Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a ...Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a statistical damage constitutive model and energy evolution mechanisms.Initially,integrating the principle of effective stress and the Hoek-Brown criterion,a statistical damage constitutive model for gas-bearing coal is established and validated through triaxial compression tests under different gas pressures to verify its accuracy and applicability.Subsequently,employing energy evolution mechanism,two energy characteristic parameters(elastic energy proportion and dissipated energy proportion)are analyzed.Based on the damage stress thresholds,the damage evolution characteristics of gas bearing coal were explored.Finally,by integrating energy characteristic parameters with damage parameters,a novel brittleness index is proposed.The results demonstrate that the theoretical curves derived from the statistical damage constitutive model closely align with the test curves,accurately reflecting the stress−strain characteristics of gas-bearing coal and revealing the stress drop and softening characteristics of coal in the post-peak stage.The shape parameter and scale parameter represent the brittleness and macroscopic strength of the coal,respectively.As gas pressure increases from 1 to 5 MPa,the shape parameter and the scale parameter decrease by 22.18%and 60.45%,respectively,indicating a reduction in both brittleness and strength of the coal.Parameters such as maximum damage rate and peak elastic energy storage limit positively correlate with coal brittleness.The brittleness index effectively captures the brittleness characteristics and reveals a decrease in brittleness and an increase in sensitivity to plastic deformation under higher gas pressure conditions.展开更多
Dynamic tensile impact properties of aramid (Technora) and UHMWPE (DC851) fiber bundles were studied at two high strain rates by means of reflecting type Split Hopkinson Bar, and stress-strain curves of fiber yarns ...Dynamic tensile impact properties of aramid (Technora) and UHMWPE (DC851) fiber bundles were studied at two high strain rates by means of reflecting type Split Hopkinson Bar, and stress-strain curves of fiber yarns at different strain rates were obtained. Experimental results show that the initial elastic modulus, failure strength and unstable strain of aramid fiber yarns are strain rate insensitive, whereas the initial elastic modulus and unstable strain of UHMWPE fiber yarns are strain rate sensitive. A fiber-bundle statistical constitutive equation was used to describe the tensile behavior of aramid and UHMWPE fiber bundles at high strain rates. The good consistency between the simulated results and experimental data indicates that the modified double Weibull function can represent the tensile strength distribution of aramid and UHMWPE fibers and the method of extracting Weibull parameters from fiber bundles stress-strain data is valid.展开更多
For radar targets flying at low altitude, multiple pathways produce fade or enhancement relative to the level that would be expected in a free-space environment. In this paper, a new detec- tion method based on a wide...For radar targets flying at low altitude, multiple pathways produce fade or enhancement relative to the level that would be expected in a free-space environment. In this paper, a new detec- tion method based on a wide-ranging multi-frequency radar for low angle targets is proposed. Sequential transmitting multiple pulses with different frequencies are first applied to decorrelate the cohe- rence of the direct and reflected echoes. After receiving all echoes, the multi-frequency samples are arranged in a sort descending ac- cording to the amplitude. Some high amplitude echoes in the same range cell are accumulated to improve the signal-to-noise ratio and the optimal number of high amplitude echoes is analyzed and given by experiments. Finally, simulation results are presented to verify the effectiveness of the method.展开更多
The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied.When the number of strong scattering cells is known,we first show an optima...The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied.When the number of strong scattering cells is known,we first show an optimal detector,which requires many processing channels.The structure of such optimal detector is complex.Therefore,a simpler quasi-optimal detector is then introduced.The quasi-optimal detector,called the strong scattering cells’ number dependent order statistics（SND-OS） detector,takes the form of an average of maximum strong scattering cells with a known number.If the number of strong scattering cells is unknown in real situation,the multi-channel order statistics（MC-OS） detector is used.In each channel,a various number of maximums scattered from target are averaged.Then,the false alarm probability analysis and thresholds sets for each channel are given,following the detection results presented by means of Monte Carlo simulation strategy based on simulated target model and three measured targets.In particular,the theoretical analysis and simulation results highlight that the MC-OS detector can efficiently detect range-spread targets in white Gaussian noise.展开更多
A three-dimensional transient numerical simulation was conducted to study the pressure fluctuations in low-specific-speed centrifugal pumps. The characteristics of the inner flow were investigated using the SST k-ω t...A three-dimensional transient numerical simulation was conducted to study the pressure fluctuations in low-specific-speed centrifugal pumps. The characteristics of the inner flow were investigated using the SST k-ω turbulence model. The distributions of pressure fluctuations in the impeller and the volute were recorded, and the pressure fluctuation intensity was analyzed comprehensively, at the design condition, using statistical methods. The results show that the pressure fluctuation intensity increases along the impeller streamline from the leading edge to the trailing edge. In the impeller passage, the intensity near the shroud is much higher than that near the hub at thc inlet. However, the intensity at the middle passage is almost equal to the intensity at the outlet. The pressure fluctuation intensity is the highest at the trailing edge on the pressure side and near the tongue because of the rotor-stator interaction. The distribution of pressure fluctuation intensity is symmetrical in the axial cross sections of the volute channel. However, this intensity decreases with increasing radial distance. Hence, the pressure fluctuation intensity can be reduced by modifying the geometry of the leading edge in the impeller and the tongue in the volute.展开更多
Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a cla...A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.展开更多
The paper describes field test results of 7.62×51 mm M61 AP(armour piercing) ammunition fired into mild steel targets at an outdoor range.The targets varied from 10 mm to 32 mm in thickness.The tests recorded pen...The paper describes field test results of 7.62×51 mm M61 AP(armour piercing) ammunition fired into mild steel targets at an outdoor range.The targets varied from 10 mm to 32 mm in thickness.The tests recorded penetration depth,probability of perforation(i.e.,complete penetration),muzzle and impact velocities,bullet mass,and plate yield strength and hardness.The measured penetration depth exhibited a variability of approximately±12%.The paper then compared ballistic test results with predictive models of steel penetration depth and thickness to prevent perforation.Statistical parameters were derived for muzzle and impact velocity,bullet mass,plate thickness,plate hardness,and model error.A Monte-Carlo probabilistic analysis was then developed to estimate the probability of plate perforation of 7.62 mm M61 AP ammunition for a range of impact velocities,and for mild steels,and High Hardness Armour(HHA) plates.This perforation fragility analysis considered the random variability of impact velocity,bullet mass,plate thickness,plate hardness,and model error.Such a probabilistic analysis allows for reliability-based design,where,for example,the plate thickness with 95% reliability(i.e.only 1 in 20 shots will penetrate the wall) can be estimated knowing the probabilistic distribution of perforation.Hence,it was found that the plate thickness to ensure a low 5% probability of perforation needs to be 11-15% thicker than required to have a 50/50 chance of perforation for mild steel plates.Plates would need to be 20-30% thicker if probability of perforation is reduced to zero.展开更多
The novel closed-form expressions for the average channel capacity of dual selection diversity is presented, as well as, the bit-error rate （BER） of several coherent and noncoherent digital modulation schemes in the...The novel closed-form expressions for the average channel capacity of dual selection diversity is presented, as well as, the bit-error rate （BER） of several coherent and noncoherent digital modulation schemes in the correlated Weibull fading channels with nonidentical statisticS. The results are expressed in terms of Meijer＇s Gfunction, which can be easily evaluated numerically. The simulation results are presented to validate the proposed theoretical analysis and to examine the effects of the fading severity on the concerned quantities.展开更多
The instantaneous frequency （IF） estimation of the linear frequency modulated （LFM） signals with time-varying amplitude using the peak of the Wigner-Ville distribution （WVD） is studied. Theoretical analysis show...The instantaneous frequency （IF） estimation of the linear frequency modulated （LFM） signals with time-varying amplitude using the peak of the Wigner-Ville distribution （WVD） is studied. Theoretical analysis shows that the estimation on LFM signals with time-varying amplitude is unbiased, only if WVD of time-varying amplitude reaches its maximum at frequency zero no matter in which time. The statistical performance in the case of additive white Guassian noise is evaluated and an analytical expression for the variance is provided. The simulations using LFM signals with Gaussian envelope testify that IF can be estimated accurately using the peak of WVD for four models of amplitude variation. Furthermore the statistical result of estimation on the signals with amplitude descending before rising is better than that of the signals with constant amplitude when the amplitude variation rate is moderate.展开更多
This work correlated the detailed work zone location and time data from the Wis LCS system with the five-min inductive loop detector data. One-sample percentile value test and two-sample Kolmogorov-Smirnov(K-S) test w...This work correlated the detailed work zone location and time data from the Wis LCS system with the five-min inductive loop detector data. One-sample percentile value test and two-sample Kolmogorov-Smirnov(K-S) test were applied to compare the speed and flow characteristics between work zone and non-work zone conditions. Furthermore, we analyzed the mobility characteristics of freeway work zones within the urban area of Milwaukee, WI, USA. More than 50% of investigated work zones have experienced speed reduction and 15%-30% is necessary reduced volumes. Speed reduction was more significant within and at the downstream of work zones than at the upstream.展开更多
Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other seman...Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.展开更多
CFAR technique is widely used in radar targets detection fields. Traditional algorithm is cell averaging (CA), which can give a good detection performance in a relatively ideal environment. Recently, censoring techniq...CFAR technique is widely used in radar targets detection fields. Traditional algorithm is cell averaging (CA), which can give a good detection performance in a relatively ideal environment. Recently, censoring technique is adopted to make the detector perform robustly. Ordered statistic (OS) and trimmed mean (TM) methods are proposed. TM methods treat the reference samples which participate in clutter power estimates equally, but this processing will not realize the effective estimates of clutter power. Therefore, in this paper a quasi best weighted (QBW) order statistics algorithm is presented. In special cases, QBW reduces to CA and the censored mean level detector (CMLD).展开更多
The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to s...The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.展开更多
An on-line blind source separation （BSS） algorithm is presented in this paper under the assumption that sources are temporarily correlated signals. By using only some of the observed samples in a recursive calculati...An on-line blind source separation （BSS） algorithm is presented in this paper under the assumption that sources are temporarily correlated signals. By using only some of the observed samples in a recursive calculation, the whitening matrix and the rotation matrix could be approximately obtained through the measurement of only one cost function. SimNations show goad performance of the algorithm.展开更多
The Okiep Copper District is the oldest mining district in South Africa with a legacy of more than 150 years of mining.This legacy can be felt in the presence of large tailings dams scattered throughout the area.These...The Okiep Copper District is the oldest mining district in South Africa with a legacy of more than 150 years of mining.This legacy can be felt in the presence of large tailings dams scattered throughout the area.These tailings have a deleterious impact on the surrounding environment.To use geochemical methods in determining the scale of the impact, pre-mining background levels need to be determined. This is especially difficult in areas for which展开更多
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金Project(52274096)supported by the National Natural Science Foundation of ChinaProject(WS2023A03)supported by the State Key Laboratory Cultivation Base for Gas Geology and Gas Control,China。
文摘Accurate assessment of coal brittleness is crucial in the design of coal seam drilling and underground coal mining operations.This study proposes a method for evaluating the brittleness of gas-bearing coal based on a statistical damage constitutive model and energy evolution mechanisms.Initially,integrating the principle of effective stress and the Hoek-Brown criterion,a statistical damage constitutive model for gas-bearing coal is established and validated through triaxial compression tests under different gas pressures to verify its accuracy and applicability.Subsequently,employing energy evolution mechanism,two energy characteristic parameters(elastic energy proportion and dissipated energy proportion)are analyzed.Based on the damage stress thresholds,the damage evolution characteristics of gas bearing coal were explored.Finally,by integrating energy characteristic parameters with damage parameters,a novel brittleness index is proposed.The results demonstrate that the theoretical curves derived from the statistical damage constitutive model closely align with the test curves,accurately reflecting the stress−strain characteristics of gas-bearing coal and revealing the stress drop and softening characteristics of coal in the post-peak stage.The shape parameter and scale parameter represent the brittleness and macroscopic strength of the coal,respectively.As gas pressure increases from 1 to 5 MPa,the shape parameter and the scale parameter decrease by 22.18%and 60.45%,respectively,indicating a reduction in both brittleness and strength of the coal.Parameters such as maximum damage rate and peak elastic energy storage limit positively correlate with coal brittleness.The brittleness index effectively captures the brittleness characteristics and reveals a decrease in brittleness and an increase in sensitivity to plastic deformation under higher gas pressure conditions.
文摘Dynamic tensile impact properties of aramid (Technora) and UHMWPE (DC851) fiber bundles were studied at two high strain rates by means of reflecting type Split Hopkinson Bar, and stress-strain curves of fiber yarns at different strain rates were obtained. Experimental results show that the initial elastic modulus, failure strength and unstable strain of aramid fiber yarns are strain rate insensitive, whereas the initial elastic modulus and unstable strain of UHMWPE fiber yarns are strain rate sensitive. A fiber-bundle statistical constitutive equation was used to describe the tensile behavior of aramid and UHMWPE fiber bundles at high strain rates. The good consistency between the simulated results and experimental data indicates that the modified double Weibull function can represent the tensile strength distribution of aramid and UHMWPE fibers and the method of extracting Weibull parameters from fiber bundles stress-strain data is valid.
基金supported by the National Natural Science Foundation of China(6137213661372134+2 种基金61172137)the Fundamental Research Funds for the Central Universities(K5051202005)the China Scholarship Council(CSC)
文摘For radar targets flying at low altitude, multiple pathways produce fade or enhancement relative to the level that would be expected in a free-space environment. In this paper, a new detec- tion method based on a wide-ranging multi-frequency radar for low angle targets is proposed. Sequential transmitting multiple pulses with different frequencies are first applied to decorrelate the cohe- rence of the direct and reflected echoes. After receiving all echoes, the multi-frequency samples are arranged in a sort descending ac- cording to the amplitude. Some high amplitude echoes in the same range cell are accumulated to improve the signal-to-noise ratio and the optimal number of high amplitude echoes is analyzed and given by experiments. Finally, simulation results are presented to verify the effectiveness of the method.
基金supported by the Major Program of National Natural Science Foundation of China (10990012)the National Natural Science Foundation of China (61201296,61271024)+1 种基金the Fundamental Research Funds for the Central Universities (K5051202037)Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (12205)
文摘The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied.When the number of strong scattering cells is known,we first show an optimal detector,which requires many processing channels.The structure of such optimal detector is complex.Therefore,a simpler quasi-optimal detector is then introduced.The quasi-optimal detector,called the strong scattering cells’ number dependent order statistics（SND-OS） detector,takes the form of an average of maximum strong scattering cells with a known number.If the number of strong scattering cells is unknown in real situation,the multi-channel order statistics（MC-OS） detector is used.In each channel,a various number of maximums scattered from target are averaged.Then,the false alarm probability analysis and thresholds sets for each channel are given,following the detection results presented by means of Monte Carlo simulation strategy based on simulated target model and three measured targets.In particular,the theoretical analysis and simulation results highlight that the MC-OS detector can efficiently detect range-spread targets in white Gaussian noise.
基金Projects(51239005,51009072) supported by the National Natural Science Foundation of ChinaProject(2011BAF14B04) supported by the National Science&Technology Pillar Program of ChinaProject(13JDG084) supported by the Research Foundation for Advanced Talents of Jiansu University,China
文摘A three-dimensional transient numerical simulation was conducted to study the pressure fluctuations in low-specific-speed centrifugal pumps. The characteristics of the inner flow were investigated using the SST k-ω turbulence model. The distributions of pressure fluctuations in the impeller and the volute were recorded, and the pressure fluctuation intensity was analyzed comprehensively, at the design condition, using statistical methods. The results show that the pressure fluctuation intensity increases along the impeller streamline from the leading edge to the trailing edge. In the impeller passage, the intensity near the shroud is much higher than that near the hub at thc inlet. However, the intensity at the middle passage is almost equal to the intensity at the outlet. The pressure fluctuation intensity is the highest at the trailing edge on the pressure side and near the tongue because of the rotor-stator interaction. The distribution of pressure fluctuation intensity is symmetrical in the axial cross sections of the volute channel. However, this intensity decreases with increasing radial distance. Hence, the pressure fluctuation intensity can be reduced by modifying the geometry of the leading edge in the impeller and the tongue in the volute.
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
基金The authors appreciate the laboratory assistance of Goran Simundic and Michael Goodwin for assistance with measurement of the field test results The assistance of final year honours student Richard Szlicht is gratefully acknowledged.
文摘The paper describes field test results of 7.62×51 mm M61 AP(armour piercing) ammunition fired into mild steel targets at an outdoor range.The targets varied from 10 mm to 32 mm in thickness.The tests recorded penetration depth,probability of perforation(i.e.,complete penetration),muzzle and impact velocities,bullet mass,and plate yield strength and hardness.The measured penetration depth exhibited a variability of approximately±12%.The paper then compared ballistic test results with predictive models of steel penetration depth and thickness to prevent perforation.Statistical parameters were derived for muzzle and impact velocity,bullet mass,plate thickness,plate hardness,and model error.A Monte-Carlo probabilistic analysis was then developed to estimate the probability of plate perforation of 7.62 mm M61 AP ammunition for a range of impact velocities,and for mild steels,and High Hardness Armour(HHA) plates.This perforation fragility analysis considered the random variability of impact velocity,bullet mass,plate thickness,plate hardness,and model error.Such a probabilistic analysis allows for reliability-based design,where,for example,the plate thickness with 95% reliability(i.e.only 1 in 20 shots will penetrate the wall) can be estimated knowing the probabilistic distribution of perforation.Hence,it was found that the plate thickness to ensure a low 5% probability of perforation needs to be 11-15% thicker than required to have a 50/50 chance of perforation for mild steel plates.Plates would need to be 20-30% thicker if probability of perforation is reduced to zero.
基金the National High-Tech Research and Development Program (2002AA123032)the Innovative Research Team Program of UESTC, China.
文摘The novel closed-form expressions for the average channel capacity of dual selection diversity is presented, as well as, the bit-error rate （BER） of several coherent and noncoherent digital modulation schemes in the correlated Weibull fading channels with nonidentical statisticS. The results are expressed in terms of Meijer＇s Gfunction, which can be easily evaluated numerically. The simulation results are presented to validate the proposed theoretical analysis and to examine the effects of the fading severity on the concerned quantities.
文摘The instantaneous frequency （IF） estimation of the linear frequency modulated （LFM） signals with time-varying amplitude using the peak of the Wigner-Ville distribution （WVD） is studied. Theoretical analysis shows that the estimation on LFM signals with time-varying amplitude is unbiased, only if WVD of time-varying amplitude reaches its maximum at frequency zero no matter in which time. The statistical performance in the case of additive white Guassian noise is evaluated and an analytical expression for the variance is provided. The simulations using LFM signals with Gaussian envelope testify that IF can be estimated accurately using the peak of WVD for four models of amplitude variation. Furthermore the statistical result of estimation on the signals with amplitude descending before rising is better than that of the signals with constant amplitude when the amplitude variation rate is moderate.
基金Project(61620106002)supported by the National Natural Science Foundation of ChinaProject(2016YFB0100906)supported by the National Key R&D Program in China+1 种基金Project(2015364X16030)supported by the Information Technology Research Project of Ministry of Transport of ChinaProject(2242015K42132)supported by the Fundamental Sciences of Southeast University,China
文摘This work correlated the detailed work zone location and time data from the Wis LCS system with the five-min inductive loop detector data. One-sample percentile value test and two-sample Kolmogorov-Smirnov(K-S) test were applied to compare the speed and flow characteristics between work zone and non-work zone conditions. Furthermore, we analyzed the mobility characteristics of freeway work zones within the urban area of Milwaukee, WI, USA. More than 50% of investigated work zones have experienced speed reduction and 15%-30% is necessary reduced volumes. Speed reduction was more significant within and at the downstream of work zones than at the upstream.
基金Project(61262035) supported by the National Natural Science Foundation of ChinaProjects(GJJ12271,GJJ12742) supported by the Science and Technology Foundation of Education Department of Jiangxi Province,ChinaProject(20122BAB201033) supported by the Natural Science Foundation of Jiangxi Province,China
文摘Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.
文摘CFAR technique is widely used in radar targets detection fields. Traditional algorithm is cell averaging (CA), which can give a good detection performance in a relatively ideal environment. Recently, censoring technique is adopted to make the detector perform robustly. Ordered statistic (OS) and trimmed mean (TM) methods are proposed. TM methods treat the reference samples which participate in clutter power estimates equally, but this processing will not realize the effective estimates of clutter power. Therefore, in this paper a quasi best weighted (QBW) order statistics algorithm is presented. In special cases, QBW reduces to CA and the censored mean level detector (CMLD).
文摘The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.
基金This project was supported by the National 863 project (2001AA422420 -02)
文摘An on-line blind source separation （BSS） algorithm is presented in this paper under the assumption that sources are temporarily correlated signals. By using only some of the observed samples in a recursive calculation, the whitening matrix and the rotation matrix could be approximately obtained through the measurement of only one cost function. SimNations show goad performance of the algorithm.
文摘The Okiep Copper District is the oldest mining district in South Africa with a legacy of more than 150 years of mining.This legacy can be felt in the presence of large tailings dams scattered throughout the area.These tailings have a deleterious impact on the surrounding environment.To use geochemical methods in determining the scale of the impact, pre-mining background levels need to be determined. This is especially difficult in areas for which
