Improved metrics for evaluating fault detection efficiency of test suite

By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out some limitations of the APFD series metrics. These limitations include APFD series metrics having inaccurate physical explanations and being unable to precisely describe the process of fault detection. To avoid the limitations of existing metrics, this paper proposes two improved metrics for evaluating fault detection efficiency of a test suite, including relative-APFD and relative-APFDc. The proposed metrics refer to both the speed of fault detection and the constraint of the testing source. The case study shows that the two proposed metrics can provide much more precise descriptions of the fault detection process and the fault detection efficiency of the test suite.

Oil Content Conversion between Ether-based and Ethanol-based Methods and Establishment of Admissible Error for Imported Wool

Due to various reasons, the inspection methods often need to be changed, and the detection reagents often need to be replaced. In this study, a comparative experiment was conducted between the ethanol-based and ether-based determination methods for oil content in imported wool. The determination results obtained from the two methods were treated as abscissa and ordinate respectively,and their linear relationship was analyzed. According to the linear regression analysis, the conversion equation of determination result between the two methods was obtained. In addition, the repeatability admissible error and reproducibility admissible error were established through analyzing the comparative experimental results by scientific software. This study will bring new ideas for further researches in this field, and provide reference for solving the similar problems in actual inspection work.

Review of Shadow Detection and De-shadowing Methods in Remote Sensing

Shadow is one of the major problems in remotely sensed imagery which hampers the accuracy of information extraction and change detection.In these images,shadow is generally produced by different objects,namely,cloud,mountain and urban materials.The shadow correction process consists of two steps:detection and de-shadowing.This paper reviews a range of techniques for both steps,focusing on urban regions(urban shadows),mountainous areas(topographic shadow),cloud shadows and composite shadows.Several issues including the problems and the advantages of those algorithms are discussed.In recent years,thresholding and recovery techniques have become important for shadow detection and de-shadowing,respectively.Research on shadow correction is still an important topic,particularly for urban regions(in high spatial resolution data) and mountainous forest(in high and medium spatial resolution data).Moreover,new algorithms are needed for shadow correction,especially given the advent of new satellite images.

Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

Traditional data driven fault detection methods assume that the process operates in a single mode so that they cannot perform well in processes with multiple operating modes. To monitor multimode processes effectively,this paper proposes a novel process monitoring scheme based on orthogonal nonnegative matrix factorization(ONMF) and hidden Markov model(HMM). The new clustering technique ONMF is employed to separate data from different process modes. The multiple HMMs for various operating modes lead to higher modeling accuracy.The proposed approach does not presume the distribution of data in each mode because the process uncertainty and dynamics can be well interpreted through the hidden Markov estimation. The HMM-based monitoring indication named negative log likelihood probability is utilized for fault detection. In order to assess the proposed monitoring strategy, a numerical example and the Tennessee Eastman process are used. The results demonstrate that this method provides efficient fault detection performance.

Orf in Goats in China： Prevalence and Risk Factors

Orf is an important viral disease that affects goats and sheep and results in large economic losses. The aim of this study was to investigate the prevalence of off and identify the potential risk factors of this disease in the main breeding areas of China. Among 1,241 blood samples collected from goats without clinical signs of off, 433 samples （34.89%） were positive for off virus infection, which was detected by polymerase chain reaction （PCR） targeting a partial B2L sequence of the viral genome. Moreover, a total of 874 buceal swab samples were collected, of which 64 samples （7.32%） were positive for the orf virus on the basis of PCR detection. According to logistic regression, all of the variables, including age, breed, location and farm management, had significant impacts on the prevalence of orf. Lambs under intensive management in Yunnan province were more susceptible to off virus infection than animals in other groups. Anglo-Nubian goats were at more risk of off positivity than other breeds, whereas Saanen dairy goats were at significantly less risk. In summary, as the first epidemiological study of off in China, this investigation suggested that off is a neglected disease that requires more attention in the future.

Discussions about the differences and credibility on grape wine scoring

The scores of grape wine given by wine critics have differences, therefore, for which its credibility differs. Firstly, our paper analysis differences of overall and individual for the two groups of raters. The analysis method is： first check the score whether meets the normal distribution or not by Chi-square and then test whether the mean of the two group＇s ratings is equal or not, through t-test, to illustrate the differences of the two groups＇ ratings. Secondly this paper respectively characterizes the credibility of each group＇s rating with the variance and gray correlation. Both the results are highly consistent on the credibility, so they can support each other. This score comparison method can be further extended to a similar scoring system.

Entropy method of measuring and evaluating periodicity of quasi-periodic trajectories

This paper presents a method for measuring the periodicity of quasi-periodic trajectories by applying discrete Fourier transform （DFT） to the trajectories and analyzing the frequency domain within the concept of entropy. Having introduced the concept of entropy, analytical derivation and numerical results indicate that entropies increase as a logarithmic function of time. Periodic trajectories typically have higher entropies, and trajectories with higher entropies mean the periodicities of the motions are stronger. Theoretical differences between two trajectories expressed as summations of trigonometric functions are also derived analytically. Trajectories in the Henon-Heiles system and the circular restricted three-body problem （CRTBP） are analyzed with the indicator entropy and compared with orthogonal fast Lyapunov indicator （OFLI）. The results show that entropy is a better tool for discriminating periodicity in quasiperiodie trajectories than OFLI and can detect periodicity while excluding the spirals that are judged as periodic cases by OFLI. Finally, trajectories in the vicinity of 243 Ida and 6489 Golevka are considered as examples, and the numerical results verify these conclusions. Some trajectories near asteroids look irregular, but their higher entropy values as analyzed by this method serve as evidence of frequency regularity in three directions. Moreover, these results indicate that applying DFT to the trajectories in the vicinity of irregular small bodies and calculating their entropy in the frequency domain provides a useful quantitative analysis method for evaluating orderliness in the periodicity of quasi-periodic trajectories within a given time interval.

A new method for retrieving equivalent cloud base height and equivalent emissivity by using the ground-based Atmospheric Emitted Radiance Interferometer (AERI)

In the paper, we propose a new method of identifying the clear sky based on the Atmospheric Emitted Radiance Interferometer （AERI）. Using the Atmospheric Radiation Measurement （ARM） Mobile Facility （AFM） dataset in Shouxian in 2008, we sim- ulate the downwelling radiances on the surface in the 8-12 μm window region using Line-By-Line Radiative Transfer Model （LBLRTM）, and compare the results with the AERI radiances, The differences larger （smaller） than 3 mW （cm2 sr cm-1）-1 suggest a cloudy （clear） sky. Meanwhile, we develop the new algorithms for retrieving the zenith equivalent cloud base height （CBHe） and the equivalent emissivity （ee）, respectively. The retrieval methods are described as follows. （1） An infinitely thin and isothermal blackbody cloud is simulated by the LBLRTM. The cloud base height （H） is adjusted iteratively to satisfy the situation that the contribution of the blackbody to the downwelling radiance is equal to that of realistic cloud. The final H is considered as CBHe. The retrieval results indicate that the differences between the CBHe and observational cloud base height （CBH） are much smaller for thick low cloud, and increase with the increasing CBH. （2） An infinitely thin and isothermal gray body cloud is simulated by the LBLRTM, with the CBH specified as the observed value. The cloud base emissivity （co） is ad- justed iteratively until the contribution of the gray body to the downwelling radiance is the same as that of realistic cloud. The corresponding εc is εe. The average εe for the low, middle, and high cloud is 0.967, 0.781, and 0.616 for the 50 cases, respec- tively. It decreases with the increasing CBH. The retrieval results will be useful for studying the role of cloud in the radiation budget in the window region and cloud parameterizations in the climate model.

High-precision time and frequency measurement method combining time-space conversion and different frequency phase detection

According to the time&space conversion relations and different frequency phase detection principle,an ultra-high precision time&frequency measurement method is proposed in this paper.The higher accuracy and stability of the speed of light and electromagnetic signals during the transmission in space or a specific medium enable the measurement of short time interval which uses the coincidence detection of signal’s transmission delay in length.The measurement precision better than 10 picoseconds can be easily obtained.The method develops the length vernier utilizing the stability of signal’s transmission delay,minimizes the fuzzy region of phase coincidence between the standard frequency signal and the measured signal,approaches the best phase coincidences and therefore improves the measurement precision which is higher than the precision provided by the traditional methods based on frequency processing.Besides,the method costs less than the traditional methods and can also solve the problem of the measurement of super-high frequency.Experimental results show the method can improve the measurement precision to 10 12/s in the time&frequency domain.