Rough Step Research Articles

An evaluation of CNN models and data augmentation techniques in hierarchical localization of mobile robots

This work presents an evaluation of CNN models and data augmentation to carry out the hierarchical localization of a mobile robot by using omnidirectional images. In this sense, an ablation study of different state-of-the-art CNN models used as backbone is presented and a variety of data augmentation visual effects are proposed for addressing the visual localization of the robot. The proposed method is based on the adaption and re-training of a CNN with a dual purpose: (1) to perform a rough localization step in which the model is used to predict the room from which an image was captured, and (2) to address the fine localization step, which consists in retrieving the most similar image of the visual map among those contained in the previously predicted room by means of a pairwise comparison between descriptors obtained from an intermediate layer of the CNN. In this sense, we evaluate the impact of different state-of-the-art CNN models such as ConvNeXt for addressing the proposed localization. Finally, a variety of data augmentation visual effects are separately employed for training the model and their impact is assessed. The performance of the resulting CNNs is evaluated under real operation conditions, including changes in the lighting conditions. Our code is publicly available on the project website https://github.com/juanjo-cabrera/IndoorLocalizationSingleCNN.git.

Evaluation and formulation of Spirulina platensis proteins for potential applications in hair care products

Spirulina platensis, a blue-green algae belonging to family Phormidiaceae, is well-known for its unique nutritional profile and high protein content. This study aims to isolate its proteins, formulate them into a cream and evaluate its potential as a hair treatment. S. platensis were collected from Nuweiba, Egypt and its protein was extracted, analyzed and used to prepare a cream. The cream was applied on natural hair strands compared to a control cream containing no protein. Both hair strands groups were observed via Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Atomic Force Microscopy (AFM) and evaluated by Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, as well as, thermal analysis tests including thermogravimetric analysis (TGA) and Differential Scanning Calorimetry (DSC). The SEM images of the test group showed more intact cuticle with the absence of cracks and holes, also, the TEM images showed stronger non-porous cuticle compared to the control group. The AFM images of the test group was smoother with lower roughness average and step height compared to the control group. On the other hand, the raman spectra showed different band intensities for disulfide bonds and C-S bonds as well as increased number of b sheet conformations in the test group which is responsible for the smoothing effect. The thermal analysis tests revealed a clear difference between both groups as an indication of improved keratin structure on the test group. S. platensis proteins could be introduced as a potential candidate for hair care products.

Characteristics of hydraulic jump and energy dissipation in the downstream of stepped spillways with rough steps

Stepped spillways are used to improve the hydraulic performance of a water-flow structure. This research describes the study of two types of physical models to investigate the impacts of rough steps on stepped spillways in terms of energy dissipation and hydraulic jumps. To this end, six stepped spillway models, including a flat spillway with smooth steps and five models with rough steps using gravel with a given grain size (i.e., d50 = 18.9 mm), were made with different step arrangements. The results showed that stepped spillways with rough steps and in the nappe flow regime show a better performance regarding energy dissipation of about 20.97 % compared to smooth steps. On the smooth stepped spillway, the non-dimensional residual head was 4.65, and for similar operating conditions, the average dimensionless residual head on the rough steps was 2.92. Furthermore, when rough steps are placed on all steps, the hydraulic jump characteristics, including sequent depth ratio, jump length, and roller length, decrease by 3.34, 8.61, and 8.14 %, respectively, compared to smooth steps.

Effect of Double Porous Layer on Rough Step Slider Bearing Lubricated with Couple Stress Fluid

Effect of Double Porous Layer on Rough Step Slider Bearing Lubricated with Couple Stress Fluid

Numerical Simulation Results of Hydraulic Jump in Different Conditions of Roughness, Adverse Slope, and Positive Step

Numerical Simulation Results of Hydraulic Jump in Different Conditions of Roughness, Adverse Slope, and Positive Step

Photocatalysis-Assisted Chemical Mechanical Polishing of SiC Wafer using a Novel SiO2@TiO2 Core-Shell Composite Nanoparticles Slurry

Silicon carbide (SiC) is considered as a promising third-generation semiconductor material, but the surface fabrication of SiC wafer is very challenging. Photocatalysis-assisted chemical mechanical polishing of Si-face SiC wafer using a novel SiO2@TiO2 core–shell composite nanoparticles slurry is developed, for attaining high removal efficiency and high surface quality of SiC wafer. The preparation of the SiO2@TiO2 core–shell particles is introduced, and the characteristics of the new composite particle abrasive are studied through scanning electron microscopy, transmission electron microscopy, size distribution, X-ray diffraction and Fourier infrared spectroscopy analysis. Polishing performances of SiC wafer using the slurry with the prepared SiO2@TiO2 composite nanoparticles under UV light are evaluated. The material removal rate (MRR) by the slurry with the SiO2@TiO2 composite nanoparticles presents much higher than that by the slurry without the SiO2@TiO2 particles. Meanwhile, the ultra-smooth surface with the low roughness and atomic step structure could be acquired. The relative removal schematic of the slurry with the photo-active composite nanoparticles abrasive towards the polishing of the SiC surface is proposed.

Optical diffractometry by rough phase steps

Optical diffractometry (OD) using a phase step is an alternative for interferometry, further, has least sensitivity to environmental vibrations. Therefore, OD has found numerous interesting metrological and technological applications. OD utilizes a phase step to detect the influence of objects under measurement by the changes in the Fresnel diffraction pattern. Recently, we showed that such measurements do not require infinitively sharp phase steps, although fabrication of such sharp elements is also impossible. Here, we address the issue of smoothness of the phase step surfaces. So far, in all of the OD applications the surfaces of the incorporated phase steps are considered to be optically smooth and flat. However, practically, some amount of roughness and unflatness is unavoidable even in precise and careful fabrication process. We show that preserving the OD-diffraction-pattern characteristics of a phase step depends on the level of roughness in the surfaces of the phase step. We define number of detectable fringes and autocorrelation functions of the diffraction patterns as the measures for evaluating the similarity of the rough phase step diffractions to the ideal case. We derive the theoretical description and confirm the results with simulations and experiments.

An environment-friendly sustainable machining solution to reduce tool consumption and machining time in face milling using a novel wiper insert

Machining is a high energy-consuming manufacturing process that also generates an enormous amount of waste in the form of chips and worn tools. With governments focusing on reducing the environmental footprint by switching to renewable energy sources and enforcing stricter regulations, industries have started looking for sustainable practices in machining as well. However, a detailed strategy with a multipronged approach in the product design, process, and system is required to transform machining into an eco-friendly process. In addition, developing a sustainable machining solution must be attempted without compromising the other critical performance characteristics of the tools. Hence, the article reports the development (design and manufacturing) of a new wiper milling insert and the investigations carried out to evaluate the sustainability quotient of the tool such as machining power, specific cutting energy, total machining time, and tool consumption. The study confirms that the new wiper milling insert can assist in combining the rough and finish machining steps into one without increasing the power consumption, which reduces the number of tools required for machining. In addition, a 50% reduction in machining time and a significant improvement in surface finish can be obtained simultaneously using the wiper insert.

Ru branched nanostructure on porous carbon nanosheet for superior hydrogen evolution over a wide pH range

Water-electrolytic hydrogen production has attracted extensive attention due to it can fully utilize renewable energy sources and residual secondary energy, while the sluggish kinetics reaction rate seriously hinders the hydrogen evolution reaction (HER) progress. Herein, we reported a novel and simple precipitation-calcination method for the synthesis of Ru branched nanostructures on porous N-doping carbon nanosheets (Ru BCNNs/PNC NNSs) with rough surface and step and edge atoms. The overpotentials for Ru BCNNs/PNC NNSs are 33.4 mV, 81.3 mV, and 99.9 mV in acidic, alkaline, and neutral media, respectively, to achieve the current density of 10 mA cm−2, indicating the superior HER performance over a wide pH range due to the unique rough surfaces and abundant of steps and edges atoms. Meanwhile, the Tafel slopes are 21.7 mV dec−1, 111.1 mV dec−1, and 104.7 mV dec−1 for Ru BCNNs/PNC NNSs in 1.0 M HClO4, 1.0 M KOH, and 1.0 M phosphate buffer solution (PBS, pH=7.0), respectively, much lower than that of commercial Pt/C catalysts with mass loading of 10 % from Macklin Inc. (Pt/C-MK). Furthermore, due to the advantages of the strong interface binding energy between Ru and N-C, the Ru BCNNs/PNC NNSs show outstanding durability for HER not only in mild conditions but also in harsh conditions, outperforming most Ru-based electrocatalysts reported previously.

Определение фактической площади контакта декеля и фактического давления в печатной зоне

The article determines the effect of micro-roughening of the surface roughness of the printing plate on the deformation of the deck. To study the effect of printing plate surface roughness on the actual contact area of the deck in the printing area, the interaction of the roughness microroughness with the surface of the softer material of the deck was considered. A review and discussion of the results of studies on contact problems was made. To determine the actual contact area from the applied load, the distribution of micro protrusions of the surface roughness of the printing plate is taken into account. A method has been developed for calculating the actual deckle contact area and the actual pressure in the printing area, taking into account the surface roughness of the printing plate. According to the proposed method, the values of the actual deckle contact area and the actual pressure in the printing zone were calculated. It has been established that with an increase in the normal load, the actual deckle contact area increases. Also, an increase in the normal load leads to an increase in the actual pressure. The set values of the actual contact area and the actual pressure contribute to the formation of an elastic saturated contact of the deckle, which ensures high quality prints. The results obtained by the developed method are compared with calculations based on known dependencies. In calculations, the number of protrusions crossed by the average level and the number of protrusion tops located above the midline in the area corresponding to the base length are determined, respectively, by the average roughness step and by the average step between adjacent roughness protrusions. The research results make it possible to make the right choice of printing plates and decals when adjusting and operating a printing machine, as well as to set the actual pressure value, which ensures high quality prints.

Interactions Between Breathy and Rough Voice Qualities and Their Contributions to Overall Dysphonia Severity.

Dysphonic voices typically present multiple voice quality dimensions. This study investigated potential interactions between perceived breathiness and roughness and their contributions to overall dysphonia severity. Synthetic stimuli based on four talkers were created to systematically map out potential interactions. For each talker, a stimulus matrix composed of 49 stimuli (seven breathiness steps × seven roughness steps) was created by varying aspiration noise and open quotient to manipulate breathiness and superimposing amplitude modulation of varying depths to simulate roughness. One-dimensional matching (1DMA) and magnitude estimation (1DME) tasks were used to measure perceived breathiness, roughness, their potential interactions, and overall dysphonia severity. Additional 1DME tasks were used to assess a set of natural stimuli that varied along both breathiness and roughness. For the synthetic stimuli, the 1DMA task indicated little interaction between the two voice qualities. For the 1DME task, breathiness magnitude was influenced by roughness step to a greater extent than roughness magnitude was influenced by breathiness step. The additive contributions of breathiness and roughness to overall severity gradually diminished with increasing breathiness and roughness steps, possibly reflecting a ceiling effect in the 1DME task. For the natural stimuli, little consistent interaction was observed between breathiness and roughness. The matching task revealed minimal interaction between perceived breathiness and roughness, whereas the magnitude estimation task revealed some interaction between the two qualities and their cumulative contributions to overall dysphonia severity. Task differences are discussed in terms of differences in response bias and the role of perceptual anchors. https://doi.org/10.23641/asha.21313701.

Static and Dynamic Characteristics of Rough Porous Rayleigh Step Bearing Lubricated with Couple Stress Fluid

In tribology, the Rayleigh step bearing has the maximum load capacity of any feasible bearing geometry. Traditional tribology resources have demonstrated that the Rayleigh step has an ideal geometry which maximizes load capacity. Both in nature and technology, rough and textured surfaces are essential for lubrication. While surface roughness enhances the performance of the bearings as an efficiency measure, it still has a significant impact on the load-carrying capacity of the bearing. In the present study, we investigate the dynamic characteristics of the Rayleigh step bearing with the impact of surface roughness and a porous medium by considering a squeezing action. Couple stress fluid is considered a lubricant with additives in both the film as well as the porous region. Based on Stokes constitutive equations for couple stress fluids, Darcy’s law for porous medium, and stochastic theory for rough surfaces, the averaged Reynolds-type equation is derived. Expressions are obtained for the volume flow rate, steady-state characteristics, and dynamic characteristics. The influence of surface roughness and the porous medium on the Rayleigh step bearing is analyzed. We investigated the static and dynamic characteristics of the Rayleigh step bearing. As a result, the couple stress fluid increases (decreases) the steady load-carrying capacity, dynamic stiffness, and dynamic damping coefficients, and decreases (increases) the volume flow rate negatively (positively) skewed roughness in comparison with that of the Newtonian case. The results are compared with those of the smooth case.

Performance of a hydromagnetic squeeze film on a rough circular step bearing: a comparision of different porous structures

This investigation deals with a comparative analysis of the impact of spongy structure based on the model of Kozeny-Carman and Irmay on a hydromagnetic squeeze film in a rough circular step bearing. Christensen and Tonder’s stochastic averaging process has been utilized to determine the role of an arbitrary transverse surface irregularity. The distribution of the pressure in the bearing is obtained by solving the concerned generalised stochastically averaged equation of Reynolds’ with appropriate boundary conditions. The outcomes show that increasing values of magnetization results in an augmented load. The impact of the surface irregularity (transverse) has been found to be adverse. In addition, the negative effect of the surface irregularity and porosity can be minimised by the positive impact of magnetization, at least in the case of the globular sphere model of Kozeny-Carman. Furthermore, the lower strength of the magnetic field results in an approximately similar performance for both these models. This study offers the possibility that the Kozeny-Carman model could be deployed in comparison with Irmay’s model.

Effect of Bed Roughness and Negative Step on Characteristics of Hydraulic Jump in Rectangular Stilling Basin

Water flow often releases excess energy after passing through gates and spillways. This energy should be reduced to avoid the destruction of downstream structures. The hydraulic jump, a natural phenomenon, inevitably reduces incoming flow energy. The experiments were carried out in this study to investigate the effect of bed roughness and abrupt negative drops on the characteristics of the hydraulic jump. Also, to investigate the effect of geometric and hydraulic parameters on energy dissipation and location of the hydraulic jump, there was a change in the height of the abrupt drop and roughness for different discharges between 30 and 50 L/s and the Froude numbers were ranging from 4.9 to 9.5. The results showed that increasing the bed roughness causes a reduction of the sequent depth ratio and the relative length of the jump by 16.6% and 20.7%, respectively, and increases the relative energy loss and the bed shear force coefficient by 10% and 31%, respectively. In contrast, increasing the step height causes an increase in the sequent depth ratio and the relative length of the jump by 6.5% and 7%, respectively, and increases the relative energy loss and the bed shear force coefficient by 11% and 3.2%, respectively.

Self‐Aeration of Supercritical Water Flow Rushing Down Artificial Vegetated Stepped Chutes

AbstractVegetation flow is commonly studied for subcritical flow motions, while in supercritical flow, the presence of vegetation often leads to enhancement of flow self‐aeration, which would in turn alter the flow hydraulics and water‐plant interaction. This paper presents an experimental investigation of aerated water flow cascading down a vegetated stepped chute. The 21.8° sloping chute is equipped with a 12‐m‐long uniformly stepped invert section. The 0.12‐m‐high steps have grate‐shaped rough top surface covered by 0.07‐m‐high flexible artificial plants, and three vegetation densities are tested in skimming flows, in addition to the reference cases of smooth steps and rough steps covered by bare grate only. The air‐water flow properties and free‐surface fluctuations are systematically measured from the upstream clear‐water region to the downstream fully developed aerated region. The experiment results demonstrate that, compared to a man‐made concrete stepped chute, the presence of vegetation cover on the steps reduces the overall self‐aeration and unsteady fluctuating motions of the flow, while it enhances the dissipation of the flow kinetic energy. The present finding also suggests that the modification to flow aeration and energy loss in the vegetated flow over macrostep‐shaped roughness is dependent on the flow regime which is primarily determined by the flow rate for a given slope and step‐induced macroroughness height.

Sustainable Development Garden Landscape Design Based on Data Mining and Virtual Reality

At present, there is no obvious development of virtual reality technology in the garden industry. In order to improve the application of virtual reality technology in the sustainable development garden landscape design, this paper combines data mining technology and virtual reality technology to construct the sustainable development garden landscape design system. Moreover, this paper proposes a method of using the maximum likelihood method to solve the optimal hyperparameters of the covariance function and analyzes the elite-preserving genetic algorithm and the rough steps of its optimization. In addition, this paper analyzes the digital design process of garden landscape and improves the algorithm. Finally, on the basis of virtual reality technology and data mining technology, an intelligent and sustainable development garden landscape design system is constructed. The experimental analysis results show that the sustainable development garden landscape design system based on data mining and virtual reality proposed in this paper has a good effect.

Training, Optimization and Validation of a CNN for Room Retrieval and Description of Omnidirectional Images

The aim of this paper is to achieve the optimal hyperparameters setup of a convolutional neural network (CNN) to address the localization of a mobile robot. The localization problem is solved with a hierarchical approach by using omnidirectional images as provided by a catadioptric visual sensor, with no panoramic conversion. In this way, we propose adapting and re-training AlexNet with a double purpose. First, to perform the rough localization step by means of a room retrieval task. Second, to carry out the fine localization step within the retrieved room, in which the CNN is used to obtain a holistic descriptor that is compared with the visual model of the retrieved room by means of a nearest neighbour search. To achieve this, a CNN has been adapted and re-trained to address both the room retrieval problem and the obtention of holistic descriptors from raw omnidirectional images. The novelty of this work is the use of a data augmentation technique and Bayesian optimization to address the training process robustly. As shown in the present paper, these tools have proven to be an efficient and robust solution to the localization problem even with substantial changes of the lighting conditions of the target environment.

A Two-Step Surface Reconstruction Method Using Signed Marching Cubes

In this paper, we explore a new approach, a two-step surface reconstruction method to extract the target isosurface from a given implicit function efficiently. Our main contribution is that we improve the surface reconstruction process by accelerating the speed of evaluation using signed marching cubes. The basic strategy is to filter the invalid voxels that do not intersect with the target isosurface in a low-cost manner, and to evaluate the valid voxels that intersect with the target isosurface accurately. The improved signed marching cubes method consists of a rough evaluation step and an exact evaluation step. The coarse evaluation step evaluates the points of all voxels using the fast multipole method with a lower order. After the rough evaluation step, the voxels that intersect with the target isosurface are screened out. Then, the exact evaluation step evaluates the points of filtered voxels using the fast multipole method with a higher order. The experimental results show that, compared with the traditional marching cubes method, the improved reconstruction method reduces the amounts of calculation for invalid voxels that do not intersect with the target isosurfaces, which is useful to improve the efficiency of surface reconstruction.

Modeling the systematic behavior at the micro and nano length scales

The assessment of the systematic behavior based on frequentist statistics was analyzed in the context of micro/nano metrology. The proposed method is in agreement with the well-known GUM recommendations. The investigation assessed three different case studies with definition of model equations and establishment of the traceability. The systematic behavior was modeled in Sq roughness parameters and step height measurements obtained from different types of optical microscopes, and in comparison with a calibrated contact instrument. The sequence of case studies demonstrated the applicability of the method to micrographs when their elements are averaged. Moreover, a number of influence factors, which are typical causes of inaccuracy at the micro and nano length scales, were analyzed in relation to the correction of the systematic behavior, viz. the amount of repeated measurements, the time sequence of the acquired micrographs and the instrument-operator chain. The possibility of applying the method individually to the elements of the micrographs was instead proven not convenient and too onerous for the industry. Eventually, the method was also examined against the framework of the metrological characteristics defined in ISO 25 178-600 with hints on possible future developments.

It’s Okay to Be Wrong: Cross-View Geo-Localization With Step-Adaptive Iterative Refinement

Cross-view image geo-localization is a challenging task of estimating the geospatial location of a street view image by matching it with a database of geotagged aerial/satellite images, and vice versa. Compared to existing CNN-based approaches that attempt to generate discriminative representations in a single step for this task, in this paper, we instead advocate endowing the network with the capability of progressive self-correcting. Towards this target, we propose a novel step-adaptive iterative refinement network (SIRNet), which decomposes the complex learning process into several refinement steps while adapting the refinement steps specifically for each input. Specifically, the SIRNet takes the output of the backbone as a rough network prediction and iteratively refines it via an iterative refinement module (IRM). The IRM cascades several refinement blocks sharing the same structure for progressive self-correcting. For each refinement block, the goal is to improve the output of the previous refinement block under the guidance of height-wise context. In this way, the IRM is capable of improving the rough network prediction step by step, and the refined features are increasingly focused on more discriminative scene regions as they are iteratively refined. In addition, considering different characteristics of input images, we devise an adaptive step estimation (ASE) mechanism, which enables our SIRNet to adapt the number of refinement steps to each input automatically. Concretely, the ASE is performed by comparing features at adjacent refinement steps, estimating whether the next step brings improvements, and finally making a halting decision at each refinement step. With the ASE, our SIRNet becomes a dynamic architecture that considers different characteristics of the inputs when performing the iterative refinement. Extensive experiments demonstrate that our SIRNet performs favorably against state-of-the-art methods on the CVUSA and the CVACT datasets. Furthermore, quantitative and qualitative experimental results demonstrate our approach’s wide applicability, impressive generalization ability, and robustness.