Watermarking 3D Surface Models into 3D Surface Models Based on ANFIS

A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

Insertion of a text message, audio data or/and an image into another image or 3D model is called as a watermarking process. Watermarking has variety of applications like: Copyright Protection, Owner Identification, Copy Protection and Data Hiding etc., depending upon the type of watermark insertion algorithm. Watermark remains in the content after applying various attacks without any distortions. The blind watermarking method used in the system is based on a wavelet transform, a fuzzy inference system and a multi-resolution representation (MRR) of the 3d model. The watermark scrambled by Arnold Transform is embedded in the wavelet coefficients at third resolution level of the MRR. Fuzzy logic approach used in the method makes it to approximate the best possible gain with an accurate scaling factor so that the watermark remains invisible. The fuzzy input variables are computed for each wavelet coefficient in the 3D model. The output of the fuzzy system is a single value which is a perceptual value for each corresponding wavelet coefficient. Thus, the fuzzy perceptual mask combines all these nonlinear variables to build a simple, easy to use HVS model. Results shows that the system is robust against affine transformations, smoothing, cropping and noise attacks.

Mappings of the earth surface and their representation in 3D (three‐dimensional) models are commonly used in most recent research. Modeling research, which starts with classical surveying methods, acquires new dimensions matching the modern technologies. 3D models of any object or earth surface can be used in much visual and scientific research. A digital model of the landscape is an important part within creation of geo‐information systems used in the public administration and in the commercial sphere. It is an important tool in applications such as geomorphology, hydrology, geology, cartography, ecology, mining etc.Values of volume in terrains that do not have regular geometric structure can be obtained more accurately by using 3D models of surfaces with respect to developing technology. Basic data of 3D models must indicate 3D coordinates of the surveyed object in the reference frame. Distribution and intensity of points are important factors in modeling earth surfaces. A minimum number of points is desired in defining an object in 3D. Interpolation methods employing different mathematical models are used to obtain 3D models of terrain surfaces. In this study, the effect of interpolation methods in defining a terrain surface is investigated. For this purpose, a uniform surface, hill‐shaped artificial object with a known volume is employed. The 3D surface and volume are calculated by using 12 different interpolation methods. Point distribution, point intensity and accuracy of point measurements are not considered. The same data set was used for all the interpolation methods. The interpolation methods are compared and evaluated based on the results. Copyright © 2007 John Wiley & Sons, Ltd.

Ultrasonography has gained increased utility to diagnose pelvic fractures in horses; however, internal pelvic contours can be difficult to appreciate from external palpable landmarks. We developed three-dimensional (3D) simulations of the pelvic ultrasonographic examination to assist with translation of pelvic contours into two-dimensional (2D) images. Contiguous 1mm transverse computed tomography (CT) images were acquired through an equine femur and hemipelvis using a single slice helical scanner. 3D surface models were created using a DICOM reader and imported into a 3D modeling and animation program. The bone models were combined with a purchased 3D horse model and the skin made translucent to visualize pelvic surface contours. 3D models of ultrasound transducers were made from reference photos, and a thin sector shape was created to depict the ultrasound beam. Ultrasonographic examinations were simulated by moving transducers on the skin surface and rectally to produce images of pelvic structures. Camera angles were manipulated to best illustrate the transducer-beam-bone interface. Fractures were created in multiple configurations. Animations were exported as QuickTime movie files for use in presentations coupled with corresponding ultrasound videoclips. 3D models provide a link between ultrasonographic technique and image generation by depicting the interaction of the transducer, ultrasound beam, and structure of interest. The horse model was important to facilitate understanding of the location of pelvic structures relative to the skin surface. While CT acquisition time was brief, manipulation within the 3D software program was time intensive. Results were worthwhile from an instructional standpoint based on user feedback.

The subject of the investigation reported in this paper is the visualization of three-dimensional (3D) surface models in a 3D mapping survey using terrestrial laser scanning (TLS). The Cangkuang Temple nearby Garut City was chosen as the object. The laser technology instruments used were a Topcon GLS-1000 (Geodetic Laser Scanner) and a Topcon IS (Image Station). Twelve points at the same position in each 3D surface model were selected. The coordinate system of the IS was then transformed into the coordinate system of the GLS-1000 using a 3D similarity transformation model. The 3D distances were calculated for each model. Differences in distance were considered as errors in the x, y, and z direction. The standard deviation of the distance differences was ±0.301 m. Some of the distance differences did not fall within the range of tolerances (about 15%). The 3D surface model visualization of the Cangkuang Temple that was created from the GLS-1000 data was more precise than the one created from the IS data. In the future, such 3D surface model visualizations could be used for documentation, preservation and reconstruction of heritage buildings.

3D data model is an indispensable component to any 3D GIS, and forms the basis of 3D spatial analysis and representation. At present, plenty of representative 3D data models are proposed. However, existing models neglect the display result and the consumption of storage space. Based on the analysis of existing 3D GIS data model, a 3D surface model is proposed for fast visualization in this paper, which is composed of node, segment and triangle. The data structure and formal representation of the proposed 3D surface model is developed to organize and store data of 3D model. Finally, an experiment is made to compare this 3D surface model with other 3D data model, and the result demonstrates that the 3D surface model proposed in this paper is superior to the existing data model in terms of data volume, moreover, it can acquire fast visualization speed.

The use of volume data and digital three-dimensional (3D) surface models in biology has increased quickly and steadily. Various methods are available to acquire 3D data, among them episcopic imaging techniques. Based on the episcopic microscopy with on-block staining protocol of Weninger et al. (Anat Embryol 197:341–348, 1998), we describe a simple and versatile setup for episcopic microtomy. It is composed of a consumer DSLR digital camera combined with standard histology equipment. The workflow of block surface staining and imaging, image processing, stack alignment, surface generation (including a custom Amira® macro), and 3D model editing is described in detail. For our sample specimen (Alytes obstetricans; Amphibia: Anura) we obtained images with a pixel size of 5.67 × 5.67 µm2. The generated image stacks allowed distinguishing different tissues and were well-suited for creating a 3D surface model. We analyzed the alignment quality achieved by various selections of specimen and fiducial marker spots. The fiducial spots had a significant positive effect on the alignment quality with the best alignment having a maximum mean alignment error of about 44.7 µm. We further tested the APS-C camera with combinations of macro lens, extension tube or teleconverter. The macro lens and extension tube yielded the smallest pixel size of 2.53 × 2.53 µm2. Considering data quality and resolution, and depending on object sizes and research goals, DSLR captured episcopic microtomy can be an alternative to other techniques, such as traditional histological sectioning or micro-computed tomography.

In this paper, a hybrid meta-heuristic artificial intelligence (AI) method, named “ANFIS–PSOGA,” is developed for modeling the discharge coefficient of circular side orifices for the first time. For this purpose, the adaptive neuro fuzzy inference system (ANFIS) network is optimized by means of two robust optimization algorithms: the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. Furthermore, the Monte Carlo simulations are utilized to enhance the performance of the AI hybrid model. Besides, the k-fold cross-validation method is employed in order to verify the numerical results. Initially, the performance of three fuzzy inference system (FIS) generations of the ANFIS model including grid partitioning, subtractive clustering and fuzzy C-means clustering are evaluated. After that, using the parameters affecting the discharge coefficient, eleven ANFIS models are defined and then the best ANFIS model and the most influencing input parameters are identified through the conduction of a sensitivity analysis. For the superior ANFIS model, mean absolute percentage error, root-mean-square error and the correlation coefficient (R) are obtained 0.021, 0.020 and 0.871, respectively. Also, the Froude number (Fr) is detected as the most effective input parameter. Moreover, the discharge coefficient of the circular side orifices and the flow field within main channels along the side orifices are simulated through the adoption of a computational fluid dynamics (CFD) model. Additionally, the flow field at three different cross sections (beginning, middle and end of orifice) along the side weir is evaluated. For instance, by advancing the flow toward the side orifice, the Froude number is reduced. Besides, at each cross section, the maximum shear stress is estimated to occur in the vicinity of the crest of the circular side orifice. According to the CFD results, the maximum lateral velocity is estimated near the circular side orifice. In addition, the results yielded by the ANFIS, ANFIS–PSO, ANFIS–PSOGA and CFD models are compared and an uncertainty analysis is carried out for the models. Based on the comparison, ANFIS–PSOGA has the best performance in simulating the discharge coefficient. Ultimately, a simple and practical computer code for the best model (ANFIS–PSOGA) is proposed which can be easily used by scholars and engineers in practical usages.

Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal. The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in Mishrif formation in Nasiriya oil field for the selected wells. The mean square error for the results obtained from the ANFIS model was 0.015. The model was trained and simulated using MATLAB and Simulink platform. Laboratory measurements were conducted on core samples selected from two wells. Ultrasonic device was used to measure the transit time of compressional and shear waves and to compare these results with log records. Ten wells in Nasiriya oil field had been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software and based on the las files and log records provided. The average rate of penetration of the studied wells was determined and listed against depth with the average dynamic elastic properties and fed into the fuzzy system. The average values of bulk modulus for the ten wells ranged between (20.57) and (27.57) . For shear modulus, the range was from (8.63) to (12.95) GPa. Also, the Poisson’s ratio values varied from (0.297) to (0.307). For the first group of wells (NS-1, NS-3, NS-4, NS-5, and NS-18), the ROP values were taken from the drilling reports and the lowest ROP was at the bottom of the formation with a value of (3.965) m/hrs while the highest ROP at the top of the formation with a value (4.073) m/hrs. The ROP values predicted by the ANFIS for this group were (3.181) m/hrs and (4.865) m/hrs for the lowest and highest values respectively. For the second group of wells (NS-9, NS-15, NS-16, NS-19, and NS-21), the highest ROP obtained from drilling reports was (4.032) m/hrs while the lowest value was (3.96) m/hrs. For the predicted values by ANFIS model were (2.35) m/hrs and (4.3) m/hrs for the lowest and highest ROP values respectively.

Comparative studies of RSM Box-Behnken and ANN-Anfis fuzzy statistical analysis for seawater biodegradability using TiO2 photocatalyst

This paper presents a new approach to Peak Load Forecasting based on EMD and ANFIS. EMD is used to decompose the load data into several Intrinsic Mode Functions (IMFs); then appropriate ANFIS models are developed for these IMFs. The target model proposed in this paper, EMD–ANFIS, is achieved by combining the predictions from these IMF–ANFIS models together and this is used for forecasting purposes. A real life data obtained from Power Holding Company of Nigeria (PHCN), Bida, was used to evaluate the forecast accuracy of the proposed model. The results revealed that the proposed EMD–ANFIS model yields better results when compared to ANN and EMD–ANN models. The proposed EMD–ANFIS model recorded 2.76% and 50.05% improvements over EMD–ANN and traditional ANN models, respectively as judged by the overall MAPE of the models.

In this paper, we compare and analyze the prediction performance of existing ANFIS (Adaptive Neuro Fuzzy Inference System) models. The ANFIS model is a fusion of neural networks with learning ability, adaptability, and computational ability, and a fuzzy system with human-like thinking ability and reasoning ability. In this paper, we analyze the prediction performance of ANFIS models using different input space partitioning methods. ANFIS 1 uses SC (Subtractive Clustering), a clustering method based on the density of data, to calculate the center of the cluster by calculating potential values inversely proportional to the distances of the different inputs. ANFIS 2 uses Fuzzy C-Means (FCM) clustering, which is a clustering method based on degree of belonging to data, and classifies each data belonging to each cluster according to degree of affiliation. ANFIS 3 is a method of using Context based Fuzzy C-Means (CFCM) clustering, which is a clustering method considering characteristics of input and output space based on FCM clustering. It creates clusters for each context generated according to characteristics of output space, to classify the data. To evaluate these ANFIS models, we use short term electricity price forecasting data and Boston housing data. The prediction performance is based on the Root Mean Squared Error (RMSE) method. Experimental results show that the prediction performance of the ANFIS model is different depending on the characteristics of each data. Furthermore, we confirmed that the prediction performance of ANFIS model using CFCM clustering method is constant.

The structure of a three-dimensional (3D) model will mutate into various shapes when it does deformation. Indeed, various shapes look different from the original 3D model. What is more, the two-dimensional (2D) image of the deformed 3D model is dissimilar to the 2D image of the undeformed model. Therefore, how to find a proper method to analyze the 3D model similarity always be the research hotspot. In these years, with the wide spread of deep learning technology, the research of similarity and retrieval system of 3D models has set off a new technical revolution. However, the data processing method of the 3D model is distinct with the methods of the 2D image, which is more complex. In consequence, the paper presents the similarity analysis method of 3D models based on convolutional neural networks with threshold (t-CNN). The trained and test datasets are multi-view colored 2D images of 3D models which the color is computed by the heat kernel signature (HKS). Meanwhile, in order to improve the accuracy of retrieval, a threshold is added to the CNN before confirming the final category. The experiments show that the colored dataset construction method proposed in the paper makes the data processing easier and improves the classification precision. Similarity analysis of 3D models based on the t-CNN in recall and precision is better than the other methods.

Simple SummaryFollicular lymphoma is an indolent B cell lymphoproliferative disorder of transformed follicular center B cells, which accounts for 20–30 percent of all non-Hodgkin lymphoma (NHL) cases. Although huge efforts have been made in the last 10 years, this pathology is still considered as incurable, leaving open the discovery and testing of new therapeutic targets requiring relevant preclinical models. Here, we report a realistic 3D model of t (14;18)-positive B-NHL cell culture (ultra-low attachment (ULA)-multicellular aggregates of lymphoma cells (MALC)), which monitored by state-of-the-art 2D and 3D imaging, allows more robust drug testing.Follicular lymphoma (FL) is an indolent B cell lymphoproliferative disorder of transformed follicular center B cells, which accounts for 20–30 percent of all non-Hodgkin lymphoma (NHL) cases. Great advances have been made to identify the most relevant targets for precision therapy. However, no relevant models for in vitro studies have been developed or characterized in depth. To this purpose, we generated a 3D cell model from t(14;18)-positive B-NHL cell lines cultured in ultra-low attachment 96-well plates. Morphological features and cell growth behavior were evaluated by classical microscopy (2D imaging) and response to treatment with different drugs was evaluated by a high-content analysis system to determine the robustness of the model. We show that the ultra-low attachment (ULA) method allows the development of regular, spherical and viable ULA-multicellular aggregates of lymphoma cells (MALC). However, discrepancies in the results obtained after 2D imaging analyses on drug-treated ULA-MALC prompted us to develop 3D imaging and specific analyses. We show by using light sheet microscopy and specifically developed 3D imaging algorithms that 3D imaging and dedicated analyses are necessary to characterize morphological properties of 3D models and drug effects. This study proposes a new method, but also imaging tools and informatic solutions, developed for FL necessary for future preclinical studies.

A new surface modeling is proposed to supply interpolation and continuity via expanded Bezier basis and laser scanning. This 3D model is generated via virtual points and Pascal triangle to improve accuracy, sped, and memory size of traditional models.