented by experts and driven by data, is proposed. It starts from experts identifying modeling parameters, and then the input space is partitioned followed by fuzzification. Afterwards, single rules are generated and then aggregated to form a rule base, on which a fuzzy inference mechanism is proposed. The experts are allowed to make necessary changes on the rule base to improve the model accuracy. A real-world application, welding fault diagnosis, is presented to demonstrate the effectiveness of the methodology.展开更多
Background:Health policy formulations in India have witnessed a shift from a reactive approach to a more proactive approach over the last decade.It is therefore important to understand the effectiveness of recent nati...Background:Health policy formulations in India have witnessed a shift from a reactive approach to a more proactive approach over the last decade.It is therefore important to understand the effectiveness of recent national health policies(such as the National Rural Health Mission and the National Urban Health Mission)in addressing the varied needs of the heterogeneous population of India.Methods:We use datasets from the National Sample Surveys carried out in 2004 and 2014 to understand the change in the health seeking behavior as a result of these policies.The choice of health care facilities and the associated expenditures are compared through descriptive analyses.A multinomial logistic regression is used to identify the significant parameters which contribute towards the share of health care providers in India.The health status of two economically disparate Indian states(Bihar and Kerala)are also compared through specific metrics of performance.Results:It is seen that due to increased availability of facilities in close proximity,both rural and urban residents prefer to avail of those facilities which will result in minimization of transportation cost.The effectiveness of national health policies is found to vary on a regional scale.Literacy and health status have a strong correlation,thereby reinforcing that Bihar still lags far behind Kerala in terms of access to equitable health care.Conclusion:Therefore,a hierarchical system,incorporating medical pluralism and tailor-made policies targeted at diverse health care demands,needs to be put in place to achieve Goal 3 of the Sustainable Development Goals as decreed by the United Nations,i.e.,“health for all”.展开更多
Compromised integrity of cementitious materials can lead to potential geo-hazards such as detrimental fluid flow to the wellbore(borehole),potential leakage of underground stored fluids,contamination of water aquifers...Compromised integrity of cementitious materials can lead to potential geo-hazards such as detrimental fluid flow to the wellbore(borehole),potential leakage of underground stored fluids,contamination of water aquifers,and other issues that could impact environmental sustainability during underground construction operations.The mechanical integrity of wellbore cementitious materials is critical to prevent wellbore failure and leakages,and thus,it is imperative to understand and predict the integrity of oilwell cement(OWC)and microbial-induced calcite precipitation(MICP)to maintain wellbore integrity and ensure zonal isolation at depth.Here,we investigated the mechanical integrity of two cementitious materials(MICP and OWC),and assessed their potential for plugging leakages around the wellbore.Further,we applied Machine Learning(ML)models to upscale and predict near-wellbore mechanical integrity at macro-scale by adopting two ML algorithms,Artificial Neural Network(ANN)and Random Forest(RF),using 100 datasets(containing 100 observations).Fractured portions of rock specimens were treated with MICP and OWC,respectively,and their resultant mechanical integrity(unconfined compressive strength,UCS;fracture toughness,K_(s))were evaluated using experimental mechanical tests and ML models.The experimental results showed that although OWC(average UCS=97 MPa,K_(s)=4.3 MPa·√m)has higher mechanical integrity over MICP(average UCS=86 MPa,K_(s)=3.6 MPa·√m),the MICP showed an edge over OWC in sealing microfractures and micro-leakage pathways.Also,the OWC can provide a greater near-wellbore seal than MICP for casing-cement or cement-formation delamination with relatively greater mechanical integrity.The results show that the degree of correlation between the mechanical integrity obtained from lab tests and the ML predictions is high.The best ML algorithm to predict the macro-scale mechanical integrity of a MICP-cemented specimen is the RF model(R^(2)for UCS=0.9738 and K_(s)=0.9988;MAE for UCS=1.04 MPa and K_(s)=0.02 MPa·√m).Similarly,for OWC-cemented specimen,the best ML algorithm to predict their macro-scale mechanical integrity is the RF model(R^(2)for UCS=0.9984 and K_(s)=0.9996;MAE for UCS=0.5 MPa and K_(s)=0.01 MPa·√m).This study provides insights into the potential of MICP and OWC as near-wellbore ce-mentitious materials and the applicability of ML model for evaluating and predicting the mechanical integrity of cementitious materials used in near-wellbore to achieve efficient geo-hazard mitigation and environmental protection in engineering and underground operations.展开更多
The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating,and weather-dependent power flows.To ensure grid stability,grid operators rely on p...The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating,and weather-dependent power flows.To ensure grid stability,grid operators rely on power forecasts which are crucial for grid calculations and planning.In this paper,a Multi-Task Learning approach is combined with a Graph Neural Network(GNN)to predict vertical power flows at transformers connecting high and extra-high voltage levels.The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach.The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished.At the same time,dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other.The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators,comprising large portions of the operated German transmission grid.The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer.It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks.A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.展开更多
基金partially supported by the Overseas Research Scholar Fund from Zhejiang University of Technology.
文摘Knowledge discovery from data directly can hardly avoid the fact that it is biased towards the collected experimental data, whereas, expert systems are always baffled with the manual knowledge acquisition bottleneck. So it is believable that integrating the knowledge embedded in data and those possessed by experts can lead to a superior modeling approach. Aiming at the classification problems, a novel integrated knowledge-based modeling methodology, oriented by experts and driven by data, is proposed. It starts from experts identifying modeling parameters, and then the input space is partitioned followed by fuzzification. Afterwards, single rules are generated and then aggregated to form a rule base, on which a fuzzy inference mechanism is proposed. The experts are allowed to make necessary changes on the rule base to improve the model accuracy. A real-world application, welding fault diagnosis, is presented to demonstrate the effectiveness of the methodology.
文摘Background:Health policy formulations in India have witnessed a shift from a reactive approach to a more proactive approach over the last decade.It is therefore important to understand the effectiveness of recent national health policies(such as the National Rural Health Mission and the National Urban Health Mission)in addressing the varied needs of the heterogeneous population of India.Methods:We use datasets from the National Sample Surveys carried out in 2004 and 2014 to understand the change in the health seeking behavior as a result of these policies.The choice of health care facilities and the associated expenditures are compared through descriptive analyses.A multinomial logistic regression is used to identify the significant parameters which contribute towards the share of health care providers in India.The health status of two economically disparate Indian states(Bihar and Kerala)are also compared through specific metrics of performance.Results:It is seen that due to increased availability of facilities in close proximity,both rural and urban residents prefer to avail of those facilities which will result in minimization of transportation cost.The effectiveness of national health policies is found to vary on a regional scale.Literacy and health status have a strong correlation,thereby reinforcing that Bihar still lags far behind Kerala in terms of access to equitable health care.Conclusion:Therefore,a hierarchical system,incorporating medical pluralism and tailor-made policies targeted at diverse health care demands,needs to be put in place to achieve Goal 3 of the Sustainable Development Goals as decreed by the United Nations,i.e.,“health for all”.
文摘Compromised integrity of cementitious materials can lead to potential geo-hazards such as detrimental fluid flow to the wellbore(borehole),potential leakage of underground stored fluids,contamination of water aquifers,and other issues that could impact environmental sustainability during underground construction operations.The mechanical integrity of wellbore cementitious materials is critical to prevent wellbore failure and leakages,and thus,it is imperative to understand and predict the integrity of oilwell cement(OWC)and microbial-induced calcite precipitation(MICP)to maintain wellbore integrity and ensure zonal isolation at depth.Here,we investigated the mechanical integrity of two cementitious materials(MICP and OWC),and assessed their potential for plugging leakages around the wellbore.Further,we applied Machine Learning(ML)models to upscale and predict near-wellbore mechanical integrity at macro-scale by adopting two ML algorithms,Artificial Neural Network(ANN)and Random Forest(RF),using 100 datasets(containing 100 observations).Fractured portions of rock specimens were treated with MICP and OWC,respectively,and their resultant mechanical integrity(unconfined compressive strength,UCS;fracture toughness,K_(s))were evaluated using experimental mechanical tests and ML models.The experimental results showed that although OWC(average UCS=97 MPa,K_(s)=4.3 MPa·√m)has higher mechanical integrity over MICP(average UCS=86 MPa,K_(s)=3.6 MPa·√m),the MICP showed an edge over OWC in sealing microfractures and micro-leakage pathways.Also,the OWC can provide a greater near-wellbore seal than MICP for casing-cement or cement-formation delamination with relatively greater mechanical integrity.The results show that the degree of correlation between the mechanical integrity obtained from lab tests and the ML predictions is high.The best ML algorithm to predict the macro-scale mechanical integrity of a MICP-cemented specimen is the RF model(R^(2)for UCS=0.9738 and K_(s)=0.9988;MAE for UCS=1.04 MPa and K_(s)=0.02 MPa·√m).Similarly,for OWC-cemented specimen,the best ML algorithm to predict their macro-scale mechanical integrity is the RF model(R^(2)for UCS=0.9984 and K_(s)=0.9996;MAE for UCS=0.5 MPa and K_(s)=0.01 MPa·√m).This study provides insights into the potential of MICP and OWC as near-wellbore ce-mentitious materials and the applicability of ML model for evaluating and predicting the mechanical integrity of cementitious materials used in near-wellbore to achieve efficient geo-hazard mitigation and environmental protection in engineering and underground operations.
文摘The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating,and weather-dependent power flows.To ensure grid stability,grid operators rely on power forecasts which are crucial for grid calculations and planning.In this paper,a Multi-Task Learning approach is combined with a Graph Neural Network(GNN)to predict vertical power flows at transformers connecting high and extra-high voltage levels.The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach.The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished.At the same time,dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other.The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators,comprising large portions of the operated German transmission grid.The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer.It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks.A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.
