To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for desig...To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for designing a new robotic helical milling hole system, which could further improve robotic hole-making ability in airplane digital assembly. After analysis on the characteristics of helical milling hole, advantages and limitations of two typical robotic helical milling hole systems were summarized. Then, vector model of helical milling hole movement was built on vector analysis method. Finally, surface roughness calculation formula was deduced according to the movement principle of helical milling hole, then the influence of main technological parameters on surface roughness was analyzed. Analysis shows that theoretical surface roughness of hole becomes poor with the increase of tool speed ratio and revolution radius. Meanwhile, the roughness decreases according to the increase of tool teeth number. The research contributes greatly to the construction of roughness prediction model in helical milling hole.展开更多
Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling a...Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.展开更多
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock c...In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.展开更多
To address the underutilization of Chinese research materials in nonferrous metals,a method for constructing a domain of nonferrous metals knowledge graph(DNMKG)was established.Starting from a domain thesaurus,entitie...To address the underutilization of Chinese research materials in nonferrous metals,a method for constructing a domain of nonferrous metals knowledge graph(DNMKG)was established.Starting from a domain thesaurus,entities and relationships were mapped as resource description framework(RDF)triples to form the graph’s framework.Properties and related entities were extracted from open knowledge bases,enriching the graph.A large-scale,multi-source heterogeneous corpus of over 1×10^(9) words was compiled from recent literature to further expand DNMKG.Using the knowledge graph as prior knowledge,natural language processing techniques were applied to the corpus,generating word vectors.A novel entity evaluation algorithm was used to identify and extract real domain entities,which were added to DNMKG.A prototype system was developed to visualize the knowledge graph and support human−computer interaction.Results demonstrate that DNMKG can enhance knowledge discovery and improve research efficiency in the nonferrous metals field.展开更多
This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological eco...This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological ecological niches and evaluates four key dimensions:patent quality,energy efficiency engineering,technological modules,and intelligent computing power.The findings reveal that USA has established strong technological barriers through standard-essential patents(SEPs)in wireless communication and integrated circuits.In contrast,Chinese mainland firms rely heavily on fundamental technologies.Qualcomm Inc.in USA and Taiwan Semiconductor Manufacturing Company(TSMC)in Chineses Taiwan have built a comprehensive patent porfolio in energy efficiency engineering.While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes.Huawei Inc.in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration.Semiconductor manufacturing and radio frequency(RF)components still rely on external suppliers.This highlights the urgent need for innovation in new materials and open'source architectures.To enhance intelligent computing power,Chinese mainland firms must address coordination challenges.They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware,operating systems,and developer networks.展开更多
Expandable profile liner(EPL)is a promising new oil well casing cementing technique,and welding is a major EPLs connection technology.Connection of EPL is still in the stage of manual welding so far,automatic welding ...Expandable profile liner(EPL)is a promising new oil well casing cementing technique,and welding is a major EPLs connection technology.Connection of EPL is still in the stage of manual welding so far,automatic welding technology is a hotspot of EPL which is one of the key technologies to be solved.A robot for automatic welding of"8"type EPL is studied.Four quadrants of mathematical equations of the 8-shaped cross-section track of EPL,consisting of multiple arcs,are established.Mechanism program for complex cross-section welding of EPL based on angle detection is proposed according to characteristics of small size,small valleys,and large forming errors,etc.A welding velocity vector control model is established by linkage control of a welding vehicle,a small driven actuator,and a height tracking mechanism.A constant speed control model based on an angle and symmetrical analysis model of rectangular coordinate system for EPL is built.Constraint conditions of constant speed control between each section are analyzed with 4 sections in first quadrant as an example,and cooperation work mechanism of the welding vehicle and the small tracking actuator is established based on pressure detection.The constant speed control model using angle self-test can be used to avoid the need for a precise mathematical model for tracking control and to adapt manufacture and installation deviation of EPL workpiece.The model is able to solve constant speed and trajectory tracking problems of EPL cross-section welding.EPL seams welded by the studied robot are good in appearance,and non-destructive testing(NDT)shows the seams are good in quality with no welding defects.Bulge tests show that the maximum pressure of welded EPL is 35 MPa,which can fulfill expansion performance requirements.展开更多
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
The United States,Russia and China are militarily and economically among the most powerful countries in the post-Cold War period,and the interactions between the three powers heavily influence the international system...The United States,Russia and China are militarily and economically among the most powerful countries in the post-Cold War period,and the interactions between the three powers heavily influence the international system.However,different conclusions about this question are generally made by researchers through qualitative analysis,and it is necessary to objectively and quantitatively investigate their interactions.Monthly-aggregated event data from the Global Data on Events,Location and Tone(GDELT)to measure cooperative and conflictual interactions between the three powers,and the complementary cumulative distribution function(CCDF)and the vector autoregression(VAR)method are utilized to investigate their interactions in two periods:January,1991 to September,2001,and October,2001 to December,2016.The results of frequencies and strengths analysis showed that:the frequencies and strengths of USA-China interactions slightly exceeded those of USA-Russia interactions and became the dominant interactions in the second period.Although that cooperation prevailed in the three dyads in two periods,the conflictual interactions between the USA and Russia tended to be more intense in the second period,mainly related to the strategic contradiction between the USA and Russia,especially in Georgia,Ukraine and Syria.The results of CCDF indicated that similar probabilities in the cooperative behaviors between the three dyads,but the differences in the probabilities of conflictual behaviors in the USA-Russia dyad showed complicated characteristic,and those between Russia and China indicated that Russia had been consistently giving China a hard time in both periods when dealing with conflict.The USA was always an essential factor in affecting the interactions between Russia and China in both periods,but China’s behavior only played a limited role in influencing the interactions between the USA-Russia dyad.Our study provides quantitative insight into the direct cooperative and conflictual interactions between the three dyads since the end of the Cold War and helps to understand their interactions better.展开更多
We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript.The dynamics of plant model depends upon threshold number P^(∗).If P^(∗)<1 then condition helpful...We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript.The dynamics of plant model depends upon threshold number P^(∗).If P^(∗)<1 then condition helpful to eradicate the disease in plants while P^(∗)>1 explains the persistence of disease.Inappropriately,standard numerical systems do not behave well in certain scenarios.We have been proposed a structure preserving stochastic non-standard finite difference system to analyze the behavior of model.This system is dynamical consistent,positive and bounded as defined by Mickens.展开更多
Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is app...Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.展开更多
Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products ...Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.展开更多
Aiming at the problem of scanning distortion in X-Y galvanometer light detecting and ranging(Lidar) scanning system,we propose a method of image scanning distortion correction with controllable driving voltage compens...Aiming at the problem of scanning distortion in X-Y galvanometer light detecting and ranging(Lidar) scanning system,we propose a method of image scanning distortion correction with controllable driving voltage compensation.Firstly,the geometrical optics vectors model is established to explain the principle of pincushion distortion in the galvanometer scanning system,and the simulation result of scanning trajectory is consistent with experiments.The linear relationship between the driving voltage and the scanning angle of the galvanometer is verified.Secondly,the relationship between the deflection angle of the galvanometer and the scanning trajectory and the driving voltage is deduced respectively,and an image scanning correction algorithm with controllable driving voltage compensation is obtained.The simulation experiment results of the proposed method show that the root-mean-square error(RMSE) and the corresponding curve between the scan value and the actual value at different distances,have a good correction effect for the pincushion distortion.Finally,the X-Y galvanometer scanning Lidar system is established to obtain undistorted two-dimensional scanned image and it can be applied to the three-dimensional Lidar scanning system in the actual experiments,which further demonstrates the feasibility and practicability of our method.展开更多
A cardiac vector model is presented and verified, and then the forward problem for cardiac magnetic fields and electric potential are discussed based on this model and the realistic human torso volume conductor model,...A cardiac vector model is presented and verified, and then the forward problem for cardiac magnetic fields and electric potential are discussed based on this model and the realistic human torso volume conductor model, including lungs. A torso-cardiac vector model is used for a 12-lead electrocardiographic （ECG） and magneto-cardiogram （MCG） simulation study by using the boundary element method （BEM）. Also, we obtain the MCG wave picture using a compound four-channel HTc.SQUID system in a magnetically shielded room. By comparing the simulated results and experimental results, we verify the cardiac vector model and then do a preliminary study of the forward problem of MCG and ECG. Therefore, the results show that the vector model is reasonable in cardiac electrophysiology.展开更多
Based on the commodity property and finance property of gold in the international gold futures market,the influence factors of international gold futures price volatility are analyzed from the perspectives of supply a...Based on the commodity property and finance property of gold in the international gold futures market,the influence factors of international gold futures price volatility are analyzed from the perspectives of supply and demand factors,financial factors and speculation factors.The structural vector autoregression(SVAR)model is applied to investigating the direction and strength of the effects of influence factors on the international gold futures prices and the variance decomposition approach(VDA)is used to compare the contributions of these factors.The results show that the supply and demand factors still play a fundamental role in the international gold futures price volatility and the role of“China’s gold demand”is exaggerated.The financial factors and speculation factors have significant impacts on the international gold futures price volatility,which reflects that the financial property of gold becomes increasingly important.Governments and investors should pay close attention to the financial property of gold futures.展开更多
The Natural Forest Protection Program(NFPP)is one of the key ecological forestry programs in China.It not only facilitates the improvement of forest ecological quality in NFPP areas,but also plays a significant role i...The Natural Forest Protection Program(NFPP)is one of the key ecological forestry programs in China.It not only facilitates the improvement of forest ecological quality in NFPP areas,but also plays a significant role in increasing the carbon storage of forest ecosystems.The program covers 17 provinces,autonomous regions,and municipalities with correspondingly diverse forest resources and environments,ecological features,engineering measures and forest management regimes,all of which affect regional carbon storage.In this study,volume of timber harvest,tending area,pest-infested forest,firedamaged forest,reforestation,and average annual precipitation,and temperature were evaluated as factors that influence carbon storage.We developed a vector autoregression model for these seven indicators and we studied the dominant factors of carbon storage in the areas covered by NFPP.Timber harvest was the dominant factorinfluencing carbon storage in the Yellow and Yangtze River basins.Reforestation contributed most to carbon storage in the state-owned forest region in Xinjiang.In state-owned forest regions of Heilongjiang and Jilin Provinces,the dominant factors were forest fires and forest cultivation,respectively.For the enhancement of carbon sequestration capacity,a longer rotation period and a smaller timber harvest are recommended for the Yellow and Yangtze River basins.Trees should be planted in stateowned forests in Xinjiang.Forest fires should be prevented in state-owned forests in Heilongjiang,and greater forest tending efforts should be made in the state-owned forests in Jilin.展开更多
A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of key...A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.展开更多
The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid mode...The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.展开更多
Objective:To determine the clinical characteristics and prognosis of primary tracheobronchial tumors(PTTs)in children,and to explore the most common tumor identification methods.Methods:The medical records of children...Objective:To determine the clinical characteristics and prognosis of primary tracheobronchial tumors(PTTs)in children,and to explore the most common tumor identification methods.Methods:The medical records of children with PTTs who were hospitalized at the Children's Hospital of Chongqing Medical University from January 1995 to January 2020 were reviewed retrospectively.The clinical features,imaging,treatments,and outcomes of these patients were statistically analyzed.Machine learning techniques such as Gaussian na?ve Bayes,support vector machine(SVM)and decision tree models were used to identify mucoepidermoid carcinoma(ME).Results:A total of 16 children were hospitalized with PTTs during the study period.This included 5(31.3%)children with ME,3(18.8%)children with inflammatory myofibroblastic tumors(IMT),2 children(12.5%)with sarcomas,2(12.5%)children with papillomatosis and 1 child(6.3%)each with carcinoid carcinoma,adenoid cystic carcinoma(ACC),hemangioma,and schwannoma,respectively.ME was the most common tumor type and amongst the 3 ME recognition methods,the SVM model showed the best performance.The main clinical symptoms of PPTs were cough(81.3%),breathlessness(50%),wheezing(43.8%),progressive dyspnea(37.5%),hemoptysis(37.5%),and fever(25%).Of the 16 patients,7 were treated with surgery,8 underwent bronchoscopic tumor resection,and 1 child died.Of the 11 other children,3 experienced recurrence,and the last 8 remained disease-free.No deaths were observed during the follow-up period.Conclusion:PTT are very rare in children and the highest percentage of cases is due to ME.The SVM model was highly accurate in identifying ME.Chest CT and bronchoscopy can effectively diagnose PTTs.Surgery and bronchoscopic intervention can both achieve good clinical results and the prognosis of the 11 children that were followed up was good.展开更多
基金Foundation item： Projects（50975141, 51005118） supported by the National Natural Science Foundation of China Projects（20091652018, 2010352005） supported by Aviation Science Fund of China Project（YKJ11-001） supported by Key Program of Nanjing College of Information Technology Institute, China
文摘To avoid the machine problems of excessive axial force, complex process flow and frequent tool changing during robotic drilling holes, a new hole-making technology (i.e., helical milling hole) was introduced for designing a new robotic helical milling hole system, which could further improve robotic hole-making ability in airplane digital assembly. After analysis on the characteristics of helical milling hole, advantages and limitations of two typical robotic helical milling hole systems were summarized. Then, vector model of helical milling hole movement was built on vector analysis method. Finally, surface roughness calculation formula was deduced according to the movement principle of helical milling hole, then the influence of main technological parameters on surface roughness was analyzed. Analysis shows that theoretical surface roughness of hole becomes poor with the increase of tool speed ratio and revolution radius. Meanwhile, the roughness decreases according to the increase of tool teeth number. The research contributes greatly to the construction of roughness prediction model in helical milling hole.
基金Sponsored by the National High Technology Research and Development Program of China(Grant No.2003AA424020), Important Scientech ProblemTackling Subject Foundation Under the State 9th 5 -Year Plan(Grant No.96 -A22 -01 -01) and Provincial Doctoral Science Foundation of LiaoningProvince, China(Grant No.2001102034).
文摘Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
文摘In this paper, vector autoregressive （VAR） models have been recognized for the selected indicators of Dhaka stock exchange （DSE）. Bangladesh uses the micro economic variables, such as stock trade, invested stock capital, stock volume, current market value, and DSE general indexes which have the direct impact on DSE prices. The data were collected for the period from June 2004 to July 2013 as the basis on daily scale. But to get the maximum explorative information and reduction of volatility, the data have been transformed to the monthly scale. The outliers and extreme values of the study variables are detected through box and whisker plot. To detect the unit root property of the study variables, various unit root tests have been applied. The forecast performance of the different VAR models is compared to have the minimum residual. Moreover, the dynamics of this financial market is analyzed through Granger causality and impulse response analysis.
文摘To address the underutilization of Chinese research materials in nonferrous metals,a method for constructing a domain of nonferrous metals knowledge graph(DNMKG)was established.Starting from a domain thesaurus,entities and relationships were mapped as resource description framework(RDF)triples to form the graph’s framework.Properties and related entities were extracted from open knowledge bases,enriching the graph.A large-scale,multi-source heterogeneous corpus of over 1×10^(9) words was compiled from recent literature to further expand DNMKG.Using the knowledge graph as prior knowledge,natural language processing techniques were applied to the corpus,generating word vectors.A novel entity evaluation algorithm was used to identify and extract real domain entities,which were added to DNMKG.A prototype system was developed to visualize the knowledge graph and support human−computer interaction.Results demonstrate that DNMKG can enhance knowledge discovery and improve research efficiency in the nonferrous metals field.
基金supported in part by the National Social Science Foundation of China(No.20BGL203).
文摘This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological ecological niches and evaluates four key dimensions:patent quality,energy efficiency engineering,technological modules,and intelligent computing power.The findings reveal that USA has established strong technological barriers through standard-essential patents(SEPs)in wireless communication and integrated circuits.In contrast,Chinese mainland firms rely heavily on fundamental technologies.Qualcomm Inc.in USA and Taiwan Semiconductor Manufacturing Company(TSMC)in Chineses Taiwan have built a comprehensive patent porfolio in energy efficiency engineering.While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes.Huawei Inc.in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration.Semiconductor manufacturing and radio frequency(RF)components still rely on external suppliers.This highlights the urgent need for innovation in new materials and open'source architectures.To enhance intelligent computing power,Chinese mainland firms must address coordination challenges.They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware,operating systems,and developer networks.
基金supported by National Natural Science Foundation of China(Grant No.51275051)
文摘Expandable profile liner(EPL)is a promising new oil well casing cementing technique,and welding is a major EPLs connection technology.Connection of EPL is still in the stage of manual welding so far,automatic welding technology is a hotspot of EPL which is one of the key technologies to be solved.A robot for automatic welding of"8"type EPL is studied.Four quadrants of mathematical equations of the 8-shaped cross-section track of EPL,consisting of multiple arcs,are established.Mechanism program for complex cross-section welding of EPL based on angle detection is proposed according to characteristics of small size,small valleys,and large forming errors,etc.A welding velocity vector control model is established by linkage control of a welding vehicle,a small driven actuator,and a height tracking mechanism.A constant speed control model based on an angle and symmetrical analysis model of rectangular coordinate system for EPL is built.Constraint conditions of constant speed control between each section are analyzed with 4 sections in first quadrant as an example,and cooperation work mechanism of the welding vehicle and the small tracking actuator is established based on pressure detection.The constant speed control model using angle self-test can be used to avoid the need for a precise mathematical model for tracking control and to adapt manufacture and installation deviation of EPL workpiece.The model is able to solve constant speed and trajectory tracking problems of EPL cross-section welding.EPL seams welded by the studied robot are good in appearance,and non-destructive testing(NDT)shows the seams are good in quality with no welding defects.Bulge tests show that the maximum pressure of welded EPL is 35 MPa,which can fulfill expansion performance requirements.
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金The Second Tibetan Plateau Scientific Expedition and Research(STEP)program,No.2019QZKK0608Talent Start Project of Beijing Normal University。
文摘The United States,Russia and China are militarily and economically among the most powerful countries in the post-Cold War period,and the interactions between the three powers heavily influence the international system.However,different conclusions about this question are generally made by researchers through qualitative analysis,and it is necessary to objectively and quantitatively investigate their interactions.Monthly-aggregated event data from the Global Data on Events,Location and Tone(GDELT)to measure cooperative and conflictual interactions between the three powers,and the complementary cumulative distribution function(CCDF)and the vector autoregression(VAR)method are utilized to investigate their interactions in two periods:January,1991 to September,2001,and October,2001 to December,2016.The results of frequencies and strengths analysis showed that:the frequencies and strengths of USA-China interactions slightly exceeded those of USA-Russia interactions and became the dominant interactions in the second period.Although that cooperation prevailed in the three dyads in two periods,the conflictual interactions between the USA and Russia tended to be more intense in the second period,mainly related to the strategic contradiction between the USA and Russia,especially in Georgia,Ukraine and Syria.The results of CCDF indicated that similar probabilities in the cooperative behaviors between the three dyads,but the differences in the probabilities of conflictual behaviors in the USA-Russia dyad showed complicated characteristic,and those between Russia and China indicated that Russia had been consistently giving China a hard time in both periods when dealing with conflict.The USA was always an essential factor in affecting the interactions between Russia and China in both periods,but China’s behavior only played a limited role in influencing the interactions between the USA-Russia dyad.Our study provides quantitative insight into the direct cooperative and conflictual interactions between the three dyads since the end of the Cold War and helps to understand their interactions better.
基金The first author thanks Prince Sultan University for supporting this paper through the research group Nonlinear Analysis Methods in Applied Mathematics(NAMAM),group number RG-DES-2017-01-17.
文摘We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript.The dynamics of plant model depends upon threshold number P^(∗).If P^(∗)<1 then condition helpful to eradicate the disease in plants while P^(∗)>1 explains the persistence of disease.Inappropriately,standard numerical systems do not behave well in certain scenarios.We have been proposed a structure preserving stochastic non-standard finite difference system to analyze the behavior of model.This system is dynamical consistent,positive and bounded as defined by Mickens.
基金This work was supported by the National Numerical Wind tunnel Project(Grant NNW2018-ZT1B01)the National Natural Science Foundation of China(Grant 91852115).
文摘Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.
基金Supported bythe Hunan Teaching Reformand Re-search Project of Colleges and Universities (2003-B72) the HunanBoard of Review on Philosophic and Social Scientific Pay-off Project(0406035) the Hunan Soft Science Research Project(04ZH6005)
文摘Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61775048 and 62027823)the Natural Science Foundation of Shenzhen(Grant No.JCYJ2020109150808037)。
文摘Aiming at the problem of scanning distortion in X-Y galvanometer light detecting and ranging(Lidar) scanning system,we propose a method of image scanning distortion correction with controllable driving voltage compensation.Firstly,the geometrical optics vectors model is established to explain the principle of pincushion distortion in the galvanometer scanning system,and the simulation result of scanning trajectory is consistent with experiments.The linear relationship between the driving voltage and the scanning angle of the galvanometer is verified.Secondly,the relationship between the deflection angle of the galvanometer and the scanning trajectory and the driving voltage is deduced respectively,and an image scanning correction algorithm with controllable driving voltage compensation is obtained.The simulation experiment results of the proposed method show that the root-mean-square error(RMSE) and the corresponding curve between the scan value and the actual value at different distances,have a good correction effect for the pincushion distortion.Finally,the X-Y galvanometer scanning Lidar system is established to obtain undistorted two-dimensional scanned image and it can be applied to the three-dimensional Lidar scanning system in the actual experiments,which further demonstrates the feasibility and practicability of our method.
基金supported by the State Key Development Program for Basic Research of China (Grant No. 2011CBA00106)the National Natural Science Foundation of China (Grant Nos. 10674006, 81171421, and 61101046)the National High Technology Research and Development Program of China (Grant No. 2007AA03Z238)
文摘A cardiac vector model is presented and verified, and then the forward problem for cardiac magnetic fields and electric potential are discussed based on this model and the realistic human torso volume conductor model, including lungs. A torso-cardiac vector model is used for a 12-lead electrocardiographic （ECG） and magneto-cardiogram （MCG） simulation study by using the boundary element method （BEM）. Also, we obtain the MCG wave picture using a compound four-channel HTc.SQUID system in a magnetically shielded room. By comparing the simulated results and experimental results, we verify the cardiac vector model and then do a preliminary study of the forward problem of MCG and ECG. Therefore, the results show that the vector model is reasonable in cardiac electrophysiology.
基金Projects(71874210,71633006,71501193) supported by the National Natural Science Foundation of China
文摘Based on the commodity property and finance property of gold in the international gold futures market,the influence factors of international gold futures price volatility are analyzed from the perspectives of supply and demand factors,financial factors and speculation factors.The structural vector autoregression(SVAR)model is applied to investigating the direction and strength of the effects of influence factors on the international gold futures prices and the variance decomposition approach(VDA)is used to compare the contributions of these factors.The results show that the supply and demand factors still play a fundamental role in the international gold futures price volatility and the role of“China’s gold demand”is exaggerated.The financial factors and speculation factors have significant impacts on the international gold futures price volatility,which reflects that the financial property of gold becomes increasingly important.Governments and investors should pay close attention to the financial property of gold futures.
基金funded by Special Research Project of Institute of Applied Ecology,CAS(No.Y5YZX151YD)Key Laboratory of Forest Ecology and Management,Institute of Applied Ecology,CAS(No.LFEM2016-05)
文摘The Natural Forest Protection Program(NFPP)is one of the key ecological forestry programs in China.It not only facilitates the improvement of forest ecological quality in NFPP areas,but also plays a significant role in increasing the carbon storage of forest ecosystems.The program covers 17 provinces,autonomous regions,and municipalities with correspondingly diverse forest resources and environments,ecological features,engineering measures and forest management regimes,all of which affect regional carbon storage.In this study,volume of timber harvest,tending area,pest-infested forest,firedamaged forest,reforestation,and average annual precipitation,and temperature were evaluated as factors that influence carbon storage.We developed a vector autoregression model for these seven indicators and we studied the dominant factors of carbon storage in the areas covered by NFPP.Timber harvest was the dominant factorinfluencing carbon storage in the Yellow and Yangtze River basins.Reforestation contributed most to carbon storage in the state-owned forest region in Xinjiang.In state-owned forest regions of Heilongjiang and Jilin Provinces,the dominant factors were forest fires and forest cultivation,respectively.For the enhancement of carbon sequestration capacity,a longer rotation period and a smaller timber harvest are recommended for the Yellow and Yangtze River basins.Trees should be planted in stateowned forests in Xinjiang.Forest fires should be prevented in state-owned forests in Heilongjiang,and greater forest tending efforts should be made in the state-owned forests in Jilin.
文摘A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.
基金This work was supported by Hainan Provincial Natural Science Foundation of China[2018CXTD333,617048]The National Natural Science Foundation of China[61762033,61702539]+1 种基金Hainan University Doctor Start Fund Project[kyqd1328]Hainan University Youth Fund Project[qnjj1444].
文摘The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.
基金supported by the Chongqing Science and Health Joint Medical Research Project(No.8187011078).
文摘Objective:To determine the clinical characteristics and prognosis of primary tracheobronchial tumors(PTTs)in children,and to explore the most common tumor identification methods.Methods:The medical records of children with PTTs who were hospitalized at the Children's Hospital of Chongqing Medical University from January 1995 to January 2020 were reviewed retrospectively.The clinical features,imaging,treatments,and outcomes of these patients were statistically analyzed.Machine learning techniques such as Gaussian na?ve Bayes,support vector machine(SVM)and decision tree models were used to identify mucoepidermoid carcinoma(ME).Results:A total of 16 children were hospitalized with PTTs during the study period.This included 5(31.3%)children with ME,3(18.8%)children with inflammatory myofibroblastic tumors(IMT),2 children(12.5%)with sarcomas,2(12.5%)children with papillomatosis and 1 child(6.3%)each with carcinoid carcinoma,adenoid cystic carcinoma(ACC),hemangioma,and schwannoma,respectively.ME was the most common tumor type and amongst the 3 ME recognition methods,the SVM model showed the best performance.The main clinical symptoms of PPTs were cough(81.3%),breathlessness(50%),wheezing(43.8%),progressive dyspnea(37.5%),hemoptysis(37.5%),and fever(25%).Of the 16 patients,7 were treated with surgery,8 underwent bronchoscopic tumor resection,and 1 child died.Of the 11 other children,3 experienced recurrence,and the last 8 remained disease-free.No deaths were observed during the follow-up period.Conclusion:PTT are very rare in children and the highest percentage of cases is due to ME.The SVM model was highly accurate in identifying ME.Chest CT and bronchoscopy can effectively diagnose PTTs.Surgery and bronchoscopic intervention can both achieve good clinical results and the prognosis of the 11 children that were followed up was good.
