BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the e...BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.展开更多
At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns st...At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.展开更多
Aiming at working out more accurate pollutant degradation coefficient of the deep tunnel system, this work puts forward a novel optimized algorithm to calibrate such coefficient and compare it with the ordinary fittin...Aiming at working out more accurate pollutant degradation coefficient of the deep tunnel system, this work puts forward a novel optimized algorithm to calibrate such coefficient and compare it with the ordinary fitting method. This algorithm incorporates the outlier filtration mechanism and the gradient descent mechanism to improve its performance, and the calibration result is substituted into storm water management model (SWMM) source codes to validate its effectiveness between simulated and observed data. COD, NH3-N, TN and TP are chosen as pollutant indicators of the observed data, and the RMSE, MSE and ME are selected as indicators to present the efficiency. The results show that the outlier filtration mechanism obtains better performance than fitting method, with the gradient descent mechanism nearly reduces 92.42% of the iterative amounts and improves 55 times of the computation efficiency than the ordinary iterative method, such algorithm is expected to function better with substantial observed data.展开更多
Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the bes...Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.展开更多
The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its effects.Thus,developing surveillance systems for predict...The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its effects.Thus,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives.In this study,a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus.The Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death cases.The real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and Italy.The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients.Standard measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed models.The empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three countries.As far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain respectively.Similarly,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain respectively.The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries.Such findings provide better insights regarding the future of Covid-19 this pandemic in general.The results were obtained by applying time series models,which need to be considered for the sake of saving the lives of many people.展开更多
The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power ...The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks.This fact is more noticeable in smart grid-connected systems.The smart grid infrastructure has more renewable energy resources installed for its operation.To overcome this problem,a deep learning widearea controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes.The proposed Deep Wide Area Controller(DWAC)uses the Deep Belief Network(DBN).The network weights are updated based on real-time data from Phasor measurement units.Resilience assessment based on failure probability,financial impact,and time-series data in grid failure management determine the norm H2.To demonstrate the effectiveness of the proposed framework,a time-domain simulation case study based on the IEEE-39 bus system was performed.For a one-channel attack on the test system,the resiliency index increased to 0.962,and inter-area dampingξwas reduced to 0.005.The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well.Results also offer robust management of power system resilience and timely control of the operating conditions.展开更多
At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for ident...At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.展开更多
Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is aut...Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio（SNR） is unknown.If the frequency-locked loop（FLL） or the phase-locked loop（PLL） with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused.Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence.Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio.The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process.In addition, the upper diagonal（UD） factorization and innovation adaptive control are used to reduce model estimation errors,suppress filter divergence and improve filtering accuracy.The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.展开更多
极深因子分解机(eXtreme deep factorization machine,xDeepFM)是一种基于上下文感知的推荐模型,它提出了一种压缩交叉网络对特征进行阶数可控的特征交叉,并将该网络与深度神经网络进行结合以优化推荐效果。为了进一步提升xDeepFM在推...极深因子分解机(eXtreme deep factorization machine,xDeepFM)是一种基于上下文感知的推荐模型,它提出了一种压缩交叉网络对特征进行阶数可控的特征交叉,并将该网络与深度神经网络进行结合以优化推荐效果。为了进一步提升xDeepFM在推荐场景下的表现,提出一种基于场因子分解的xDeepFM改进模型。该模型通过场信息增强了特征的表达能力,并建立了多个交叉压缩网络以学习高阶组合特征。最后分析了用户场、项目场设定的合理性,并在3个不同规模的MovieLens系列数据集上通过受试者工作特征曲线下面积、对数似然损失指标进行性能评估,验证了该改进模型的有效性。展开更多
The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We...The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.展开更多
A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthet...A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthetic seismogram algorithm, which is capable of calculating the response of thin alternating high and low velocity layers, is applied as a solution for forward modeling, and the genetic algorithm is used to find the optimal solution of the inverse problem. Numerical tests suggest that the method has the capability of resolving low-velocity layers, thin alternating high and low velocity layers, and noise suppression. Waveform inversion using P-wave records from Zeku, Xiahe and Lintao shots in the seismic wide-angle reflection/refraction survey along northeastern Qinghai-Xizang （Tibeteau） Plateau has revealed fine structures of the bottom of the upper crust and alternating layers in the middle/lower crust and topmost upper mantle.展开更多
With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an i...With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.展开更多
基金Supported by Beijing Hospitals Authority Youth Programme,No.QML20200505.
文摘BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.
基金supported by Project No.R-2023-23 of the Deanship of Scientific Research at Majmaah University.
文摘At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.
文摘Aiming at working out more accurate pollutant degradation coefficient of the deep tunnel system, this work puts forward a novel optimized algorithm to calibrate such coefficient and compare it with the ordinary fitting method. This algorithm incorporates the outlier filtration mechanism and the gradient descent mechanism to improve its performance, and the calibration result is substituted into storm water management model (SWMM) source codes to validate its effectiveness between simulated and observed data. COD, NH3-N, TN and TP are chosen as pollutant indicators of the observed data, and the RMSE, MSE and ME are selected as indicators to present the efficiency. The results show that the outlier filtration mechanism obtains better performance than fitting method, with the gradient descent mechanism nearly reduces 92.42% of the iterative amounts and improves 55 times of the computation efficiency than the ordinary iterative method, such algorithm is expected to function better with substantial observed data.
基金supported by the National Natural Science Foundation of China(41601369)the Young Talents Program of Institute of Crop Sciences,Chinese Academy of Agricultural Sciences(S2019YC04)
文摘Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.
文摘The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its effects.Thus,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives.In this study,a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus.The Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death cases.The real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and Italy.The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients.Standard measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed models.The empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three countries.As far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain respectively.Similarly,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain respectively.The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries.Such findings provide better insights regarding the future of Covid-19 this pandemic in general.The results were obtained by applying time series models,which need to be considered for the sake of saving the lives of many people.
文摘The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks.This fact is more noticeable in smart grid-connected systems.The smart grid infrastructure has more renewable energy resources installed for its operation.To overcome this problem,a deep learning widearea controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes.The proposed Deep Wide Area Controller(DWAC)uses the Deep Belief Network(DBN).The network weights are updated based on real-time data from Phasor measurement units.Resilience assessment based on failure probability,financial impact,and time-series data in grid failure management determine the norm H2.To demonstrate the effectiveness of the proposed framework,a time-domain simulation case study based on the IEEE-39 bus system was performed.For a one-channel attack on the test system,the resiliency index increased to 0.962,and inter-area dampingξwas reduced to 0.005.The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well.Results also offer robust management of power system resilience and timely control of the operating conditions.
基金funded by the State Grid Limited Science and Technology Project of China,Grant Number SGSXDK00DJJS2200144.
文摘At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.
基金supported by Program for New Century Excellent Talents in University of China (No.NCET-120030)National Natural Science Foundation of China (No.91438116)
文摘Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio（SNR） is unknown.If the frequency-locked loop（FLL） or the phase-locked loop（PLL） with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused.Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence.Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio.The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process.In addition, the upper diagonal（UD） factorization and innovation adaptive control are used to reduce model estimation errors,suppress filter divergence and improve filtering accuracy.The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.
文摘极深因子分解机(eXtreme deep factorization machine,xDeepFM)是一种基于上下文感知的推荐模型,它提出了一种压缩交叉网络对特征进行阶数可控的特征交叉,并将该网络与深度神经网络进行结合以优化推荐效果。为了进一步提升xDeepFM在推荐场景下的表现,提出一种基于场因子分解的xDeepFM改进模型。该模型通过场信息增强了特征的表达能力,并建立了多个交叉压缩网络以学习高阶组合特征。最后分析了用户场、项目场设定的合理性,并在3个不同规模的MovieLens系列数据集上通过受试者工作特征曲线下面积、对数似然损失指标进行性能评估,验证了该改进模型的有效性。
文摘The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.
基金National Nature Science Foundation of China (40334040) & Joint Seismological foundation of CEA (101026)
文摘A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthetic seismogram algorithm, which is capable of calculating the response of thin alternating high and low velocity layers, is applied as a solution for forward modeling, and the genetic algorithm is used to find the optimal solution of the inverse problem. Numerical tests suggest that the method has the capability of resolving low-velocity layers, thin alternating high and low velocity layers, and noise suppression. Waveform inversion using P-wave records from Zeku, Xiahe and Lintao shots in the seismic wide-angle reflection/refraction survey along northeastern Qinghai-Xizang （Tibeteau） Plateau has revealed fine structures of the bottom of the upper crust and alternating layers in the middle/lower crust and topmost upper mantle.
基金supported by National Natural Science Foundation of China (61471109, 61501104 and 91438110)Fundamental Research Funds for the Central Universities ( N140405005 , N150401002 and N150404002)Open Fund of IPOC (BUPT, IPOC2015B006)
文摘With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.
