An Improved Minimum Distance Method Based on Artificial Neural Networks

MDM (minimum distance method) is a very popular algorithm in staterecognition. But it has a presupposition, that is, the distance within one class must be shorterenough than the distance between classes. When this presupposition is not satisfied, the method isno longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstratethat IMDM has two advantages, that is, the rate of recognition is faster and the accuracy ofrecognition is higher compared with MDM.

A method for computing unsupported roof distance in roadway advancement and its in-situ application

A reasonable unsupported roof distance(URD) when advancing underground coal mine roadways can contribute greatly to safe and rapid roadway development.A mechanical model of the roof,using the relationship between the roof stress distribution and URD,obtained by the difference method,and roof stability according to the in-situ roof stress and rock mass strength was developed.We subsequently designed a proper range of URD,developed a testing method of URD with the function of mining protection,evaluated roof stability through analyzing the test data and then determined a reasonable URD.Considering the factors of the geological conditions,the immediate roof stability and the efficiency of the labor arrangement system,the URD of the advancing roadway of 9802 working face in Zhangshuanglou coal mine was determined to be 6 m using the proposed method.The results show that,when a 2 m length of roadway was reinforced by temporary support and high pre-stressed bolt support after the roadway advancement of 6 m per cycle,the speed and the security of the roadway development can be achieved and the advance rate can reach more than 400 m per month.

Changes of coastline and tidal flat and its implication for ecological protection under human activities: Take China’s Bohai Bay as an example

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area,which is also of great significance for understanding coastal ecological environment changes and even global changes.Based on field measurements,combined with Linear Regression(LR)model and Inverse Distance Weighing(IDW)method,this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay.The shoreline faces a high erosion chance under the action of natural factors,while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities.The implication of change rule for ecological protection and recovery is also discussed.Measures should be taken to protect the coastal ecological environment.The models used in this paper show a high correlation coefficient between observed and modeling data,which means that this method can be used to predict the changing trend of shoreline and tidal flat.The research results of present study can provide scientific supports for future coastal protection and management.

Assessment of different genetic distances in constructing cotton core subset by genotypic values

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances(Euclidean,standardized Euclidean,Mahalanobis,city block,cosine and correlation distances) combining four commonly used hierarchical cluster methods(single distance,complete distance,unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling(LDSS) method for constructing different core subsets. The analyses of variance(ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean,standardized Euclidean,Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean,Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages,which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

A New Method for Detecting Internal Defects in Composite Materials Based on Time of Flight

Carbon-fiber reinforced polymer composites have been widely used to achieve the light-weighted design and high performance due to superior performance. Internal defects in the composite materials are the main factors that determine their performance,which makes reliable and effective detection methods of internal defects essential. Nondestructive testing(NDT)methods are the most widely-used way due to their tremendous advantages. Though the theoretical background is found,experimental results could be quite complicated and confusing,especially for composite materials with complex defects characteristics. In this paper,experimental study on internal defects in composite materials based on the time of flight(ToF)are investigated. The Gaussian echo model and the parameter estimation methods are established to build a theoretical model for measurements. Then,the distance amplitude correction(DAC)method is proposed to effectively improve the signal-to-noise ratio(SNR)and to reduce distortion of the signal during measurements. Finally,the ToF is adopted to determine depth of internal defects. Experiment study is conducted to investigate the porosity defects and the anti-impact performance of composite materials,as well as defects in objects with various thicknesses. Experimental results show that the proposed method is quite helpful for obtaining the intuition and deep understanding of internal defects,thus contributing to the determination of product performance and its improvement.

Similarity evaluation of stratum anti-drilling ability and a new method of drill bit selection

Considering the stratum anti-drilling ability,drill bit working conditions,drill bit application effect and drill bit economic benefits,the similarity of stratum anti-drilling ability was evaluated by grey relational analysis theory to screen out candidate drill bits with reference values.A new comprehensive performance evaluation model of drill bit was established by constructing the absolute ideal solution,changing the relative distance measurement method,and introducing entropy weight to work out the closeness between the candidate drill bits and ideal drill bits and select the reasonable drill bit.Through the construction of absolute ideal solution,improvement of relative distance measurement method and introduction of entropy weight,the inherent defects of TOPSIS decision analysis method,such as non-absolute order,reverse order and unreasonable weight setting,can be overcome.Simple in calculation and easy to understand,the new bit selection method has good adaptability to drill bit selection using dynamic change drill bit database.Field application has proved that the drill bits selected by the new drill bit selection method had significant increase in average rate of penetration,low wear rate,and good compatibility with the drilled formations in actual drilling.This new method of drill bit selection can be used as a technical means to select drill bits with high efficiency,long life and good economics in oilfields.

Are Type Ia Supernovae Reliable Distance Indicators?

Recent applications of type Ia supernovae （SNe Ia） in cosmology have successfully revealed the accelerating expansion of the universe. However, as distance indicators used in measuring the expansion history of the universe and probing the nature of dark energy, these objects must pass more strict tests. We propose a K-S test to investigate if there exists any systematic bias when deriving the luminosity distances under the standard candle assumption. Two samples, one comprising 71 high-redshift SNe Ia and the other, 44 nearby ones, are used in our investigation. We find that it is likely there exists a bias in the adopted samples, which is probably caused by a systematic error, e.g. in the color parameter used in the luminosity calibration and a bias may be caused by the SN evolution or by varying properties of the dust surrounding the SNe Ia.

Research on the Model of Linear Data Fitting Method

By using the method of least square linear fitting to analyze data do not exist errors under certain conditions, in order to make the linear data fitting method that can more accurately solve the relationship expression between the volume and quantity in scientific experiments and engineering practice, this article analyzed data error by commonly linear data fitting method, and proposed improved process of the least distance squ^re method based on least squares method. Finally, the paper discussed the advantages and disadvantages through the example analysis of two kinds of linear data fitting method, and given reasonable control conditions for its application.

The revised distance of supernova remnant G15.4+0.1

We measure the distance to the supernova remnant G15.4±0.1 which is likely associated with TeV source HESS J1818-154. We build the neutral hydrogen （HI） absorption and 13CO spectra for supernova remnant G 15.4±0.1 by employing data from the Southern Galactic Plane Survey （SGPS） and the HI/OH/Recombination line survey （THOR）. The maximum absorption velocity of about 140 km s-1 constrains the lower limit of its distance to about 8.0 kpc. Further, the fact that the HI emission feature at about 95 km s-1 seems to have no corresponding absorption suggests that G 15.4±0.1 likely has an upper limit for distance of about 10.5 kpc. The 13CO spectrum for the remnant supports our measurement. The new distance provides revised parameters on its associated pulsar wind nebula and TeV source.

Evaluation model of individual thermal comfort based on mean skin temperature

The local skin temperatures of 22 subjects at air temperatures of 21,24,26,29 ℃ are measured,and the mean skin temperatures are calculated by ten skin temperature measuring points.The thermal comfort levels and the thermal sensations of these subjects are also investigated by a questionnaire.The Mahalanobis distance discrimination method is applied to establish the evaluation model for the thermal comfort based on the mean skin temperature.The experimental results indicate that the difference of the mean skin temperatures between the comfort level and the discomfort level is significant.Using the evaluation model,the mean skin temperature at the thermal comfort level is 32.6 to 33.7 ℃,and the thermal comfort levels of 72% of the subjects are correctly evaluated.The accuracy of the evaluation model can be improved when the effects of sex of the subject on the mean skin temperature and the thermal comfort are considered.It can be concluded that the mean skin temperature can be used as an effective physiological indicator to evaluate human thermal comfort in a steady thermal environment.

Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images.Thus,there were lots of efforts trying to automate the classification operation and retrieve similar images accurately.To reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically.Then,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network.The Siamese model built and trained at first from scratch but,it didn’t generated high evaluation metrices.Thus,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation metrices.Afterward,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved images.Reaching that the highest evaluation parameters generated using the Cosine distance metric.Moreover,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time efficiently.After extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.

Evaluation of the asset＇s value by using fuzzy two-stage discounted cash flow model

This paper establishes the fuzzy discounted cash flow model to settle the uncertainties of the cash flow and discount rate in two-stage DCF model, to take the imprecise of the time period of the supernormal growth phase with considering the investor＇s attitude to risk. Firstly, the discount rate and the growth rate are fuzzified as triangular fuzzy numbers in this fuzzy discounted cash flow model. Then the intrinsic value of an asset can be evaluated by the arithmetic operation on interval and λ- signed distance method under the framework of DCF approach. Finally, a listed company at Shanghai Stock Exchange is analyzed as the case to demonstrate the process of stock value calculation by the fuzzy discounted cash flow model.

Application of Log Kriging on Estimated Reserves of the 10-9 Ore Body of Lutangba in the Gejiu Tin Deposits

The approaches adopted so far in China to calculate reserves of ore deposits have largely been dependent on polygonal methods, including a cross-section method and a geological block method. However, for various reasons, especially, the complexity of the geology, and the ore bodies, the deposits became gradually known as development and exploitation took place. There is a certain difference between the prospecting and mining, which will have a great impact on both geological and cutoff grade at the stage of geological mine construction and production. As much accurate information as possible on the spatial distribution patterns of ore bodies and global reserves is essential to improve the production efficiency and reduce production cost. The 10-9 ore body of Lutangba in the Gejiu Tin Deposits was selected as our research object, using a log Kriging and an inverse distance weighing method. In our paper the results are compared with the reserves calculated by the polygonal block method. The global estimate and estimation errors associated with each estimation method are discussed.

Interaction analyses between tunnel and landslide in mountain area

This paper focuses on the analytical derivation and the numerical simulation analyses to predict the interaction influences between a landslide and a new tunnel in mountain areas. Based on the slip-line theory, the disturbance range induced by tunneling and the minimum safe distance between the tunnel vault and the sliding belt are obtained in consideration of the mechanical analyses of relaxed rocks over the tunnel opening. The influence factors for the minimum safe crossing distance are conducted,including the tunnel radius, the friction angle of surrounding rocks, the inclination angle of sliding belt,and the friction coefficient of surrounding rocks. Secondly, taking account of the compressive zone and relaxed rocks caused by tunneling, the Sarma method is employed to calculate the safety factor of landslide. Finally, the analytical solutions for interaction between the tunnel and the landslide are compared with a series of numerical simulations, considering the cases for different perpendicular distances between the tunnel vault and the sliding belt. Resultsshow that the distance between the tunnel vault and the slip zone has significant influence on the rock stress and strain. For the case of the minimum crossing distance, a plastic zone in the landslide traversed by tunneling would be formed with rather large range, which seriously threatens the stability of landslide. This work demonstrates that the minimum safe crossing distance obtained from numerical simulation is in a good agreement with that calculated by the proposed analytical solutions.

Numerical analysis of submarine landslides using a smoothed particle hydrodynamics depth integral model

Submarine landslides can cause severe damage to marine engineering structures. Their sliding velocity and runout distance are two major parameters for quantifying and analyzing the risk of submarine landslides.Currently, commercial calculation programs such as BING have limitations in simulating underwater soil movements. All of these processes can be consistently simulated through a smoothed particle hydrodynamics（SPH） depth integrated model. The basis of the model is a control equation that was developed to take into account the effects of soil consolidation and erosion. In this work, the frictional rheological mode has been used to perform a simulation study of submarine landslides. Time-history curves of the sliding body＇s velocity, height,and length under various conditions of water depth, slope gradient, contact friction coefficient, and erosion rate are compared; the maximum sliding distance and velocity are calculated; and patterns of variation are discussed.The findings of this study can provide a reference for disaster warnings and pipeline route selection.

Photometric redshift estimation for quasars by integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches （such as the k-nearest neighbor （KNN） algorithm）. In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine （SVM） methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding Zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of Zphot E [0.3, 1.2] and the other in the range of Zphot E [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey （SDSS） quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different /△z/ ranges and root mean square （rms） error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN （71.96%, 83.78%, 89.73% and 0.204）.

Improved Approach Based on SVM for License Plate Character Recognition

An improved approach based on support vector machine （SVM） called the center distance ratio method is presented for license plate character recognition. First the support vectors are pre-extraeted. A minimal set called the margin vector set, which contains all support vectors, is extracted. These margin vectors compose new training data and construct the classifier by using the general SVM optimized. The experimental resuhs show that the improved SVM method does well at correct rate and training speed.

Improved Support Vector Machine Approach Based on Determining Thresholds Automatically

To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.

Multiple mobile-obstacle avoidance algorithm for redundant manipulator

In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacles and a manipulator was obtained according to the states of obstacles and transformed to escape velocity of the corresponding link of the manipulator.The escape velocity was introduced to the gradient projection method to obtain the joint velocity of the manipulator so as to complete the obstacle avoidance trajectory planning.A7-DOF manipulator was used in the simulation,and the results verified the effectiveness of the algorithm.

Multicolor photometry of the nearby galaxy cluster A119

This paper presents multicolor optical photometry of the nearby galaxy cluster Abell 119 （z = 0.0442） with the Beijing-Arizona-Taiwan-Connecticut system of 15 intermediate bands. Within the BATC field of view of 58＇× 58＇, there are 368 galaxies with known spectroscopic redshifts, including 238 member galaxies （called sample I）. Based on the spectral energy distributions of 1376 galaxies brighter than iBATC ---- 19.5, the photometric redshift technique and the color-magnitude relation of early-type galaxies are applied to select faint member galaxies. As a result, 117 faint galaxies were selected as new member galaxies. Combined with sample I, an enlarged sample （called sample II） of 355 member galaxies is obtained. Spatial distribution and localized velocity structure for two samples demonstrate that A119 is a dynami- cally complex cluster with at least three prominent substructures in the central region within 1 Mpc. A large velocity dispersion for the central clump indicates a merging along the line of sight. No significant evidence for morphology or luminosity segrega- tions is found in either sample. With the PEGASE evolutionary synthesis model, the environmental effect on the properties of star formation is confirmed. Faint galaxies in the low-density region tend to have longer time scales of star formation, smaller mean stellar ages, and lower metallicities in their interstellar medium, which is in agreement with the context of the hierarchical cosmological scenario.