Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation to...Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools.In this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model.The proposed ensemble model is composed of two levels of regression models.The first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting machine.Whereas the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal result.To achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each model.Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset.The findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.展开更多
Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the cl...Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.Methods:We enrolled 160 patients with epithelial ovarian cancer as the experimental group,and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group.We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens(EpCAM and MUC1),and used multiple reverse transcription-polymerase chain reaction(RT-PCR)detecting three markers(EpCAM,MUC1 and WT1)for quantification.And then we used a binary logistic regression analysis and focused on EpCAM,MUC1 and WT1 to establish an optimized CTC detection model.Results:The sensitivity and specificity of the optimized model is 79.4%and 92.2%,respectively.The specificity of the CTC detection model is significantly higher than CA125(92.2%vs.82.2%,P=0.044),and the detection rate of CTCs was higher than the positive rate of CA125(74.5%vs.58.2%,P=0.069)in early-stage patients(stage I and II).The detection rate of CTCs was significantly higher in patients with ascitic volume≥500 mL,suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy(P<0.05).The detection rate of CTC;and CTC;was significantly higher in chemo-resistant patients(26.3%vs.11.9%;26.4%vs.13.4%,P<0.05).The median progression-free survival time for CTC;patients trended to be longer than CTC;patients,and overall survival was shorter in CTC;patients(P=0.043).Conclusions:Our study presents an optimized detection model for CTCs,which consists of the expression levels of three markers(EpCAM,MUC1 and WT1).In comparison with CA125,our model has high specificity and demonstrates better diagnostic values,especially for early-stage ovarian cancer.Detection of CTC;and CTC;had predictive value for chemotherapy resistance,and the detection of CTC;suggested poor prognosis.展开更多
Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these...Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.展开更多
To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item ...Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.展开更多
This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction...This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge.In this study,CNN architectures such as VGG-16,VGG-19,RestNet50,RestNet18 are compared,and an optimized model for feature extraction in X-ray images from various domains invol-ving several classes is proposed.An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19(Negative or Positive).Then,2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models.Among those,the optimized model architecture classifier technique achieves higher accuracy(0.97)than four other models,specifically VGG-16,VGG-19,RestNet18,and RestNet50(0.96,0.72,0.91,and 0.93,respectively).Therefore,this study will enable radiol-ogists to more efficiently and effectively classify a patient’s coronavirus disease.展开更多
In Internet of Things (IoT), large amount of data are processed andcommunicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integrat...In Internet of Things (IoT), large amount of data are processed andcommunicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration ofIoT and Artificial Intelligence (AI). The amalgamation of above mentioned toolshas taken the new peak in terms of diagnosis and treatment process especially inthe pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paperproposes a novel optimized cognitive learning based BAN model based on FogIoT technology as a real-time health monitoring systems with the increased network-life time. Energy and latency aware features of BAN have been extractedand used to train the proposed fog based learning algorithm to achieve low energyconsumption and low-latency scheduling algorithm. To test the proposed network,Fog-IoT-BAN test bed has been developed with the battery driven MICOTTboards interfaced with the health care sensors using Micro Python programming.The extensive experimentation is carried out using the above test beds and variousparameters such as accuracy, precision, recall, F1score and specificity has beencalculated along with QoS (quality of service) parameters such as latency, energyand throughput. To prove the superiority of the proposed framework, the performance of the proposed learning based framework has been compared with theother state-of-art classical learning frameworks and other existing Fog-BAN networks such as WORN, DARE, L-No-DEAF networks. Results proves the proposed framework has outperformed the other classical learning models in termsof accuracy and high False Alarm Rate (FAR), energy efficiency and latency.展开更多
A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future e...A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.展开更多
In order to ensure the effective analysis and reconstruction of forests,it is key to ensure the quantitative description of their spatial structure.In this paper,a distance model for the optimal stand spatial structur...In order to ensure the effective analysis and reconstruction of forests,it is key to ensure the quantitative description of their spatial structure.In this paper,a distance model for the optimal stand spatial structure based on weighted Voronoi diagrams is proposed.In particular,we provide a novel methodological model for the comprehensive evaluation of the spatial structure of forest stands in natural mixed conifer-broadleaved forests and the formulation of management decision plans.The applicability of the rank evaluation and the optimal solution distance model are compared and assessed for different standard sample plots of natural mixed conifer-broadleaved forests.The effect of crown width on the spatial structure unit of the trees is observed to be higher than that of the diameter at breast height.Moreover,the influence of crown length is greater than that of tree height.There are nine possible spatial structure units determined by the weighted Voronoi diagram for the number of neighboring trees in the central tree,with an average intersection of neighboring crowns reaching 80%.The rank rating of natural forest sample plots is correlated with the optimal solution distance model,and their results are generally consistent for natural forests.However,the rank rating is not able to provide a quantitative assessment.The optimal solution distance model is observed to be more comprehensive than traditional methods for the evaluation of the spatial structure of forest stands.It can effectively reflect the trends in realistic stand spatial structure factors close to or far from the ideal structure point,and accurately assesses the forest spatial structure.The proposed optimal solution distance model improves the integrated evaluation of the spatial structure of forest stands and provides solid theoretical and technical support for sustainable forest management.展开更多
To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an impr...To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance.展开更多
In response to the main problems in commonly used model selection methods,a method was proposed to apply the concept of experimental design to the optimization of uncertain reservoir models.Firstly,based on the actual...In response to the main problems in commonly used model selection methods,a method was proposed to apply the concept of experimental design to the optimization of uncertain reservoir models.Firstly,based on the actual situation of the oil field,the uncertain variables were determined that affect the geological reserves of the model and their possible range of variation,and experimental design was used to determine the modeling plan.Then,multiple geological models were established and reserves were calculated,and multiple regression was performed between uncertain variables and the corresponding geological reserves of the model.Finally,Monte Carlo simulation technology was applied to determine the parameters of the P10,P50,and P90 models for probabilistic reserves,and P10,P50,and P90 models were established.This method is not only more objective and time-saving in the application process,but also can determine the main geological variables that affect geological reserves,providing a new idea for evaluating the uncertainty of geological reserves.展开更多
Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monito...Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.展开更多
Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential malfunctioning.In recent years,to relieve the manual efforts by human experts,models based ...Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential malfunctioning.In recent years,to relieve the manual efforts by human experts,models based on deep learning have been introduced to automatically identify potential defects.However,these models are insufficient in terms of dataset complexity,model versatility and performance.Our work addresses these issues with amulti-stage defect detection architecture using a composite backbone Swin Transformer.Themodel based on this architecture is trained using a more comprehensive dataset containingmore classes of defects.By ablation studies on the modules of combined backbone Swin Transformer,multi-stage detector,test-time data augmentation and model fusion,it is revealed that they all contribute to the improvement of detection accuracy from different aspects.The model incorporating all these modules achieves the mean Average Precision(mAP)of 78.6% at an Intersection over Union(IoU)threshold of 0.5.This represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network(R-CNN)model and a 6.7% improvement over You Only Look Once version 6(YOLOv6)-large,the highest in the YOLO methods.In addition,for other defect detection models for sewer pipes,although direct comparison with themis infeasible due to the unavailability of their private datasets,our results are obtained from a more comprehensive dataset and have superior generalization capabilities.展开更多
The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience ...The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.展开更多
Compressed earth blocks (CEB) are an alternative to cement blocks in the construction of wall masonry. However, the optimal architectural construction methods for adequate thermal comfort for occupants in hot and arid...Compressed earth blocks (CEB) are an alternative to cement blocks in the construction of wall masonry. However, the optimal architectural construction methods for adequate thermal comfort for occupants in hot and arid environments are not mastered. This article evaluates the influence of architectural and constructive modes of buildings made of CEB walls and concrete block walls, to optimize and compare their thermal comfort in the hot and dry tropical climate of Ouagadougou, Burkina Faso. Two identical pilot buildings whose envelopes are made of CEB and concrete blocks were monitored for this study. The thermal models of the pilot buildings were implemented in the SketchUp software using an extension of EnergyPlus. The models were empirically validated after calibration against measured thermal data from the buildings. The models were used to do a parametric analysis for optimization of the thermal performances by simulating plaster coatings on the exterior of walls, airtight openings and natural ventilation depending on external weather conditions. The results show that the CEB building displays 7016 hours of discomfort, equivalent to 80.1% of the time, and the concrete building displays 6948 hours of discomfort, equivalent to 79.3% of the time. The optimization by modifications reduced the discomfort to 2918 and 3125 hours respectively;i.e. equivalent to only 33.3% for the CEB building and 35.7% for the concrete building. More study should evaluate thermal optimizations in buildings in real time of usage such as residential buildings commonly used by the local middle class. The use of CEB as a construction material and passive means of improving thermal comfort is a suitable ecological and economical option to replace cementitious material.展开更多
The structure of laminar cooling control system for hot rolling was introduced and the control mode, cooling strategy, segment tracking and model recalculation were analyzed. The parameters of air/water cooling models...The structure of laminar cooling control system for hot rolling was introduced and the control mode, cooling strategy, segment tracking and model recalculation were analyzed. The parameters of air/water cooling models were optimized by regressing the data gathering in situ, and satisfactory effect was obtained. The coiling temperature can be controlled within ±15℃.展开更多
A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computation...A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.展开更多
Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
An improved multiple car-following model is proposed by considering the arbitrary number of preceding cars, which includes both the headway and the velocity difference of multiple preceding cars. The stability conditi...An improved multiple car-following model is proposed by considering the arbitrary number of preceding cars, which includes both the headway and the velocity difference of multiple preceding cars. The stability condition of the extended model is obtained by using the linear stability theory. The modified Korteweg-de Vries equation is derived to describe the traffic behaviour near the critical point by applying the nonlinear analysis. Traffic flow can be also divided into three regions: stable metastable and unstable regions. Numerical simulation is in accordance with the analytical result for the model. And numerical simulation shows that the stabilisation of traffic is increasing by considering the information of more leading cars and there is unavoidable effect on traffic flow from the multiple leading cars information.展开更多
The objective and constraint functions related to structural optimization designs are classified into economic and performance indexes in this paper.The influences of their different roles in model construction of str...The objective and constraint functions related to structural optimization designs are classified into economic and performance indexes in this paper.The influences of their different roles in model construction of structural topology optimization are also discussed.Furthermore,two structural topology optimization models,optimizing a performance index under the limitation of an economic index,represented by the minimum compliance with a volume constraint(MCVC)model,and optimizing an economic index under the limitation of a performance index,represented by the minimum weight with a displacement constraint(MWDC)model,are presented.Based on a comparison of numerical example results,the conclusions can be summarized as follows:(1)under the same external loading and displacement performance conditions,the results of the MWDC model are almost equal to those of the MCVC model;(2)the MWDC model overcomes the difficulties and shortcomings of the MCVC model;this makes the MWDC model more feasible in model construction;(3)constructing a model of minimizing an economic index under the limitations of performance indexes is better at meeting the needs of practical engineering problems and completely satisfies safety and economic requirements in mechanical engineering,which have remained unchanged since the early days of mechanical engineering.展开更多
基金The authors received funding for this study from the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IFP2021-033).
文摘Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools.In this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model.The proposed ensemble model is composed of two levels of regression models.The first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting machine.Whereas the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal result.To achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each model.Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset.The findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.
文摘Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.Methods:We enrolled 160 patients with epithelial ovarian cancer as the experimental group,and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group.We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens(EpCAM and MUC1),and used multiple reverse transcription-polymerase chain reaction(RT-PCR)detecting three markers(EpCAM,MUC1 and WT1)for quantification.And then we used a binary logistic regression analysis and focused on EpCAM,MUC1 and WT1 to establish an optimized CTC detection model.Results:The sensitivity and specificity of the optimized model is 79.4%and 92.2%,respectively.The specificity of the CTC detection model is significantly higher than CA125(92.2%vs.82.2%,P=0.044),and the detection rate of CTCs was higher than the positive rate of CA125(74.5%vs.58.2%,P=0.069)in early-stage patients(stage I and II).The detection rate of CTCs was significantly higher in patients with ascitic volume≥500 mL,suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy(P<0.05).The detection rate of CTC;and CTC;was significantly higher in chemo-resistant patients(26.3%vs.11.9%;26.4%vs.13.4%,P<0.05).The median progression-free survival time for CTC;patients trended to be longer than CTC;patients,and overall survival was shorter in CTC;patients(P=0.043).Conclusions:Our study presents an optimized detection model for CTCs,which consists of the expression levels of three markers(EpCAM,MUC1 and WT1).In comparison with CA125,our model has high specificity and demonstrates better diagnostic values,especially for early-stage ovarian cancer.Detection of CTC;and CTC;had predictive value for chemotherapy resistance,and the detection of CTC;suggested poor prognosis.
基金This work was supported by the National Nature Science Foundation of China(No.61503423,H.P.Jiang).The URL is http://www.nsfc.gov.cn/.
文摘Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
基金The Key Project of the National Ninth-Five-Year Plan No. 96-004-02-09The 48Project of Ministry of Water Resources No. 985106The Project of Chinese Academy of Sciences
文摘Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.
文摘This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge.In this study,CNN architectures such as VGG-16,VGG-19,RestNet50,RestNet18 are compared,and an optimized model for feature extraction in X-ray images from various domains invol-ving several classes is proposed.An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19(Negative or Positive).Then,2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models.Among those,the optimized model architecture classifier technique achieves higher accuracy(0.97)than four other models,specifically VGG-16,VGG-19,RestNet18,and RestNet50(0.96,0.72,0.91,and 0.93,respectively).Therefore,this study will enable radiol-ogists to more efficiently and effectively classify a patient’s coronavirus disease.
文摘In Internet of Things (IoT), large amount of data are processed andcommunicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration ofIoT and Artificial Intelligence (AI). The amalgamation of above mentioned toolshas taken the new peak in terms of diagnosis and treatment process especially inthe pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paperproposes a novel optimized cognitive learning based BAN model based on FogIoT technology as a real-time health monitoring systems with the increased network-life time. Energy and latency aware features of BAN have been extractedand used to train the proposed fog based learning algorithm to achieve low energyconsumption and low-latency scheduling algorithm. To test the proposed network,Fog-IoT-BAN test bed has been developed with the battery driven MICOTTboards interfaced with the health care sensors using Micro Python programming.The extensive experimentation is carried out using the above test beds and variousparameters such as accuracy, precision, recall, F1score and specificity has beencalculated along with QoS (quality of service) parameters such as latency, energyand throughput. To prove the superiority of the proposed framework, the performance of the proposed learning based framework has been compared with theother state-of-art classical learning frameworks and other existing Fog-BAN networks such as WORN, DARE, L-No-DEAF networks. Results proves the proposed framework has outperformed the other classical learning models in termsof accuracy and high False Alarm Rate (FAR), energy efficiency and latency.
基金supported by Global Energy Interconnection Group Co.,Ltd.:Assessment of China’s carbon neutrality implementation path and simulation research on policy tool combination(SGGEIG00JYJS2200059).
文摘A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.
基金funded by National Key Research and development project(2022YFD2201001)。
文摘In order to ensure the effective analysis and reconstruction of forests,it is key to ensure the quantitative description of their spatial structure.In this paper,a distance model for the optimal stand spatial structure based on weighted Voronoi diagrams is proposed.In particular,we provide a novel methodological model for the comprehensive evaluation of the spatial structure of forest stands in natural mixed conifer-broadleaved forests and the formulation of management decision plans.The applicability of the rank evaluation and the optimal solution distance model are compared and assessed for different standard sample plots of natural mixed conifer-broadleaved forests.The effect of crown width on the spatial structure unit of the trees is observed to be higher than that of the diameter at breast height.Moreover,the influence of crown length is greater than that of tree height.There are nine possible spatial structure units determined by the weighted Voronoi diagram for the number of neighboring trees in the central tree,with an average intersection of neighboring crowns reaching 80%.The rank rating of natural forest sample plots is correlated with the optimal solution distance model,and their results are generally consistent for natural forests.However,the rank rating is not able to provide a quantitative assessment.The optimal solution distance model is observed to be more comprehensive than traditional methods for the evaluation of the spatial structure of forest stands.It can effectively reflect the trends in realistic stand spatial structure factors close to or far from the ideal structure point,and accurately assesses the forest spatial structure.The proposed optimal solution distance model improves the integrated evaluation of the spatial structure of forest stands and provides solid theoretical and technical support for sustainable forest management.
文摘To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance.
基金supported by Tangshan Normal University Scientific Research Fund Project (2019A08)Hebei Provincial Natural Science Youth Fund Project (D2022105002).
文摘In response to the main problems in commonly used model selection methods,a method was proposed to apply the concept of experimental design to the optimization of uncertain reservoir models.Firstly,based on the actual situation of the oil field,the uncertain variables were determined that affect the geological reserves of the model and their possible range of variation,and experimental design was used to determine the modeling plan.Then,multiple geological models were established and reserves were calculated,and multiple regression was performed between uncertain variables and the corresponding geological reserves of the model.Finally,Monte Carlo simulation technology was applied to determine the parameters of the P10,P50,and P90 models for probabilistic reserves,and P10,P50,and P90 models were established.This method is not only more objective and time-saving in the application process,but also can determine the main geological variables that affect geological reserves,providing a new idea for evaluating the uncertainty of geological reserves.
文摘Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.
基金supported by the Science and Technology Development Fund of Macao(Grant No.0079/2019/AMJ)the National Key R&D Program of China(No.2019YFE0111400).
文摘Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential malfunctioning.In recent years,to relieve the manual efforts by human experts,models based on deep learning have been introduced to automatically identify potential defects.However,these models are insufficient in terms of dataset complexity,model versatility and performance.Our work addresses these issues with amulti-stage defect detection architecture using a composite backbone Swin Transformer.Themodel based on this architecture is trained using a more comprehensive dataset containingmore classes of defects.By ablation studies on the modules of combined backbone Swin Transformer,multi-stage detector,test-time data augmentation and model fusion,it is revealed that they all contribute to the improvement of detection accuracy from different aspects.The model incorporating all these modules achieves the mean Average Precision(mAP)of 78.6% at an Intersection over Union(IoU)threshold of 0.5.This represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network(R-CNN)model and a 6.7% improvement over You Only Look Once version 6(YOLOv6)-large,the highest in the YOLO methods.In addition,for other defect detection models for sewer pipes,although direct comparison with themis infeasible due to the unavailability of their private datasets,our results are obtained from a more comprehensive dataset and have superior generalization capabilities.
基金National Natural Science Foundation of China under Grant Nos.U1939210 and 51825801。
文摘The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.
文摘Compressed earth blocks (CEB) are an alternative to cement blocks in the construction of wall masonry. However, the optimal architectural construction methods for adequate thermal comfort for occupants in hot and arid environments are not mastered. This article evaluates the influence of architectural and constructive modes of buildings made of CEB walls and concrete block walls, to optimize and compare their thermal comfort in the hot and dry tropical climate of Ouagadougou, Burkina Faso. Two identical pilot buildings whose envelopes are made of CEB and concrete blocks were monitored for this study. The thermal models of the pilot buildings were implemented in the SketchUp software using an extension of EnergyPlus. The models were empirically validated after calibration against measured thermal data from the buildings. The models were used to do a parametric analysis for optimization of the thermal performances by simulating plaster coatings on the exterior of walls, airtight openings and natural ventilation depending on external weather conditions. The results show that the CEB building displays 7016 hours of discomfort, equivalent to 80.1% of the time, and the concrete building displays 6948 hours of discomfort, equivalent to 79.3% of the time. The optimization by modifications reduced the discomfort to 2918 and 3125 hours respectively;i.e. equivalent to only 33.3% for the CEB building and 35.7% for the concrete building. More study should evaluate thermal optimizations in buildings in real time of usage such as residential buildings commonly used by the local middle class. The use of CEB as a construction material and passive means of improving thermal comfort is a suitable ecological and economical option to replace cementitious material.
基金ItemSponsored by National Natural Science Foundation of China (50104004)
文摘The structure of laminar cooling control system for hot rolling was introduced and the control mode, cooling strategy, segment tracking and model recalculation were analyzed. The parameters of air/water cooling models were optimized by regressing the data gathering in situ, and satisfactory effect was obtained. The coiling temperature can be controlled within ±15℃.
基金funded by“The Pearl River Talent Recruitment Program”in 2019 for Professor Shui-Long Shen(Grant No.2019CX01G338),Guangdong Provincethe Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019)。
文摘A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.
文摘Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
基金Project supported by the Natural Science Foundation of Hunan Province,China (Grant No. 07JJ6106)the Important Project of Scientific Research Foundation of Hunan University of Arts and Science,China (Grant No. JJZD0902)the Fund of the 11th Five-year Plan for Key Construction Academic Subject of Hunan Province,China (Grant No. 06GXCD02)
文摘An improved multiple car-following model is proposed by considering the arbitrary number of preceding cars, which includes both the headway and the velocity difference of multiple preceding cars. The stability condition of the extended model is obtained by using the linear stability theory. The modified Korteweg-de Vries equation is derived to describe the traffic behaviour near the critical point by applying the nonlinear analysis. Traffic flow can be also divided into three regions: stable metastable and unstable regions. Numerical simulation is in accordance with the analytical result for the model. And numerical simulation shows that the stabilisation of traffic is increasing by considering the information of more leading cars and there is unavoidable effect on traffic flow from the multiple leading cars information.
基金supported by the National Natural Science Foundation of China(Grant 11172013)
文摘The objective and constraint functions related to structural optimization designs are classified into economic and performance indexes in this paper.The influences of their different roles in model construction of structural topology optimization are also discussed.Furthermore,two structural topology optimization models,optimizing a performance index under the limitation of an economic index,represented by the minimum compliance with a volume constraint(MCVC)model,and optimizing an economic index under the limitation of a performance index,represented by the minimum weight with a displacement constraint(MWDC)model,are presented.Based on a comparison of numerical example results,the conclusions can be summarized as follows:(1)under the same external loading and displacement performance conditions,the results of the MWDC model are almost equal to those of the MCVC model;(2)the MWDC model overcomes the difficulties and shortcomings of the MCVC model;this makes the MWDC model more feasible in model construction;(3)constructing a model of minimizing an economic index under the limitations of performance indexes is better at meeting the needs of practical engineering problems and completely satisfies safety and economic requirements in mechanical engineering,which have remained unchanged since the early days of mechanical engineering.