Fingerprints of two varieties of rice and their mixtures were investigated by a nonlinear chemical reaction system consisting of rice components, sodium bromate, manganese sulfate, sulfuric acid and acetone. The variety of rice was identified by the visual characteristic of fingerprint and system similarity pattern recognition, and the content of each variety of rice in the mixture was determined by the quantitative information of fingerprint. The results show that nonlinear chemical analysis may be used to exactly identify the variety of pure rice and to accurately determine the content of each variety of rice in the mixture, indicating the method is simple and convenient.

Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval. However, there are some problems in both of them: 1) the methods defining directly texture in color space put more emphasis on color than texture feature; 2) the methods extract several features respectively and combine them into a vector, in which bad features may lead to worse performance after combining directly good and bad features. To address the problems above, a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision. The bag-of-visual words (BoW) models and color intensity-based local difference patterns (CILDP) are exploited to capture local and global features of an image. The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method. The performance of our proposed framework in terms of average precision on Corel-1K database is 86.26%, and it improves the average precision by approximately 6.68% and 12.53% over CILDP and BoW, respectively. Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.

Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components. In order to predict the surface roughness in ultrasonic vibration assisted grinding of brittle materials, the surface morphologies of grinding wheel were obtained firstly in the present work, the grinding wheel model was developed and the abrasive trajectories in ultrasonic vibration assisted grinding were also investigated, the theoretical model for surface roughness was developed based on the above analysis. The prediction model was developed by using Gaussian processing regression (GPR) due to the influence of brittle fracture on machined surface roughness. In order to validate both the proposed theoretical and GPR models, 32 sets of experiments of ultrasonic vibration assisted grinding of BK7 optical glass were carried out. Experimental results show that the average relative errors of the theoretical model and GPR prediction model are 13.11% and 8.12%, respectively. The GPR prediction results can match well with the experimental results.

A dynamic model of a helical gear rotor system is proposed. Firstly, a generally distributed dynamic model of a helical gear pair with tooth profile errors is developed. The gear mesh is represented by a pair of cylinders connected by a series of springs and the stiffness of each spring is equal to the effective mesh stiffness. Combining the gear dynamic model with the rotor-bearing system model, the gear-rotor-bearing dynamic model is developed. Then three cases are presented to analyze the dynamic responses of gear systems. The results reveal that the gear dynamic model is effective and advanced for general gear systems, narrow-faced gear, wide-faced gear and gear with tooth profile errors. Finally, the responses of an example helical gear system are also studied to demonstrate the influence of the lead crown reliefs and misalignments. The results show that both of the lead crown relief and misalignment soften the gear mesh stiffness and the responses of the gear system increase with the increasing lead crown reliefs and misalignments.

A new method for interaction recognition based on sparse representation of feature covariance matrices was presented. Firstly, the dense trajectories (DT) extracted from the video were clustered into different groups to eliminate the irrelevant trajectories, which could greatly reduce the noise influence on feature extraction. Then, the trajectory tunnels were characterized by means of feature covariance matrices. In this way, the discriminative descriptors could be extracted, which was also an effective solution to the problem that the description of the feature second-order statistics is insufficient. After that, an over-complete dictionary was learned with the descriptors and all the descriptors were encoded using sparse coding (SC). Classification was achieved using multiple instance learning (MIL), which was more suitable for complex environments. The proposed method was tested and evaluated on the WEB Interaction dataset and the UT interaction dataset. The experimental results demonstrated the superior efficiency.

The car sequencing problem (CSP) concerns a production sequence of different types of cars in the mixed-model assembly line. A hybrid algorithm is proposed to find an assembly sequence of CSP with minimum violations. Firstly, the hybrid algorithm is based on the tabu search and large neighborhood search (TLNS), servicing as the framework. Moreover, two components are incorporated into the hybrid algorithm. One is the parallel constructive heuristic (PCH) that is used to construct a set of initial solutions and find some high quality solutions, and the other is the small neighborhood search (SNS) which is designed to improve the new constructed solutions. The computational results show that the proposed hybrid algorithm (PCH+TLNS+SNS) obtains 100 best known values out of 109 public instances, among these 89 instances get their best known values with 100% success rate. By comparing with the well-known related algorithms, computational results demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.

This work describes an experimental investigation into the influence of geometric micro-groove texture patterns on the tribological performance of stainless steel. Five geometries were studied: one with untextured and four with micro-groove textured making parallel, triangular, square and hexagonal patterns. The micro-groove textures were produced using an MFT-20 laser system as well as a two-step laser surface texturing (LST) process. Tribological performance was measured using a pin-on-disk tribometer. The investigation showed that the two-step LST process could fabricate high-precision micro-grooves. The experimental data indicated that the micro-groove textured surfaces achieved the lower frictional coefficients than the untextured surface and the geometric patterns had significantly affected the tribological properties of samples in both lubricated and unlubricated states. The results were analyzed from the lubricant supplying and fluid dynamic pressure effect under lubricated conditions as well as abrasive capture and remove under dry friction conditions.

The correspondence analysis will describe elemental association accompanying an indicator samples. This analysis indicates strong mineralization of Ag, As, Pb, Te, Mo, Au, Zn and to a lesser extent S, W, Cu at Glojeh polymetallic mineralization, NW Iran. This work proposes a backward elimination approach (BEA) that quantitatively predicts the Au concentration from main effects (X), quadratic terms (X²) and the first order interaction (X_i×X_j) of Ag, Cu, Pb, and Zn by initialization, order reduction and validation of model. BEA is done based on the quadratic model (QM), and it was eliminated to reduced quadratic model (RQM) by removing insignificant predictors. During the QM optimization process, overall convergence trend of R², R²(adj) and R²(pred) is obvious, corresponding to increase in the R²(pred) and decrease of R². The RQM consisted of (threshold value, Cu, Ag×Cu, Pb×Zn, and Ag²–Pb²) and (Pb, Ag×Cu, Ag×Pb, Cu×Zn, Pb×Zn, and Ag²) as main predictors of optimized model according to 288 and 679 litho-samples in trenches and boreholes, respectively. Due to the strong genetic effects with Au mineralization, Pb, Ag², and Ag×Pb are important predictors in boreholes RQM, while the threshold value is known as an important predictor in the trenches model. The RQMs R²(pred) equal 74.90% and 60.62% which are verified by R² equal to 73.9% and 60.9% in the trenches and boreholes validation group, respectively.

The characteristics of joints are crucial factors which influence the penetration efficiency of tunnel boring machine (TBM). Based on the theoretical study, numerical simulation and experimental research, many researchers have studied the interaction between TBM disc cutters and jointed rock mass. However, in most of these works, the effect of joint on rock fragmentation by double disc cutter has been scarcely investigated. Thus, the effects of joint orientation and joint space on rock fragmentation by double disc cutter are highlighted in this study. During the test, jointed concrete specimens are adopted to simulate jointed rock mass. Improved RYL-600 rock shear rheological instrument was employed during the indentation process under disc cutters, and acoustic emission location system was used to analyze the rock damage and physical deterioration. The results show that there are four failure modes and three modes of crack initiation and propagation in jointed rock mass. It is concluded that the existing joint planes have obviously restrained the crack initiation and propagation during the rock fragmentation process. The results also indicate that samples are damaged most seriously when joint orientation equals 60°, which is proved to be the optimum joint orientation in TBM penetration.

The purpose of this work is to predict the state of collapse in shallow tunnel in layered strata by using a new curved failure mechanism within the framework of upper bound theorem. Particular emphasis is first given to consider the effects of seepage forces and surface settlement. Furthermore, the Hoek-Brown nonlinear failure criterion is adopted to analyze the influence of different factors on the collapsing shape. Two different curve functions which describe two different rock layers are obtained by virtual work equations under the variational principle. According to the numerical results, the parameter B in Hoek-Brown failure criterion and the unit weights in different rock layers have a positive relationship with the size of collapsing block while pore pressure coefficient and the parameter A in Hoek-Brown failure criterion present a reverse tend.

Tunnel water inrush is one of the common geological disasters in the underground engineering construction. In order to effectively evaluate and control the occurrence of water inrush, the risk assessment model of tunnel water inrush was proposed based on improved attribute mathematical theory. The trigonometric functions were adopted to optimize the attribute mathematical theory, avoiding the influence of mutation points and linear variation zones in traditional linear measurement functions on the accuracy of the model. Based on comprehensive analysis of various factors, five parameters were selected as the evaluation indicators for the model, including tunnel head pressure, permeability coefficient of surrounding rock, crushing degree of surrounding rock, relative angle of joint plane and tunnel section size, under the principle of dimension rationality, independence, directness and quantification. The indicator classifications were determined. The links among measured data were analyzed in detail, and the objective weight of each indicator was determined by using similar weight method. Thereby the tunnel water inrush risk assessment model is established and applied in four target segments of two different tunnels in engineering. The evaluation results and the actual excavation data agree well, which indicates that the model is of high credibility and feasibility.

Cone penetration test (CPT) is an appropriate technique for quickly determining the geotechnical properties of lunar soil, which is valuable for in situ lunar exploration. Utilizing a typical coupling method recently developed by the authors, a finite element method (FEM)-discrete element method (DEM) coupled model of CPTs is obtained. A series of CPTs in lunar soil are simulated to qualitatively reveal the flow of particles and the development of resistance throughout the penetration process. In addition, the effects of major factors, such as penetration velocity, penetration depth, cone tip angle, and the low gravity on the Moon surface are investigated.

Deformation prediction and the analysis of underground goaf are important to the safe and efficient recovery of residual ore when shifting from open-pit mining to underground mining. To address the comprehensive problem of stability in the double mined-out area of the Tong-Lv-Shan (TLS) mine, which employed the dry stacked gangue technology, this paper applies the function fitting theory and a regression analysis method to screen the sensitive interval of four influencing factors based on single-factor experiments and the numerical simulation software FLAC^3D. The influencing factors of the TLS mine consist of the column thickness (d), gob area span (D), boundary pillar thickness (h) and height of tailing gangue (H). The fitting degree between the four factors and the displacement of the gob roof (W) is reasonable because the correlation coefficient (R²) is greater than 0.9701. After establishing 29 groups that satisfy the principles of Box-Behnken design (BBD), the dry gangue tailings process was re-simulated for the selected sensitive interval. Using a combination of an analysis of variance (ANOVA), regression equations and a significance analysis, the prediction results of the response surface methodology (RSM) show that the significant degree for the stability of the mined-out area for the factors satisfies the relationship of h>D>d>H. The importance of the four factors cannot be disregarded in a comparison of the prediction results of the engineering test stope in the TLS mine. By comparing the data of monitoring points and function prediction, the proposed method has shown promising results, and the prediction accuracy of RSM model is acceptable. The relative errors of the two test stopes are 1.67% and 3.85%, respectively, which yield satisfactory reliability and reference values for the mines.

Reliability and remaining useful life (RUL) estimation for a satellite rechargeable lithium battery (RLB) are significant for prognostic and health management (PHM). A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data, including bivariate degradation data and lifetime data. Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system. First, linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB’s temperature and discharge voltage. Next, the Bayesian method, in combination with Markov Chain Monte Carlo (MCMC) simulations, is provided to integrate limited bivariate degradation data with other congeneric RLBs’ lifetime data. Then reliability evaluation and RUL prediction are carried out for PHM. A simulation study demonstrates that due to the data fusion, parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes. Finally, a practical case study of a satellite RLB verifies the usability of the model.

To predict the soil–water characteristic curve (i.e. SWCC) of natural and remoulded Malan loess from soil physical properties, one-point methods for determining the SWCC that are much simpler than experimental methods are proposed. The predicted SWCC is presented in the form of the BRUTSAERT equation, in which the four model parameters can be estimated from soil physical properties using the best correlations obtained in the present study along with one measured data point. The proposed one-point methods are validated using the measured SWCC data reported in the literature. The results of validation studies suggest that the proposed one-point methods can provide reasonable prediction of the SWCC for natural and remoulded Malan loess. The measured data point should be within the transition zone; the measured suction is suggested between 25 to 100 kPa for natural loess, while between 100 to 500 kPa for remoulded loess.

Failure of the surrounding rock around a roadway induced by roof separation is one major type of underground roof-fall accidents. This failure can especially be commonly-seen in a bottom-driven roadway within an extra-thick coal seam (“bottom-driven roadway” is used throughout for ease of reference), containing weak partings in their roof coal seams. To determine the upper limit position of the roof interlayer separation is the primary premise for roof control. In this study, a mechanical model for predicting the interlayer separation overlying a bottom-driven roadway within an extra-thick coal seam was established and used to deduce the vertical stress, and length, of the elastic, and plastic zones in the rock strata above the wall of the roadway as well as the formulae for calculating the deflection in different regions of rock strata under bearing stress. Also, an approach was proposed, calculating the stratum load, deflection, and limiting span of the upper limit position of the interlayer separation in a thick coal seam. Based on the key strata control theory and its influence of bedding separation, a set of methods judging the upper limit position of the roof interlayer separation were constructed. In addition, the theoretical prediction and field monitoring for the upper limit position of interlayer separation were conducted in a typical roadway. The results obtained by these two methods are consistent, indicating that the methods proposed are conducive to improving roof control in a thick coal seam.