浒苔漂移轨迹预报系统

2008年6月下旬,青岛海域出现大量浒苔,并迅速蔓延至近岸,对第29届奥运会帆船赛事构成严重威胁。为给浒苔清理和打捞提供决策支持,国家海洋环境预报中心及时作出应急响应,在短时间内建立了浒苔漂移路径预测系统,向有关部门发布浒苔漂移轨迹预报和海洋环境预报,成功协助前线应急指挥中心完成了浒苔的控制和治理。本文对国家海洋环境预报中心建立的浒苔漂移轨迹预报模型进行了详细描述,并开展了浒苔漂移预报误差分析。

Passive detection of copy-paste forgery between JPEG images

A blind digital image forensic method for detecting copy-paste forgery between JPEG images was proposed.Two copy-paste tampering scenarios were introduced at first:the tampered image was saved in an uncompressed format or in a JPEG compressed format.Then the proposed detection method was analyzed and simulated for all the cases of the two tampering scenarios.The tampered region is detected by computing the averaged sum of absolute difference(ASAD) images between the examined image and a resaved JPEG compressed image at different quality factors.The experimental results show the advantages of the proposed method:capability of detecting small and/or multiple tampered regions,simple computation,and hence fast speed in processing.

Position Group Contribution Method for Predicting the Normal Boiling Point of Organic Compounds

A new position group contribution model is proposed for the estimation of normal boiling data of organic compounds involving a carbon chain from C2 to C18.The characteristic of this method is the use of position distribution function.It could distinguish most of isomers that include cis-or trans-structure from organic compounds.Contributions for hydrocarbons and hydrocarbon derivatives containing oxygen,nitrogen,chlorine,bromine and sulfur,are given.Compared with the predictions,results made use of the most common existing group contribution methods,the overall average absolute difference of boiling point predictions of 417 organic compounds is 4.2 K;and the average absolute percent derivation is 1.0%,which is compared with 12.3 K and 3.2% with the method of Joback,12.1 K and 3.1% with the method of Constantinou-Gani.This new position contribution groups method is not only much more accurate but also has the advantages of simplicity and stability.

Evaluation and prediction of blast induced ground vibration using support vector machine

We present the application of Support Vector Machine (SVM) for the prediction of blast induced ground vibration by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by SVM have been compared with conventional predictor equations. Blast vibration study was carried out at Magnesite mine of Pithoragarh, India. Total 170 blast vibrations data sets were recorded at different strate-gic and vulnerable locations in and around to mine. Out of 170 data sets, 150 were used for the training of the SVM network as well as to determine site constants of different conventional predictor equations, whereas, 20 new randomly selected data sets were used to compare the prediction capability of SVM network with conventional predictor equations. Results were compared based on Co-efficient of Determination (CoD) and Mean Absolute Error (MAE) between monitored and predicted values of Peak Particle Veloc-ity (PPV). It was found that SVM gives closer values of predicted PPV as compared to conventional predictor equations. The coef-ficient of determination between measured and predicted PPV by SVM was 0.955, whereas it was 0.262, 0.163, 0.337 and 0.232 by USBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations, respectively. The MAE for PPV was 11.13 by SVM, whereas it was 0.973, 1.088, 0.939 and 1.292 by USBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations respectively.

Correlation of Shear Viscosity of Nanofluids Using the Local Composition Theory

In this study,a new method is presented to correlate the shear viscosity of nanofluids by local composition theory.The Eyring theory and nonrandom two-liquid(NRTL)equation are used for this purpose.The effects of temperature and particle volume concentration on the viscosity are investigated.The adjustable parameters of NRTL equation are obtained by fitting with experimental data.The calculated shear viscosities for nanofluids of CuO/water with 29 nm particle size,Al2O3/water with two different particle diameters,36 nm and 47 nm,and CuO/(ethylene glycol,water)are compared with experimental data and the average absolute deviation(AAD)is 1.2%,while the results from some conventional models yield an AAD of 190%.The results of this study are in excellent agreement with experimental data.

Automatic detection of respiratory rate from electrocardiogram,respiration induced plethysmography and 3D acceleration signals

Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities.Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring,however,relatively little attention is paid to accuracy and reliability.In previous study,a wearable respiration biofeedback system was designed.In this work,three kinds of signals were mixed to extract respiratory rate,i.e.,respiration inductive plethysmography(RIP),3D-acceleration and ECG.In-situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate.It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37%of a reference gold standard.Similarly studies derive respiratory rate from single-lead ECG within mean absolute percentage error 17%of a reference gold standard.

Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

CFD Simulation of Flow and Mass Transfer in Structured Packing Distillation Columns

Detailed investigation of flow behavior in structured packing distillation columns is of great importance in accurate prediction of process efficiency and development of more efficient and optimal equipment internals. In this study, a three-dimensional two-phase flow model based on VOF method for simulating the hydrodynamics and mass-transfer behavior in a typical representative unit of the structured packing is developed. In the proposed model, the c 2 - ε c model is used for the closure of turbulent mass transfer equation. By solving the proposed model, the velocity distribution, phase fraction profile and concentration field are obtained. Using these data, the total liquid holdup, the wetted area and the separation efficiency [height equivalent to a theoretical plate (HETP)] are estimated. For testing the model validation, the simulated HETPs are compared with our previous experimental data obtained in a 150 mm-diameter column containing Mellapak 350Y operating at the pressures of 0.6-1.8 MPa. The compari-son shows that they are in satisfactory agreement, with an average absolute deviation (AAD) of 25.4%.

Comparison of stem taper models for the four tropical tree species in Mount Makiling, Philippines

This study was conducted to evaluate the performance of six stem taper models on four tropical tree species, namely Celtis luzonica(Magabuyo),Diplodiscus paniculatus(Balobo), Parashorea malaanonan(Bagtikan), and Swietenia macrophylla(Mahogany) in Mount Makiling Forest Reserve(MMFR), Philippines using fit statistics and lack-of-fit statistics. Four statistical criteria were used in this study, including the standard error of estimate(SEE),coefficient of determination(R^2), mean bias( E),and absolute mean difference(AMD). For the lack-offit statistics, SEE, E and AMD were determined in different relative height classes. The results indicated that the Kozak02 stem taper model offered the best fit for the four tropical species in most statistics. The Kozak02 model also consistently provided the best performance in the lack-of-fit statistics with the best SEE, E and AMD in most of the relative height classes. These stem taper equations could help forest managers and researchers better estimate the diameter of the outside bark with any given height,merchantable stem volumes and total stem volumes of standing trees belonging to the four species of thetropical forest in MMFR.

Viscosities of Pure Water, Acetic Acid ＋ Water, and p-Xylene ＋ Acetic Acid ＋ Water at Different Temperature and Pressure

The viscosities of pure water,the acetic acid+water binary system,and the p-xylene+acetic acid+ water ternary system at different concentrations were determined with a rolling-ball viscometer at temperatures from 313.15 to 473.15 K and pressures from 0.10 to 3.20 MPa.The viscosity data were fitted by a correlation equation for the estimation of the mixture viscosities.The average absolute deviations(AAD)of the correlation for binary and ternary systems are 2.48%and 1.77%,respectively.

A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

A Fully Flexible Potential Model for Carbon Dioxide

A fully flexible potential model for carbon dioxide has been developed to predict the vapor-liquid coexistence properties using the NVT-Gibbs ensemble Monte Carlo technique(GEMC).The average absolute deviation between our simulation and the literature experimental data for saturated liquid and vapor densities is 0.3% and 2.0%,respectively.Compared with the experimental data,our calculated results of critical properties(7.39 MPa,304.04 K,and 0.4679 g?cm-3) are acceptable and are better than those from the rescaling the potential parameters of elementary physical model(EPM2).The agreement of our simulated densities of supercritical carbon dioxide with the experimental data is acceptable in a wide range of pressure and temperature.The radial distribution function estimated at the supercritical conditions suggests that the carbon dioxide is a nonlinear molecule with the C O bond length of 0.117 nm and the O C O bond angle of 176.4°,which are consistent with Car-Parrinello molecular-dynamics(CPMD),whereas the EPM2 model shows large deviation at supercritical state.The predicted self-diffusion coefficients are in agreement with the experiments.

Predication of plasma concentration of remifentanil based on Elman neural network

Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.

Prediction of Volumetric Properties(p-v-T)of Natural Gas Mixtures Using Extended Tao-Mason Equation of State

A statistical-mechanical-based equation of state(EOS)for pure substances,the Tao-Mason equation of state,is successfully extended to prediction of the(p-v-T)properties of fourteen natural gas mixtures at temperatures from 225 K to 483 K and pressures up to 60.5 MPa.This work shows that the Tao-Mason equation of state for multicomponent natural gas is predictable with minimal input information,namely critical temperature,critical pressure,and the Pitzer acentric factor.The calculated results agree well with the experimental data.From a total of 963 data of density and 330 data of compressibility factor for natural gases examined in this work,the average absolute deviations(AAD)are 1.704%and 1.344%,respectively.The present EOS is further assessed through the comparisons with Peng-Robinson(PR)equation of state.For the all of mixtures Tao-Mason(TM)EOS outperforms the PR EOS.

Extreme air pollution events:Modeling and prediction

In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution （GPD） in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants： PM10, NO2 and SO：, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators： EMA （mean absolute error）, ERMS （root mean square error）, IA （index of agreement） and R2 （coefficient of determination） were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NOe belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.

Adaptive Rate Control Scheme for H.264/AVC

The purpose of this paper is to improve allocation of the number of bits without skipping the frame by accurately estimating the target bits in H. 264/AVC rate control. The scheme ImPoses an enhancement method of the target frame rate based on H. 264/AVC bit allocation. The enhancement uses a frame complexion estimation to improve the existing Mean Absolute Difference （MAD） complexity measurement. Bit allocation to each frame is not just computed by target frame rote but also adjusted by a combined frame complexity measure. Using the statistical characteristic, the scheme obtains change of occurrence bit about QP to apply the bit amount by QP from the video characteristic and apply it in the estimated bit amount of the current frame. Simulation results show that the proposed rate eontrol scheme achieves time saving of mine than 99% over existing rate control algorithm. Nevertheless, Peak Signal-to-Noise Ration （PSNR） and bit rate were almost the same as the performances.

Improved Rate Control Using Statistical Information in Unit-Layer for H.264/AVC

The purpose of this paper is to improve allocation of the number of bits by estimating the target bits in H.264/AVC rate control.In the scheme,an enhancement method of the target unit-layer bit allocation is proposed,which uses a frame and unit complexity estimation to improve the existing mean absolute difference(MAD)complexity measurement.Using the statistical characteristics,we obtain change of occurrence bit about QP to apply the bit amount by QP from the video characteristics in the estimated bit amount of the current frame.Simulation results show that not only the proposed rate control scheme could achieve time saving of more than 99% over existing rate control algorithm,but also PSNR and bit rate were almost the same as the performance in all the sequences.

Measurement and Correlation of Solid Drugs Solubility in Supercritical Systems

A dynamic experimental set-up was utilized to measure ibuprofen solubility in supercritical CO2 at the pressure range of 8-13 MPa and the temperatures of 308, 313 and 318 K. Mole fraction values varied from 0.015＆#215;10^-3 to 3.261＆#215;10^-3 and correlated by using seven different semi empirical equations of state （Bartle, Modi-fied Bartle, Mendez-Teja, Modified Mendez-Teja, Kumar-Johnson, Sung-shim and Gordillo） as well as seven cubic equations of state （van der Waals, Redlich-Kwong, Soave-Redlich-Kwong, Peng-Robinson, Stryjek-Vera, Patel-Teja-Valderana and Pazuki）. Single and twin-parametric van der Walls mixing rules （vdW1, vdW2） were ap-plied in order to estimate the supercritical solution properties. The physicochemical properties were also obtained using Joback, Lydersen and Ambrose methods. Absolute average relatives deviation （AARD） were calculated and compared for all the correlating systems. Results showed that among the cubic equations of state （EOSs） the Pazuki equation （AARD=19.85% using vdW1 and AARD=8.79% using vdW2） and SRK equation （AARD=19.20%using vdW1 and AARD=10.03%using vdW2） predicted the ibuprofen solubility in supercritical CO2 with the least error in comparison to the others. Among the semi-empirical EOSs the most desirable deviation （AARD〈10%） was obtained by using Modified Bartle and Modified Mendez-Teja equations in all the studied temperatures.

Long-range precipitation forecasts using paleoclimate reconstructions in the western United States

Long-range precipitation forecasts are useful when managing water supplies.Oceanicatmospheric oscillations have been shown to influence precipitation.Due to a longer cycle of some of the oscillations,a short instrumental record is a limitation in using them for long-range precipitation forecasts.The influence of oscillations over precipitation is observable within paleoclimate reconstructions;however,there have been no attempts to utilize these reconstructions in precipitation forecasting.A data-driven model,KStar,is used for obtaining long-range precipitation forecasts by extending the period of record through the use of reconstructions of oscillations.KStar is a nearest neighbor algorithm with an entropy-based distance function.Oceanic-atmospheric oscillation reconstructions include the El Nino-Southern Oscillation(ENSO),the Pacific Decadal Oscillation(PDO),the North Atlantic Oscillation(NAO),and the Atlantic Multi-decadal Oscillation(AMO).Precipitation is forecasted for 20 climate divisions in the western United States.A 10-year moving average is applied to aid in the identification of oscillation phases.A lead time approach is used to simulate a one-year forecast,with a 10-fold cross-validation technique to test the models.Reconstructions are used from 1658-1899,while the observed record is used from 1900-2007.The model is evaluated using mean absolute error(MAE),root mean squared error(RMSE),RMSE-observations standard deviation ratio(RSR),Pearson's correlation coefficient(R),NashSutcliffe coefficient of efficiency(NSE),and linear error in probability space(LEPS) skill score(SK).The role of individual and coupled oscillations is evaluated by dropping oscillations in the model.The results indicate 'good' precipitation estimates using the KStar model.This modeling technique is expected to be useful for long-term water resources planning and management.

Evaluation of Some Stem Taper Models for Camellia japonica in Mount Halla, Korea

This study was conducted to evaluate the performance of the four stem taper models on Camellia japonica in Jeju Island, Korea using fit statistics and lack-of-fit statistics. The five statistical criteria that were used in this study were standard error of estimate(SEE), mean bias( E), absolute mean difference(AMD), coefficient of determination(R2), and root mean square error(RMSE). Results showed that the Kozak model 02 stem taper had the best performance in all fit statistics(SEE: 3.4708, E : 0.0040 cm, AMD : 0.9060 cm, R2 : 0.9870, and RMSE : 1.2545). On the other hand, Max and Burkhart stem taper model had the poorest performance in each statistical criterion(SEE: 4.2121, E : 0.2520 cm, AMD : 1.1300 cm, R2 : 0.9805, and RMSE: 1.5317). For the lack-of-fit statistics, the Kozak model 02 also provided the best performance having the best AMD in most of the relative height classes for diameter outside bark prediction and in most of the DBH classes for total volume prediction while Max and Burkhart had the poorest performance. These stem taper equations could help forest managers to better estimate the diameter outside bark at any given height, merchantable stem volumes and total stem volumes of the standing trees of Camellia japonica in the forests of Jeju Island, Korea.