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The purpose of this paper is to present research results in analyzing image contents to improve the accuracy of using an artificial neural network (ANN) to detect embedded data in a digital image.

A texture measure based on the MPEG‐7 texture descriptor is applied to assess the local texture amount. Those image blocks with high texture are masked out and the remaining blocks with low texture are used to derive features for an ANN to classify an image as embedded or clear. The high‐texture blocks are not discarded and can be tested independently for embedded data.

By masking out the high‐texture image blocks, an ANN has improved detection performance especially when the original embedding rate is low. Bypassing the low‐texture image blocks do not pay off for a steganographer because the effective embedding rate in the high‐texture blocks is driven higher.

Hidden data detectors should take the image content into account in order to improve detection performance.

The results can be integrated into a steganalytic system.

This paper presents evidence that image texture affects steganalytic performance and proposes a solution that incorporates texture that has improved detection performance.

In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years.

First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared.

The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR.

A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

This study aims to investigate the performance improvement of journal bearing by applying the arc-shaped textures on various regions of bearing expressly full, second half and pressure increasing regions operating with and without nanoparticles in the lubricant.

The Reynolds equation is solved numerically by using the finite element method to obtain static performance parameters such as load-carrying capacity (LCC) and coefficient of friction (COF), which are then compared with untextured bearing at eccentricity ratios of 0.2 to 0.8. Aluminum oxide (Al₂O₃) and copper oxide (CuO) nanoparticles additives are used, and viscosity variation due to the addition of additives in the base lubricant is computed for considering the range of temperatures 50 to 90°C at a weight fraction of 0.1 to 0.5% by using an experimentally validated regression model.

The results indicate that the maximum LCC and the lower COF are found in the pressure-increasing region. A maximum increase of 34.42% is observed in the pressure-increasing region without nanoparticles, and furthermore, with the addition of Al₂O₃ and CuO nanoparticles in lubricants in the same region, the LCC increased to 21 and 24%, respectively.

Designers should use optimal parameters from the present work to achieve high bearing performance.

Surface texturing has emerged in the past two decades as a viable option of surface engineering, resulting in significant improvement in wear resistance and friction coefficient. The purpose of this study is to find the appropriate surface texture to reduce vibration and improve the stability of journal bearings.

Micro-dimples, evenly distributed in a square array, were selected as the texture pattern and formed on the lower surface of bush by the laser surface texturing technique. Experiments were carried out to evaluate the effects of micro-dimples under different depths, densities and distributions.

The results are summarized in the form of shaft center orbits, waterfall illustrations and Hilbert-Huang transforms. In the entire test, it was found that an optimum geometric and distributive range of micro-dimples exists, where vibration acceleration can be decreased at least 3dB and stability can be greatly improved.

A majority of researchers devoted to studying on static characteristics, such as friction coefficient, load carrying capacity, pressure distribution and cavitation model. Besides, the influence of surface texture on stability of rotor-journal bearing system was rarely investigated and the recent examples can be found in Refs. (Ausas et al. 2007). However, a complete study of textured journal bearings has not been undertaken in the dynamic properties. Therefore, the purpose of this paper is to experimentally investigate the comprehensive effects of density, depth and distribution of micro-dimples on bearing vibration and stability.

The purpose of this paper is to investigate the use of steam in order to replace air in the production of spun-like textured yarns. Further, this paper analyse the effect of production speed on process and textured yarn properties.

An existing air-jet texturing machine was modified to supply either air or steam to the texturing nozzle. Using standard commercial nozzles, both air-jet and steam-jet textured yarns were manufactured by varying production speed.

It can be concluded that steam can be used as an alternative fluid for air in making spun-like textured yarns. Results show that yarn parameters for steam-jet texturing show a similar trend to those of air-jet texturing relative to the production speed. Further, sewing threads made from steam-jet textured yarns showed good sewability up to the speeds of 350 m/min.

Only the effect of production speed on process and yarn parameters is discussed in this paper. Production speed was limited to 350 m/min due to machine constraints.

Steam is more economical than air to manufacture spun-like textured yarn at commercial pressures such as 8 bar. Steam-jet textured yarns could be used for commercial applications such as sewing threads at competitive speeds. Further, steam could be generated using sustainable and renewable fuel sources such as biomass.

This research introduced steam as an alternative fluid for air in manufacturing spun-like textured yarns.

This study aims to investigate the efficacy of micro dimple in inhibiting stick-slip phenomenon on the sliding guideway.

In this study, micro-dimples were fabricated by laser on surfaces of steel disk and guideway. The disks and guideways were respectively performed pin-on-disk tribological tests and working condition experiments to study differences in lubrication condition and friction stability between textured and untextured surfaces.

Micro-dimples help reduce critical sliding speed that allows contact surfaces to enter in hydrodynamic lubrication regime. This increases hydrodynamic lubrication range and narrows speed range where stick-slip phenomenon can occur, enhancing sliding guideway’s adaptability for broader working conditions. Furthermore, friction stability on the textured surface improved, lowering the occurrence possibility of stick-slip phenomenon. Finally, difference between static and kinetic frictions on the textured surface is lower relative to the untextured surface, which decreases the critical velocity when the stick-slip phenomenon occurs.

The results indicate that laser-textured micro-dimples are significantly conducive to inhibit stick-slip phenomenon, thus providing smoother movement for the guideway and eventually increasing precision of the machine.

This study aims to understand how the texture shape, number of textures and addition of nanoparticle additives in lubricants impact the dynamic characteristics of journal bearing by comparing six different texture shapes like triangle, chevron, arc, circle, rectangle and elliptical applied in pressure-increasing region under various geometrical and operating conditions.

The finite element method approach has been employed to solve governing Reynold’s equation, assuming iso-viscous Newtonian fluid, for computation of performance parameters like stiffness and damping coefficient, threshold speed, etc. By using a regression model, the impact of adding nanoparticles Al₂O₃ and CuO to the base lubricant on viscosity variation is calculated for selected temperature ranges and weight fractions of nanoparticles.

The arc-shaped texture with an area density of 28.27%, eccentricity ratio of 0.2 and texture depth of 0.6 exhibited 35.22% higher direct stiffness and 41.4% higher damping coefficient compared to the lowest value in the circle-shaped texture. Increasing the number of arc-shaped textures on the bearing surface with low area density led to declining stiffness and damping parameters. However, with nanoparticle additives, the arc-shaped texture further showed 10.75% and 8.11% improvement in stiffness and 9.99% and 4.87% enhancement in damping coefficient for Al₂O₃ and CuO, respectively, at 90 °C temperature and 0.5% weight fraction.

By understanding the influence of texture shapes on the dynamic characteristics, engineers can design bearings that exhibit improved stability and enhance overall performance.

Surface texturing can improve the tribological performance of contacting sliding surfaces under different contact and lubrication conditions, which has been proved both numerically and experimentally. This study aims to suggest a new methodology to evaluate the tribological behavior of textured surfaces using an adapted pendular scratch tester.

A Charpy-type tester was adapted to meet conditions that are relevant for sliding surfaces. The test rig was used to evaluate low carbon steel textured surfaces produced via maskless electrochemical texturing. The textures were composed of 100 pockets with an average diameter of 200 µm of and variable average depths (1.5, 3.5 and 7 µm). The tests were performed under dry and lubricated conditions for smooth and textured surfaces. The lubricated tests simulated a starved condition by applying a drop of lubricant.

For starved lubrication, surface texturing reduced the sliding energy when compared with smooth surfaces. This was attributed to the pocket’s ability to provide an additional supply of lubricant, as well as a reduced amount of plastic deformation around the pockets during sliding of the indenter. However, under dry sliding conditions, no significant effect of surface texturing was detected.

A new evaluation methodology was proposed, using single-pass pendular sliding of a spherical indenter to measures the energy absorbed during sliding, which was referred to as sliding energy. The measurements are repeatable and can detect sliding energy differences between smooth and textured surfaces.

This study aims to explore the superiority of the compound dimple (e.g. the rectangular-rectangular dimple) and compare its tribological performance for rough parallel surfaces with those of the traditional one-layer dimple (simple dimple).

A mixed-lubrication model for a rough textured surface is established and solved using the finite difference method for film pressure and contact pressure. To accelerate the evaluation of surface deformation, the efficient Continuous convolution fast Fourier transform algorithm is applied. The effects of the compound dimple on the tribological performance for the rough parallel surfaces is numerically investigated. And these effects are compared with those of the simple dimple. Furthermore, a reciprocating friction test is conducted to verify the superiority of the compound dimple.

The compound dimple exhibits better tribological performances in comparison with the traditional simple dimple, that is, a larger load-carrying capacity and a smaller friction coefficient. To achieve the best tribological performances for the rough parallel surfaces, the depth ratio of the lower pore to the total pore of the compound dimple and the dimple interval should be reasonably chosen. For the surface with compound dimples, there exists an optimal surface roughness to simultaneously maximize the load-carrying capacity and minimize the friction coefficient. The smaller friction coefficient of the surface with compound dimples is verified by the reciprocating friction test.

The compound dimple is proposed and the superiority of this novel surface texture is confirmed. This study is expected to provide a new texturing method to improve the tribological performances of the traditional simple dimple.

The purpose of this paper is to investigate the effects of surface texture with roughness orientation considered on tribological properties under a mixed lubrication state numerically and experimentally.

Based on the average Reynolds equation and asperity contact model, the impacts of surface texture parameters and roughness orientation on lubrication properties have been calculated using finite difference method. Tin–bronze samples with various prescribed surface texture geometric parameters and roughness orientation were fabricated by laser surface texturing technique, and the tribology performance of the textured surface was studied experimentally.

The effects of surface geometric parameters and roughness orientation parameters have been discerned. The experimental observations are in good agreement with the numerical prediction, which suggests that the numerical scheme adopted in this work is suitable in capturing the surface texture and roughness effect under mixed lubrication state.

By meticulously controlling the surface roughness and surface texture geometric characteristics based on the laser surface texturing process, samples with prescribed surface texture parameters and roughness orientation consistent with that in theoretical studies were fabricated and the theoretical model and results were verified experimentally.