The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android malware.However,the current methods of Andr...The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android malware.However,the current methods of Android malware detection need a lot of time in the feature engineering phase.Furthermore,these models have the defects of low detection rate,high complexity,and poor practicability,etc.We analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other attributes.We classify the software’s threat levels based on the correlation of features.Then,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware detection.First,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray image.Then,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single classifier.Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.展开更多
Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hy...Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.展开更多
Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and thei...Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.展开更多
Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.Th...Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.The classic detection approach involves placing sensors on the skin or body of the fish,which may interfere with typical behavior and welfare.The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy.This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion(IFBC-MIWOEF)model.The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification.To accomplish this,the IFBC-MIWOEF model designs an ensemble of Deep Learning(DL)based fusion models such as VGG-19,DenseNet,and Effi-cientNet models for fish behavior classification.In addition,the hyperparam-eter tuning of the DL models is carried out using the MIWO algorithm,which is derived from the concepts of oppositional-based learning(OBL)and the IWO algorithm.Finally,the softmax(SM)layer at the end of the DL model categorizes the input into distinct fish behavior classes.The experimental validation of the IFBC-MIWOEF model is tested using fish videos,and the results are examined under distinct aspects.An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.展开更多
In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an ada...In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an adaptive temporal-spatial information fusion model is proposed.Firstly,an adaptive evaluation correction mechanism is constructed by the evidence distance and Deng entropy,which realizes the credibility discrimination and adaptive correction of the spatial evidence.Secondly,the credibility decay operator is introduced to obtain the dynamic credibility of temporal evidence.Finally,the sequential combination of temporal-spatial evidences is achieved by Shafer’s discount criterion and Dempster’s combination rule.The simulation results show that the proposed method not only considers the dynamic and sequential characteristics of the temporal-spatial evidences com-bination,but also has a strong conflict information processing capability,which provides a new refer-ence for the field of temporal-spatial information fusion.展开更多
Based on the dinuclear system model,the calculated evaporation residue cross sections matched well with the current experimental results.The synthesis of superheavy elements Z=121 was systematically studied through co...Based on the dinuclear system model,the calculated evaporation residue cross sections matched well with the current experimental results.The synthesis of superheavy elements Z=121 was systematically studied through combinations of stable projectiles with Z=21-30 and targets with half-lives exceeding 50 d.The influence of mass asymmetry and isotopic dependence on the projectile and target nuclei was investigated in detail.The reactions^(254)Es(^(46)Ti,3n)^(297)121 and^(252)Es(^(46)Ti,3n)^(295)121 were found to be experimentally feasible for synthesizing superheavy element Z=121,with maximal evaporation residue cross sections of 6.619 and 4.123 fb at 219.9 and 223.9 MeV,respectively.展开更多
Effect of various spatial and energy distributions of fusion neutron sourceon the calculation of neutron wall loading of Tokamak D-D fusion device has been investigated bymeans of the 3-D Monte Carlo code MCNP. A real...Effect of various spatial and energy distributions of fusion neutron sourceon the calculation of neutron wall loading of Tokamak D-D fusion device has been investigated bymeans of the 3-D Monte Carlo code MCNP. A realistic Monte Carlo source model was developed based onthe accurate representation of the spatial distribution and energy spectrum of fusion neutrons tosolve the complicated problem of tokamak fusion neutron source modelling. The results show thatthose simplified source models will introduce significant uncertainties. For accurate estimation ofthe key nuclear responses of the tokamak design and analyses, the use of the realistic source isrecommended. In addition, the accumulation of tritium produced during D-D plasma operation should becarefully considered.展开更多
We present a discrete time single-server two-level mixed service polling systems with two queue types, one center queue and N normal queues. Two-level means the center queue will be successive served after each normal...We present a discrete time single-server two-level mixed service polling systems with two queue types, one center queue and N normal queues. Two-level means the center queue will be successive served after each normal queue. In the first level, server visits between the center queue and the normal queue. In the second level, normal queues are polled by a cyclic order. Mixed service means the service discipline are exhaustive for center queue, and parallel 1-limited for normal queues. We propose an imbedded Markov chain framework to drive the closed-form expressions for the mean cycle time, mean queue length, and mean waiting time. Numerical examples demonstrate that theoretical and simulation results are identical the new system efficiently differentiates priorities.展开更多
In order to meet the demand of testability analysis and evaluation for complex equipment under a small sample test in the equipment life cycle, the hierarchical hybrid testability model- ing and evaluation method (HH...In order to meet the demand of testability analysis and evaluation for complex equipment under a small sample test in the equipment life cycle, the hierarchical hybrid testability model- ing and evaluation method (HHTME), which combines the testabi- lity structure model (TSM) with the testability Bayesian networks model (TBNM), is presented. Firstly, the testability network topo- logy of complex equipment is built by using the hierarchical hybrid testability modeling method. Secondly, the prior conditional prob- ability distribution between network nodes is determined through expert experience. Then the Bayesian method is used to update the conditional probability distribution, according to history test information, virtual simulation information and similar product in- formation. Finally, the learned hierarchical hybrid testability model (HHTM) is used to estimate the testability of equipment. Compared with the results of other modeling methods, the relative deviation of the HHTM is only 0.52%, and the evaluation result is the most accu rate.展开更多
Dezert-Smarandache(DSm) theory, a new information fusion theory, is widely applied in image processing, multiple targets tracking identification, and other areas for its excellent processing ability of imperfect inf...Dezert-Smarandache(DSm) theory, a new information fusion theory, is widely applied in image processing, multiple targets tracking identification, and other areas for its excellent processing ability of imperfect information. However, earlier research on DSm theory mainly focused on one sort of questions. An evidence fusion procedure is proposed based on the hybrid DSm model to compensate for a lack of research on the entire information procedure of DSm theory. This paper analyzes the evidence fusion procedure, as well as correlative node input and output information. Key steps and detailed procedures of evidence fusion are also discussed. Finally, an experiment illustrates the efficiency of the proposed evidence fusion procedure.展开更多
Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy man...Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.展开更多
An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Sin...An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.展开更多
In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat ...In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and. consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligenee (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The no table features of this study are that the models with a better AFT prediction and generalization performanee, a wider application potential, and reduced complexity, have been developed. Among the Ci-based models, GP and MLP based models have yielded overall improved performanee in predicting all four AFTs.展开更多
Nucleus-nucleus potentials are determined in the framework of double folding model for M3Y-Reid and M3Y- Paris effective nucleon-nucleon (NN) interactions. Both zero-range and finite-range exchange parts of NN inter...Nucleus-nucleus potentials are determined in the framework of double folding model for M3Y-Reid and M3Y- Paris effective nucleon-nucleon (NN) interactions. Both zero-range and finite-range exchange parts of NN interactions are considered in the folding procedure. In this paper the spherical projectile-spherical target system 16O+^2008Pb is selected for calculating the barrier energies, fusion cross sections and barrier distributions with the density-independent and density-dependent NN interactions on the basis of M3Y-Reid and M3Y Paris NN interactions. The barrier energies become lower for Paris NN interactions in comparison with Reid NN interactions, and also for finite-range exchange part in comparison with zero-range exchange part. The density-dependent NN interactions give similar fusion cross sections and barrier distributions, and the density-independent NN interaction causes the barrier distribution moving to a higher position. However, the density-independent Reid NN interaction with zero-range exchange part gives the lowest fusion cross sections. We find that the calculated fusion cross sections and the barrier distributions are in agreement with the experimental data after renormalization of the nuclear potential due to coupled-channel effect.展开更多
The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse th...The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete DSM.However,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs.Therefore,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points.The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.展开更多
In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop productivity.Some of the recently-developed computer visi...In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop productivity.Some of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes.With this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and classification.The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner.In addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions.The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the features.Finally,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for classification.In order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was conducted.The results were assessed under different measures.The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.展开更多
Nowadays,as the number of textual data is exponentially increasing,sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese senti...Nowadays,as the number of textual data is exponentially increasing,sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference.In this paper,we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization.First of all,FastText was trained to get the basic classification model,which can generate pre-trained word vectors as a by-product.Secondly,Bidirectional Long Short-Term Memory Network(Bi-LSTM)utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment analysis.By combining FastText and Bi-LSTM,we have developed a new fast sentiment analysis,called FAST-BiLSTM,which consistently achieves a balance between performance and speed.In particular,experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’comments,and is superior to the traditional algorithm in time efficiency,accuracy,recall and F1 criteria.展开更多
基金supported by the National Natural Science Foundation of China(62072255)。
文摘The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android malware.However,the current methods of Android malware detection need a lot of time in the feature engineering phase.Furthermore,these models have the defects of low detection rate,high complexity,and poor practicability,etc.We analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other attributes.We classify the software’s threat levels based on the correlation of features.Then,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware detection.First,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray image.Then,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single classifier.Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.
文摘Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP.1/172/42)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R191)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This study is supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444).
文摘Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development,regulatory measures,and their implementation elevating the environment.Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe.Therefore,the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent.Therefore,this paper focuses on the design of automated forest fire detection using a fusion-based deep learning(AFFD-FDL)model for environmental monitoring.The AFFDFDL technique involves the design of an entropy-based fusion model for feature extraction.The combination of the handcrafted features using histogram of gradients(HOG)with deep features using SqueezeNet and Inception v3 models.Besides,an optimal extreme learning machine(ELM)based classifier is used to identify the existence of fire or not.In order to properly tune the parameters of the ELM model,the oppositional glowworm swarm optimization(OGSO)algorithm is employed and thereby improves the forest fire detection performance.A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects.The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.
文摘Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.The classic detection approach involves placing sensors on the skin or body of the fish,which may interfere with typical behavior and welfare.The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy.This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion(IFBC-MIWOEF)model.The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification.To accomplish this,the IFBC-MIWOEF model designs an ensemble of Deep Learning(DL)based fusion models such as VGG-19,DenseNet,and Effi-cientNet models for fish behavior classification.In addition,the hyperparam-eter tuning of the DL models is carried out using the MIWO algorithm,which is derived from the concepts of oppositional-based learning(OBL)and the IWO algorithm.Finally,the softmax(SM)layer at the end of the DL model categorizes the input into distinct fish behavior classes.The experimental validation of the IFBC-MIWOEF model is tested using fish videos,and the results are examined under distinct aspects.An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.
基金the National Natural Science Foundation of China(No.61976080)the Key Project on Research and Practice of Henan University Graduate Education and Teaching Reform(YJSJG2023XJ006)+1 种基金the Key Research and Development Projects of Henan Province(231111212500)the Henan University Graduate Education Innovation and Quality Improvement Program(SYLKC2023016).
文摘In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an adaptive temporal-spatial information fusion model is proposed.Firstly,an adaptive evaluation correction mechanism is constructed by the evidence distance and Deng entropy,which realizes the credibility discrimination and adaptive correction of the spatial evidence.Secondly,the credibility decay operator is introduced to obtain the dynamic credibility of temporal evidence.Finally,the sequential combination of temporal-spatial evidences is achieved by Shafer’s discount criterion and Dempster’s combination rule.The simulation results show that the proposed method not only considers the dynamic and sequential characteristics of the temporal-spatial evidences com-bination,but also has a strong conflict information processing capability,which provides a new refer-ence for the field of temporal-spatial information fusion.
基金the National Key R&D Program of China(No.2023YFA1606401)the National Natural Science Foundation of China(Nos.12135004,11635003 and 11961141004).
文摘Based on the dinuclear system model,the calculated evaporation residue cross sections matched well with the current experimental results.The synthesis of superheavy elements Z=121 was systematically studied through combinations of stable projectiles with Z=21-30 and targets with half-lives exceeding 50 d.The influence of mass asymmetry and isotopic dependence on the projectile and target nuclei was investigated in detail.The reactions^(254)Es(^(46)Ti,3n)^(297)121 and^(252)Es(^(46)Ti,3n)^(295)121 were found to be experimentally feasible for synthesizing superheavy element Z=121,with maximal evaporation residue cross sections of 6.619 and 4.123 fb at 219.9 and 223.9 MeV,respectively.
基金The project supported partly by the National Science Foundation of Anhui Province (No. 0104360)
文摘Effect of various spatial and energy distributions of fusion neutron sourceon the calculation of neutron wall loading of Tokamak D-D fusion device has been investigated bymeans of the 3-D Monte Carlo code MCNP. A realistic Monte Carlo source model was developed based onthe accurate representation of the spatial distribution and energy spectrum of fusion neutrons tosolve the complicated problem of tokamak fusion neutron source modelling. The results show thatthose simplified source models will introduce significant uncertainties. For accurate estimation ofthe key nuclear responses of the tokamak design and analyses, the use of the realistic source isrecommended. In addition, the accumulation of tritium produced during D-D plasma operation should becarefully considered.
基金Supported by the National Natural Science Foundation of China (No. 61072079)Science Foundation of Yunnan Provincial Department (No. 2011Y117)
文摘We present a discrete time single-server two-level mixed service polling systems with two queue types, one center queue and N normal queues. Two-level means the center queue will be successive served after each normal queue. In the first level, server visits between the center queue and the normal queue. In the second level, normal queues are polled by a cyclic order. Mixed service means the service discipline are exhaustive for center queue, and parallel 1-limited for normal queues. We propose an imbedded Markov chain framework to drive the closed-form expressions for the mean cycle time, mean queue length, and mean waiting time. Numerical examples demonstrate that theoretical and simulation results are identical the new system efficiently differentiates priorities.
基金supported by the National Defense Pre-research Foundation of China(51327030104)
文摘In order to meet the demand of testability analysis and evaluation for complex equipment under a small sample test in the equipment life cycle, the hierarchical hybrid testability model- ing and evaluation method (HHTME), which combines the testabi- lity structure model (TSM) with the testability Bayesian networks model (TBNM), is presented. Firstly, the testability network topo- logy of complex equipment is built by using the hierarchical hybrid testability modeling method. Secondly, the prior conditional prob- ability distribution between network nodes is determined through expert experience. Then the Bayesian method is used to update the conditional probability distribution, according to history test information, virtual simulation information and similar product in- formation. Finally, the learned hierarchical hybrid testability model (HHTM) is used to estimate the testability of equipment. Compared with the results of other modeling methods, the relative deviation of the HHTM is only 0.52%, and the evaluation result is the most accu rate.
基金supported by the National Natural Science Foundation of China(61102168)the Military Innovation Foundation(X11QN106)
文摘Dezert-Smarandache(DSm) theory, a new information fusion theory, is widely applied in image processing, multiple targets tracking identification, and other areas for its excellent processing ability of imperfect information. However, earlier research on DSm theory mainly focused on one sort of questions. An evidence fusion procedure is proposed based on the hybrid DSm model to compensate for a lack of research on the entire information procedure of DSm theory. This paper analyzes the evidence fusion procedure, as well as correlative node input and output information. Key steps and detailed procedures of evidence fusion are also discussed. Finally, an experiment illustrates the efficiency of the proposed evidence fusion procedure.
基金This work was supported by the National Key Research and Development Program of China(2017YFB0103802)the National Natural Science Foundation of China(51922006 and 51707011).
文摘Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric airplanes.The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in real-time.This study proposes a multistage model fusion algorithm to co-estimate SOC and capacity.Firstly,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters.Secondly,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence speed.Thirdly,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application environment.Fourthly,the convergence and anti-noise performance of the fusion algorithm are discussed.Finally,the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm.The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.
基金supported by the National Natural Science Foundation of China(6153102061471383)
文摘An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.
文摘In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and. consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligenee (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The no table features of this study are that the models with a better AFT prediction and generalization performanee, a wider application potential, and reduced complexity, have been developed. Among the Ci-based models, GP and MLP based models have yielded overall improved performanee in predicting all four AFTs.
基金Project supported by the National Natural Science Foundation of China (Grant No 60572177)
文摘Nucleus-nucleus potentials are determined in the framework of double folding model for M3Y-Reid and M3Y- Paris effective nucleon-nucleon (NN) interactions. Both zero-range and finite-range exchange parts of NN interactions are considered in the folding procedure. In this paper the spherical projectile-spherical target system 16O+^2008Pb is selected for calculating the barrier energies, fusion cross sections and barrier distributions with the density-independent and density-dependent NN interactions on the basis of M3Y-Reid and M3Y Paris NN interactions. The barrier energies become lower for Paris NN interactions in comparison with Reid NN interactions, and also for finite-range exchange part in comparison with zero-range exchange part. The density-dependent NN interactions give similar fusion cross sections and barrier distributions, and the density-independent NN interaction causes the barrier distribution moving to a higher position. However, the density-independent Reid NN interaction with zero-range exchange part gives the lowest fusion cross sections. We find that the calculated fusion cross sections and the barrier distributions are in agreement with the experimental data after renormalization of the nuclear potential due to coupled-channel effect.
基金John Hopkins University Applied Physics Lab to support the Imagery of the 2019 DFC datasets
文摘The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete DSM.However,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs.Therefore,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points.The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.
文摘In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop productivity.Some of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes.With this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and classification.The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner.In addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions.The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the features.Finally,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for classification.In order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was conducted.The results were assessed under different measures.The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.
基金supported by the National Science Foundation of China(No.61771140)the 2017 Natural Science Foundation of Fujian Provincial Science&Technology Department(No.2018J01560)the 2016 Fujian Education and Scientific Research Project for Young and Middle-aged Teachers(JAT170522).
文摘Nowadays,as the number of textual data is exponentially increasing,sentiment analysis has become one of the most significant tasks in natural language processing(NLP)with increasing attention.Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference.In this paper,we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization.First of all,FastText was trained to get the basic classification model,which can generate pre-trained word vectors as a by-product.Secondly,Bidirectional Long Short-Term Memory Network(Bi-LSTM)utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment analysis.By combining FastText and Bi-LSTM,we have developed a new fast sentiment analysis,called FAST-BiLSTM,which consistently achieves a balance between performance and speed.In particular,experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’comments,and is superior to the traditional algorithm in time efficiency,accuracy,recall and F1 criteria.