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In table tennis, a 40 mm in diameter table tennis ball weighing 2.7g can reach the opponent’s table in a very short time when it travels at a speed of 17m/s. That is difficult for new players to hit accurately. The purpose of this work is to recognize hits and misses by distinguishing the action difference between hitting and missing table tennis. The result of recognition can be used to figure out a small deviation from the correct posture and improve the hitting accuracy. Six volunteers participated in the experiment. The volunteers wore wristband sensors on the wrist on which they held the table tennis bat. Each volunteer played the ball 100 times and collected the information through a wristband sensor. The collected information was analyzed by support vector machine (SVM), decision tree (CART), linear discriminant analysis (LDA), K nearest neighbor (KNN), and Naive Bayes (NB) to identify the identities of volunteers and the hit or miss of the ball. The accuracy rate of volunteers’ identity recognition is 99%, and the accuracy rate of hits and misses is 95%. The results show that the wristband sensor can accurately identify the volunteers’ identities and missing cases through appropriate classification methods. This shows that we can find out the nuances between the stroke postures by hitting or missing the ball, and then use the results to correct the movement of the players, and thus improve the players’ skill.

As a result of global warming, the coverage and the thickness of sea ice in the Polar Regions is gradually decreasing, making energy development and maritime transport possible in the Polar Regions. However, ships will inevitably encounter the problem of collision between ships and sea ice when sail in the Polar Regions. In some extreme cases, the impact of sea ice on the ship can lead to catastrophic damage. Therefore, it is of great significance to study the influence of ship speed, collision angle and ice thickness on the response of sea ice collision structure. This paper uses the finite element method of the numerical method to study the ship-ice collision. The finite element simulation model was established by software LS-DYNA. The appropriate material model and material parameters were selected for the ship and sea ice to simulate the collision process of the ship and sea ice. In this way, the influence of ship speed, collision angle and sea ice thickness on the response of ship ice collision structure is obtained. This is of great significance to study the mechanism of ship-ice collision.

Calibration model building is composed of suitable number of samples and multivariate regression techniques. The accuracy of prediction is determined by both factors (steps). In these two steps, the multivariate regression step is influenced by too many factors, making it impossible to determine the number of samples. However, sample number in sample collection step can be used to ensure population representation in statistics. The sample number is the cornerstone of the robustness of model that should be concentrated on; however, few instructions but some empirical expressions have been given up until now. The factors affecting the sampling accuracy include confidence level, relative standard errors, and relative representation requirements. The required number can be calculated by statistical parameters of population and the required representation. The relative standard error is an important factor related to the statistical parameters of the sample set. For general instructions, the calibration kit should use 100-150 samples, the more the better, but it is not recommended to use more than 200. These suggestions would help guide the operator by selecting an appropriate calibration sample number in spectroscopy.

Biomass is an important parameter in fermentation processes. The estimation of biomass during fermentation usually uses an off-line method, such as optical density at 600 nm (OD600) or the determination of dry cell weight (DCW). Online measurement of biomass via mid-infrared (MIR) spectroscopy has also been published. However, no strict demonstration has been given that biomass measurement by MIR is due to the specific absorption of infrared radiation by cells. Three factors are analyzed about cell absorption, which being: optical sampling theory, spectral absorbance intensity inspection, and PLS regression of cells model. Three aspects lead to the conclusion that the measurement of biomass by MIR is not due to specific absorption by bacteria but rather to a chance correlation with the substrate glutamate in this study. If a chance correlation is present, the biomass can be measured by this indirect method. The most reliable measurement method is still by DCW or OD600. It is frustrating that the online measurement of biomass still remains uncertain.

For spectroscopic measurements, representative samples are needed in the course of building a calibration model to guarantee accurate predictions. The most widely used selection method is the Kennard-Stone method, which can be used before a reference measurement is done. In this paper, a method termed semi-supervised selection is presented to determine whether a sample should be added to the calibration set. The selection procedure has two steps. First, part of the population of samples is selected using the Kennard-Stone method, and their concentrations are measured. Second, another part of the population of samples is selected based on the scalar value distribution of the net analyte signal. If the net analyte signal of a sample is distinctive compared to the existing net analyte signal values, then the sample is added to the calibration set. The analyte of interest in the sample is then measured so that the sample can be used as a calibration sample. By a validation test, it is shown that the presented method is more efficient than random selection and Kennard-Stone selection. As a result, both the time and the money spent on reference measurements are saved.

The normal specification for multivariate calibration is the root mean square error (RMSE), which is computed from the error of all the samples in one set. As a result, condensed samples will inherently have less error than sparse samples. However, this phenomenon is undesirable in monitoring processes where marginal samples should be measured more accurately. Improving the accuracy of a calibration model over a particular interval would have a practical impact. By selecting uniformly distributed samples and including all the focused interval samples to decrease the cluster effect and include more matrix information, the accuracy of the target interval can be improved over that of an unselected calibration set. The selection method is based on a net analyte signal norm value computation and selection. Simulated spectral data and real sample sets are used to test the capability of the presented sample selection method. The experimental results show the method can improve interval accuracy for minor analyte and get almost equal interval accuracy for major analyte.

Direct determination of chemical constituents in complex biofluids without the need for any reagent or pre-processing of samples has become a promising technique for clinical analysis. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been widely studied as a powerful method of reagent-free biofluids analysis. In this work, to further utilize information and improve prediction performance, new hybrid spectra space was constructed based on different derivative spectra space. Then, hybrid spectra space ensemble interval partial least squares modeling (HSEiPLS) was proposed for quantification analysis of biofluids. In the experiment of determining glucose concentrations in 58 whole blood samples, the F-test is used to determine the optimal number of latent variables for models and the F-test significance level is set to 0.25. HSEiPLS model provided lower root mean square error of prediction (RMSEP) values 0.352 mM/L compared with other methods. In the experiment of determining cholesterol concentrations in 50 whole blood samples, HSEiPLS model provided RMSEP values 0.205 mM/L under the same condition of the significance level. Experimental results demonstrate that the proposed HSEiPLS based on hybrid spectra space provides superior predictive power for biofluids.

A calibration set comprises the multidimensional space that represents the samples for prediction. The representative ability of a calibration set is a major factor that affects the predictive performance of a multivariate regression. A new reference value (YR)-based sample-selection algorithm that assembles a dependent value (y-value) uniform distribution is presented to assure the representation. The existing typical sample-selection algorithm is used for comparison. A set of soy sauce data is used as a set of typical samples that have a complex solution. Comparing the prediction results, it is shown that YR sample-selection has similar prediction performance to that of sample set partitioning based on joint x–y distances (SPXY), but with a simpler algorithm. The calibration models of the y-reference-included sample sets (SPXY and YR) are more accurate than those of y-reference-excluded sample sets (RS and KS). After modeling with the selected representative samples, the performances of YR and SPXY are comparable to that of full sample modeling with fewer samples.

To guarantee accurate predictions, representative samples are needed when building a calibration model for spectroscopic measurements. However, in general, it is not known whether a sample is representative prior to measuring its concentration, which is both time-consuming and expensive. In this paper, a method to determine whether a sample should be selected into a calibration set is presented. The selection is based on the difference of Euclidean norm of net analyte signal (NAS) vector between the candidate and existing samples. First, the concentrations and spectra of a group of samples are used to compute the projection matrix, NAS vector, and scalar values. Next, the NAS vectors of candidate samples are computed by multiplying projection matrix with spectra of samples. Scalar value of NAS is obtained by norm computation. The distance between the candidate set and the selected set is computed, and samples with the largest distance are added to selected set sequentially. Last, the concentration of the analyte is measured such that the sample can be used as a calibration sample. Using a validation test, it is shown that the presented method is more efficient than random selection. As a result, the amount of time and money spent on reference measurements is greatly reduced.

A wavelength modulated photoacoustic spectroscopy system utilizing an optical fiber microphone and a tunable Erbium-doped fiber Ring laser combined with an erbium doped fiber amplifier is first developed. The diaphragm based Fabry-Perot sensor using interferometric/intensity demodulated method is used as high sensitive optical fiber microphone. This system is applied to the detection of acetylene using a resonant photoacoustic cell on its second longitudinal mode. The experimental results demonstrate that the low concentration acetylene flowing measurement can be detected by optical microphone, and the amplitude of second harmonic is proportion to the concentration of acetylene. The linearity is 0.9999. At room temperature and atmospheric pressure, the minimum detectable limit of acetylene is about 1.5 parts-per-billion (ppb) volume (SNR=1), which can be compared with condense microphone.

A chip visual localization method based on vision for IC packaging equipment was developed to satisfy the high precision requirement in the chip inspection in this paper. The method is based on the combination of Hough transform and prior geometric knowledge of chip. First, a straight line was extracted to match chip image.Then the conversion function of the chip image was deduced based on the straight line endpoint coordinates. Because the relative position coordinates of chip corner are known, the other two endpoints of chip in image can be deduced with substituting the known model coners coordinates into the matching conversion function. Finally, the chip orientation is achieved by linking the corners coordinates. Experimental results have shown that the the chip can be orientated accurately using this method.

The rapid pace of change in modern medicine makes training on simulation platforms an absolute necessity. With the microprocessors the sophistication of simulators exploded. More recently medicine has developed simulators that are becoming increasingly sophisticated, especially in anesthesia training but also to train intensive care personnel. This is an area that will rapidly advance in the future and will have great impact on medical education medical. Unfortunately, the current state of development for simulated procedural environments remains rather limited and the availability of validated metrics of skills acquisition is just beginning. There are two impulse to push respiration simulation forward, as list following.

Spatial data mining refers to extracting and “mining” the hidden, implicit, valid, novel and interesting spatial or non-spatial patterns or rules from large-amount, incomplete, noisy, fuzzy, random, and practical spatial databases. Spatial association rules mining is extracted implicit association rules from spatial database. Many metallogenic association rules lie in geology spatial database. In this paper, a method for mineral resources prediction is proposed, which mainly including uncertainty transmission between qualitative and quantitative geology spatial data using cloud model, metallogenic association rules extracting using Apriori algorithm, and comprehensive assessment of rules. At last, an experiment of iron resources prediction is performed in Eastern Kunlun Mountains, China. The results indicated that the method proposed in this paper is suitable for regional metallogenic prediction.

Traditional qualitative methods and quantitative geostatistical methods are usually two kinds of ways for regional metallogenic prediction, the former's workloads are large and heavy, the latter's algorithm models are complex. In order to propose a new, simple and more accurate method, this paper carried out research work on Case-Based Reasoning and GIS approach to regional metallogenic prediction. The metallogenic Case-Based Reasoning model including metallogenic case expression model and metallogenic case retrieval model was well discussed, the reasoning flow was given, experiment to Gold mine prediction was fully done. The experiment results show the prediction of metallogenic Case-Based Reasoning is effective.

In breathing model study, most research focus on the characteristic of breathing system. Less study concentrate on the breath power. In order to get the sponeous breath model, respiratory muscle pressure must be taken into consideration. In this paper, the respiratory muscle pressure of autonomous respiration is used as source power and the simplest linear model is used in the model construction. Simulink is utilized as the tool, respiratory flow is obtained, which validated the model.

A novel waveform named double-slope symmetrical saw-tooth wave is presented and its corresponding algorithm is also introduced to resolve the problem of multiple targets detection for automotive anti-collision radar. The method by using the proposed waveform and algorithm improves the response characteristic of radar system and reduces false targets in detection process. Computer simulation results and theoretical analysis prove that the method is effective and practical for multiple targets detection in intelligence transportation system.

In this paper an attempt ofNIR analysis to measure the milk constituents is introduced. In milk, light is absorbed and scattered simultaneously. The concentrations of each component no longer have linear relationship to the absorbency, but proportional to the absorption coefficient and scattering coefficient. It is necessary to separate the effect of absorption and scattering. Compare with the widely used NIR method, we do not calibrate the prediction model by diffuse reflectance. The calibration is composed by two steps. Firstly, the optical parameters are measured by double integrating sphere; secondly, calibration is made by optical parameters. Concentration of protein and fat are within reasonable range. Fifty samples are acquired for calibration. The experimental result shows a higher prediction accuracy of protein than the intensity calibration method, and a comparable accuracy of fat.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We proposed and demonstrated a digital method of dispersion compensation suitable for spectral-domain optical coherence tomography. The wavelength coordinate of the coherence spectrum was calibrated digitally using a two-order polynomial. A software-based scheme was introduced to determine the polynomial coefficients of the polynomial fitting spectrum wavelength. Therefore, the spectrum deformation introduced by dispersion can be compensated effectively. This method was experimentally validated by in vivo imaging an early-stage chick embryonic heart.