Anchorage performance of a new rebar bolt under different surrounding rock strength and borehole depth.

The theory and technology of rock bolting are fundamental research topics for strata control in civil and mining engineering. Rebar bolts are commonly used for roadway primary support in underground coal mine. To adapt to deep resource mining, a new left threaded rebar bolt has been developed. Compared to conventional rebar bolts, the result of installation test showed that the new bolt reduced of 41.5% and 57.9% in stirring resistance force and torque, respectively. In laboratory pullout tests, PVC and aluminum sleeves were used to simulate weak and medium strength surrounding rocks. The average peak pullout force, displacement at the peak load and energy absorption increased by 27%, 107% and 108%, respectively, using PVC sleeve; and increased by 113%, 109% and 212%, respectively, using aluminum sleeve. Field tests were conducted under soft coal, hard coal and medium strength rock geo-conditions. Different borehole depths were selected to precisely calculate the average anchorage performance of the new bolt. Results showed that the average peak pullout force of the new bolt increased by 37%, 38% and 28%, respectively, under different surrounding rock conditions. Moreover, based on on-site test results, the pullout curves in field-testing were summarised and classified into 6 different patterns, which were discussed from a viewpoint of causality mechanism. The research findings validate that the newly developed bolt has better anchorage performance compared to conventional rebar bolts, making it a new anchorage material for deep resource mining.

An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions.

Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions. To address these problems, a novel fault diagnosis method combining a generative adversarial network and transfer learning is proposed in this paper. Dummy samples with similar fault characteristics to the actual engineering monitoring data are generated by the generative adversarial network to expand the dataset. The transfer fault characteristics of monitoring data under different working conditions are extracted by a deep residual network. Domain-adapted regular term constraints are formulated to the training process of the deep residual network to form a deep transfer fault diagnosis model. The bearing fault data are used as the original dataset to validate the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the influence of insufficient original monitoring data and enable the migration of fault diagnosis knowledge under different working conditions.

O2O Method for Fast 2D Shape Retrieval.

A novel post-processing method, online to offline (O2O), to improve the efficiency of shape retrieval is proposed in this paper. The essence of this proposed method is to move more work that requires a lot of computation to offline. Based on this approach, the O2O rerank the retrieval result online with the help of the offline analysis. The result of offline analysis can be reused indefinitely, regardless of the query shape, as long as the database is unchanged. Therefore, O2O is very efficient and suitable for real-time applications. We evaluated our method for shape retrieval and recognition on five databases including MPEG-7 CE-1 Part B, Tari 1000, Animals, Kimia 99, and Swedish Plant Leaf. Our experimental results show that as a post-processing algorithm, O2O provides highly efficient and effective shape retrieval.

A Fourier Descriptor of 2D Shapes Based on Multiscale Centroid Contour Distances Used in Object Recognition in Remote Sensing Images.

A shape descriptor is an effective tool for describing the shape feature of an object in remote sensing images. Researchers have put forward a lot of excellent descriptors. The discriminability of some descriptors is very strong in the experiments, but usually their computational cost is large, which makes them unsuitable to be used in practical applications. This paper proposes a new descriptor-FMSCCD (Fourier descriptor based on multiscale centroid contour distance)-which is a frequency domain descriptor based on the CCD (centroid contour distance) method, multiscale description, and Fourier transform. The principle of FMSCCD is simple, and the computational cost is very low. What is commendable is that its discriminability is still strong, and its compatibility with other features is also great. Experiments on three databases demonstrate its strong discriminability and operational efficiency.

An auxiliary gaze point estimation method based on facial normal.

Considering the main disadvantage of the existing gaze point estimation methods which restrict user's head movement and have potential injury on eyes, we propose a gaze point estimation method based on facial normal and binocular vision. Firstly, we calibrate stereo cameras to determine the extrinsic and intrinsic parameters of the cameras; Secondly, face is quickly detected by Viola-Jones framework and the center position of the two irises can be located based on integro-differential operators; The two nostrils and mouth are detected based on the saturation difference and their 2D coordinates can be calculated; Thirdly, the 3D coordinates of these five points are obtained by stereo matching and 3D reconstruction; After that, a plane fitting algorithm based on least squares is adopted to get the approximate facial plane, then, the normal via the midpoint of the two pupils can be figured out; Finally, the point-of-gaze can be obtained by getting the intersection point of the facial normal and the computer screen. Experimental results confirm the accuracy and robustness of the proposed method.

Fast single image haze removal via local atmospheric light veil estimation.

In this study, a novel single-image based dehazing framework is proposed to remove haze artifacts from images through local atmospheric light estimation. We use a novel strategy based on a physical model where the extreme intensity of each RGB pixel is used to define an initial atmospheric veil (local atmospheric light veil). Across bilateral filter is applied to each veil to achieve both local smoothness and edge preservation. A transmission map and a reflection component of each RGB channel are constructed from the physical atmospheric scattering model. The proposed approach avoids adverse effects caused by the error in estimating the global atmospheric light. Experimental results on outdoor hazy images demonstrate that the proposed method produces image output with satisfactory visual quality and color fidelity. Our comparative study demonstrates a higher performance of our method over several state-of-the-art methods.

A fast color image enhancement algorithm based on Max Intensity Channel.

In this paper, we extend image enhancement techniques based on the retinex theory imitating human visual perception of scenes containing high illumination variations. This extension achieves simultaneous dynamic range modification, color consistency, and lightness rendition without multi-scale Gaussian filtering which has a certain halo effect. The reflection component is analyzed based on the illumination and reflection imaging model. A new prior named Max Intensity Channel (MIC) is implemented assuming that the reflections of some points in the scene are very high in at least one color channel. Using this prior, the illumination of the scene is obtained directly by performing a gray-scale closing operation and a fast cross-bilateral filtering on the MIC of the input color image. Consequently, the reflection component of each RGB color channel can be determined from the illumination and reflection imaging model. The proposed algorithm estimates the illumination component which is relatively smooth and maintains the edge details in different regions. A satisfactory color rendition is achieved for a class of images that do not satisfy the gray-world assumption implicit to the theoretical foundation of the retinex. Experiments are carried out to compare the new method with several spatial and transform domain methods. Our results indicate that the new method is superior in enhancement applications, improves computation speed, and performs well for images with high illumination variations than other methods. Further comparisons of images from National Aeronautics and Space Administration and a wearable camera eButton have shown a high performance of the new method with better color restoration and preservation of image details.