Coding Method Research Articles

Preparation and performance of pH-temperature responsive low-damage gel temporary plugging agent

Temporary plugging to fracturing has become the main production enhancement and transformation method for low-permeability, unconventional oil and gas reservoirs. In this paper, copolymers (PANA) were firstly prepared by free-radical aqueous solution polymerization with monomers acrylamide, N-vinylpyrrolidone, and 2-acrylamido-2-methylpropanesulfonic acid, and then gel temporary plugging agents (PJPs) were prepared using polyethyleneimine as cross-linker. The successful preparation of the target products was also demonstrated by using infrared and nuclear magnetic characterization. The results of rheological performance tests on PJPs show that they have good temperature and shear resistance. Validation experiments on the pH and temperature response performance of PJPs based on the visual code method and viscoelastic strength showed that PJPs could not be gelled at low temperature (<30 °C) and low pH (<7), thus proving that PJPs is controlled by both temperature and pH. Then the gelling mechanism of PJPs was investigated by characterizing the state of PJPs before and after gelling by SEM. The experiments to evaluate the temporary plugging performance of PJPs show that when the pH is 10 and the temperature is 120 ℃, the temporary plugging agent can completely seal the fracture with a repellent pressure of 2 MPa, and the final flow rate tends to 0 mL/min. The maximum breakthrough pressure reaches 12.6 MPa with a fixed repellent flow rate of 5 mL/min, which has good plugging pressure-bearing performance. Finally, the damage performance evaluation experiment of PJPs concluded that the permeability damage of fractured cores was below 13 %, thus indicating that PJPs have good protection performance for the reservoir.

A Quantitative Evaluation of Thin Slice Sampling for Parent–Infant Interactions

Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative approach, aiming to alleviate the coding burden. However, little is understood about whether different behaviours coded over thin slices are comparable to those same behaviours over entire interactions. To provide quantitative evidence for the value of thin slice sampling for a variety of behaviours. We used data from three populations of parent-infant interactions: mother-infant dyads from the Grown in Wales (GiW) cohort (n = 31), mother-infant dyads from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 14), and father-infant dyads from the ALSPAC cohort (n = 11). Mean infant ages were 13.8, 6.8, and 7.1 months, respectively. Interactions were coded using a comprehensive coding scheme comprised of 11–14 behavioural groups, with each group comprised of 3–13 mutually exclusive behaviours. We calculated frequencies of verbal and non-verbal behaviours, transition matrices (probability of transitioning between behaviours, e.g., from looking at the infant to looking at a distraction) and stationary distributions (long-term proportion of time spent within behavioural states) for 15 thin slices of full, 5-min interactions. Measures drawn from the full sessions were compared to those from 1-, 2-, 3- and 4-min slices. We identified many instances where thin slice sampling (i.e., < 5 min) was an appropriate coding method, although we observed significant variation across different behaviours. We thereby used this information to provide detailed guidance to researchers regarding how long to code for each behaviour depending on their objectives.

Multi-Objective Evolutionary Algorithm With Machine Learning and Local Search for an Energy-Efficient Disassembly Line Balancing Problem in Remanufacturing

Abstract Product disassembly is a vital element of recycling and remanufacturing processes. The disassembly line balancing problem (DLBP), i.e., how to assign a set of tasks to a disassembly workstation, is crucial for a product disassembly process. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this study proposes an optimization model for a multi-objective disassembly line balancing problem that aims to minimize the idle rate, smoothness, cost, and energy consumption during the disassembly operation. Due to the complex nature of the optimization problem, a discrete whale optimization algorithm is proposed in this study, which is developed as an extension of the whale optimization algorithm. To enable the algorithm to solve discrete optimization problems, we propose coding and decoding methods that combine the features of DLBP. First of all, the initial disassembly solution is obtained by using K-means clustering to speed up the exchange of individual information. After that, new methods for updating disassembly sequences are developed, in which a local search strategy is introduced to increase the accuracy of the algorithm. Finally, the algorithm is used to solve the disassembly problem of a worm reducer and the first 12 feasible task allocation options in the Pareto frontier are shown. A comparison with typically existing algorithms confirms the high performance of the proposed whale optimization algorithm, which has a good balance of solution quality and efficiency.

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding.

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

Advanced audio coding steganography algorithm with distortion minimization model based on audio beat

Currently, most advanced audio coding (AAC) steganography methods are content-non-adaptive without considering the characteristics of audio, and there are several limitations in imperceptibility and steganalysis. In this paper, we use an audio feature beat as the anchor point to identify the cover elements, group the quantized modified discrete cosine transform (QMDCT) coefficients in the small value area, and finally use the syndrome-trellis codes (STCs) framework for content-adaptive embedding to obtain the minimum distortion. In the STCs framework, we comprehensively consider auditory and data distortions. Experimental results demonstrate that the proposed steganography algorithm has a 10% improvement over the compared algorithms in terms of imperceptibility and steganalysis, and it can accurately extract secret information in face of frame loss and misalignment.

A Texture-Hidden Anti-Counterfeiting QR Code and Authentication Method.

This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.

Convergence characteristics and acceleration of the transient fixed source equation solved by Monte Carlo method

The safety analysis of nuclear systems such as nuclear reactors requires transient calculation. The Monte Carlo (MC) method has grown rapidly in recent years because of its high-fidelity modelling and simulation capability. The predictor-corrector quasi-static (PCQS) MC method has been investigated for kinetic calculation. However, the approach to shorten the computational time required to solve the transient fixed source equation (TFSE) is still under development. The convergence characteristic of the neutron source iteration algorithm of the PCQS MC method is analyzed in this study with a simplified model. It is found that the convergence rate of the iteration algorithm is governed by the effective spectral radius (ESR). The lower the ESR is, the faster the convergence is. In order to reduce the ESR, the asymptotic superhistory method (ASM) is developed for the PCQS MC method in the RMC code. The performance of ASM is evaluated by the C5G7-TD benchmark. Results show that the reduction in the number of inactive cycles is more than 85%, and over 15% of computational time including active cycles is saved. It is demonstrated how ASM speeds up the iterations using the Wasserstein distance measure.

Combination RC4 Algorithm and Base64 Encryption on The Least Significant Bit Method

Steganography is an art form of hiding data or information on a medium. Steganography was created as a way to secure data by hiding it in other media so that it is "invisible". In steganography, secret data is hidden in a carrier such as sound, image or video. Securing messages using steganography is still vulnerable to third parties because the message inserted is still the original message, so that when it is successfully deleted from the message carrier it can be immediately identified. Given these problems, the steganography method needs to be combined with other methods to strengthen the level of data security, in this case a combination of steganography and cryptography will be carried out so that the embedded message will be different from the original message. Even if the data is deleted from the user, the message cannot be known immediately. In this trial, the RC4 cryptographic method which has a symmetric key and also Base64 will be implemented in the PHP and Android programming languages using the Least Significant Bit (LSB) steganography method which will create encrypted secret messages embedded in JPG format, JPEG image media formats on each the last bit of a pixel so that the eye does not see the difference between the inserted and non-inserted images with the original image variable/result of 131.91KB after the Encryption process the amount of data is 31.92KB with very small differences so that data security is maintained without being visible to the naked eye

Thermal-hydraulic 0D/3D coupling in OpenFOAM: Validation and application in nuclear installations

The nuclear safety assessment involving large transient simulations is forcing the community to develop methods for coupling thermal-hydraulics and neutronic codes and three-dimensional (3D) Computational Fluid Dynamics (CFD) codes. In this paper a set of dynamic boundary conditions are implemented in OpenFOAMⓇ in order to apply zero-dimensional (0D) approaches coupling with 3D thermal-hydraulic simulation in a single framework. This boundary conditions are applied to model pipelines, tanks, pumps, and heat exchangers. On a first stage, four tests are perform in order to assess the implementations. The results are compared with experimental data, full 3D CFD, and system code simulations, finding a general good agreement. The semi-implicit implementation nature of these boundary conditions has shown robustness and accuracy for large time steps. Finally, an application case, consisting of a simplified open pool with a cooling external circuit is solved to remark the capability of the tool to simulate thermal hydraulic systems commonly found in nuclear installations.

An improved framework for detecting discrete epidemiologically meaningful partitions in hierarchically clustered genetic data.

Hierarchical clustering of microbial genotypes has the limitation that hierarchical clusters are nested, where smaller groups of related isolates exist within larger groups that get progressively larger as relationships become increasingly distant. In an epidemiologic context, investigators must dissect hierarchical trees into discrete groupings that are epidemiologically meaningful. We recently described a statistical framework (Method A) for dissecting hierarchical trees that attempts to minimize investigator bias. Here, we apply a modified version of that framework (Method B) to a hierarchical tree constructed from 2111 genotypes of the foodborne parasite Cyclospora, including 639 genotypes linked to epidemiologically defined outbreaks. To evaluate Method B's performance, we examined the concordance between these epidemiologically defined groupings and the genetic partitions identified. We also used the same epidemiologic clusters to evaluate the performance of Method A, plus two tree-dissection methods (cutreeHybrid and cutreeDynamic) available within the Dynamic Tree Cut R package, in addition to the TreeCluster method and PARNAS. Compared to the other methods, Method B, TreeCluster, and PARNAS were the most accurate (99.4%) in identifying genetic groups that reflected the epidemiologic groupings, noting that TreeCluster and PARNAS performed identically on our dataset. CutreeHybrid identified groups reflecting patterns in the wider Cyclospora population structure but lacked finer, strain-level discrimination (Simpson's D: cutreeHybrid=0.785). CutreeDynamic displayed good strain discrimination (Simpson's D = 0.933), though lacked sensitivity (77%). At two different threshold/radius settings TreeCluster/PARNAS displayed similar utility to Method B. However, Method B computes a tree-dissection threshold automatically, and the threshold/radius settings used when executing TreeCluster/PARNAS here were computed using Method B. Using a TreeCluster threshold of 0.045 as recommended in the TreeCluster documentation, epidemiologic utility dropped markedly below that of Method B. Relevant code and data are publicly available. Source code (Method B) and instructions for its use are available here: https://github.com/Joel-Barratt/Hierarchical-tree-dissection-framework.

A Least Significant Bit Steganographic Method Using Hough Transform Technique

Steganography is a data-hiding scientific branch that aims to hide secret data in an image, video, or audio. Image steganography methods try to embed a large amount of data into images with high imperceptibility. However, increasing the number of embedded data in the image decreases its quality. Therefore, in this paper, a new method based on Least Significant Bit (LSB) using Hough Transform is proposed to improve the stego image quality with increasing the amount of embedded data. The LSB is the common embedding steganography method due to its simplicity of implementation and low complexity. The proposed method inverts the LSBs of image pixels to enhance the quality of stego image. First, improved edge detection filter is used to detect edges areas. Then, we invert LSBs for the pixel in edge area pixels. Finally, the LSBs smooth area pixels of the cover image are inverted. The performance of the presented method is evaluated for the stego image quality and the amount of embedded data. The results show that the new method has better performance in comparison with the current methods in terms of Peak Signal-to-Noise Ratio (PSNR) and capacity.

Robust Coverless Image Steganography Based on Neglected Coverless Image Dataset Construction

Most of the existing image selection-based coverless image steganography methods mainly focus on improving the capacity and robustness under the assumption that the corresponding dataset is available. But they ignore how to successfully construct the coverless image dataset, which is the foundation of such methods and has a critical impact on the capacity. In this paper, a coverless image steganography is proposed that considers how to efficiently construct the coverless image dataset. In the proposed method, the CNN-based deep hash is extracted from the image and a specific mapping rule is designed to map the high-dimensional deep hash to the low-dimensional secret message. In addition, an unsupervised clustering algorithm is adopted to construct the coverless image dataset, which makes the construction of the coverless image dataset efficient and improves the robustness of the proposed steganography method. To our best knowledge, this is the first attempt to improve the construction efficiency of the coverless image dataset in the field of coverless image steganography. Experimental results show that the construction of a large coverless image dataset is feasible and reliable, and the proposed method has better robustness and higher dataset utilization rate compared with the state-of-the-art methods.

Topic Controlled Steganography via Graph-to-Text Generation

Generation-based linguistic steganography is a popular research area of information hiding. The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to. However, in the course of our experiment, we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text, which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases, and that the topic of generated texts is uncontrollable, so there is still room for improvement in concealment. In this paper, we propose a topic-controlled steganography method which is guided by graph-to-text generation. The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs, and the topic of the generated texts is controllable. We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation. Different from traditional linguistic steganography methods, we encode the secret information during graph path coding rather than using conditional probability. We test our method in different aspects and compare it with other text generative steganographic methods. The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

Assessment of Lateral Load Capacity of Single Pile at Bettiah Site: A Parametric Study

Pile foundations are widely used and accepted approach to transfer superstructure load to a deeper and stronger stratum. This work has been focused on the assessment of the effect of different parameters associated with piles such as pile diameter, pile length, grade of concrete and pile head condition (i.e., free or fixed) on the lateral load capacity. To evaluate the lateral load capacity considering the aforementioned parameters ‘IS Code’ and ‘Matlock and Reese’ methodologies have been utilized. The soil exploration data has been collected from ten different boreholes near to the railway bridge at Bettiah site, Bihar. The results of lateral pile load at different boreholes reflects that the lateral load capacity of pile significantly increased with the increase of pile diameters and grades of concrete. The lateral load capacity of pile was increased approximately by 13%, from both IS Code and Matlock and Reese methods, when the grade of concrete was increased from M25 to M40. It was also found that the condition of pile head also plays a major role in lateral load capacity. The lateral load capacity of pile obtained from IS code method under fixed head condition was found to be higher as compared to free head condition. It has also been observed that the lower values of ultimate lateral load capacity for fixed head and lower values for free head condition was obtained by IS Code as compared to Matlock and Reese method.

Эффективный выбор основания кода при первичном кодировании

Modern advances in microelectronics and the widespread use of computer technology for the purpose of software implementation of various telecommunication and information devices are a reliable justification for the representation of messages from various sources in digital form, that is, in the form of appropriate code combinations, which are essentially numbers in a certain number system. Considering the effective primary coding of messages from the source, they strive to achieve, first of all, the coding rate as close as possible to the entropy of the source, which was proved in the well-known theorems of Claude Shannon, which substantiated this position regarding the minimum value of the average length of the code combination. Since different messages from the source appear, as a rule, with different probabilities, it is possible to achieve the minimum coding rate using non-uniform coding methods, for example, according to the algorithm proposed by David Huffman. In an effort to achieve the minimum encoding rate, one should also take into account the complexity of the implementation of the primary encoding device, take into account the number of elements that will be required for its practical implementation, and so on. Based on this, the effectiveness of one or another method of primary coding is determined not by one, but by a number of factors that often contradict each other. One of these factors is the choice of the code base, which makes it possible to optimize the costs of the practical implementation of the primary coding method, which was the subject of consideration in this paper

Deep learning-based video coding optimisation of H.265

Today's multi-media applications need high video quality with low bitrates. However, it is restricted in its capacity to provide higher quality than earlier coding methods. Deep learning (DL) approaches for video coding have shown compression capacities equal to or better than traditional methods, including high-efficiency video coding (HEVC) methods. The trade-off between compression efficiency and encoding/decoding complexity, optimisation for perceptual nature of semantic dependability, specialisation, and universality, the federalised layout of various deep toolkits, etc. remains unclear. HEVC encoding is more efficient than previous standards. Improved efficiency is driven by intra image prediction, which incorporates more prior directions (35 modes) than previous standards. Its high efficiency comes from balancing encoder complexity and dependability. This article presents DL, which uses a convolutional neural network to predict the best model with the least rate-distortion (RD) and further promotes study into deep learning video coding (DLVC).

APPLICATION OF CUTTER–JORDAN–BOSSEN METHOD FOR DATA HIDING IN THE IMAGE SPATIAL AREA

The article deals with the main methods of digital steganography and presents a classification scheme. Special attention is paid to Cutter–Jordan–Bossen method for hiding data in the spatial area of the image. Aim. The study of digital shorthand methods, as well as the assessment of their applicability for hiding information in images. The main task is to analyze the Cutter–Jordan–Bossen method for hiding data in the spatial area of the image and evaluate its effectiveness under various conditions. Materials and methods. In this work, various methods of digital shorthand were used, including the Cutter–Jordan–Bossen method. Images of various types and quality, as well as various embedding parameters were used for testing. Results. As a result of the study, it was revealed that the Cutter–Jordan–Bossen method is effective for hiding information in the spatial area of the image. The dependence of the data extraction quality on the embedding parameters was tested, which showed that the optimal parameters depend on the type of image and its quality. The resistance of the information hidden by this method to distortion during compression was also tested. The test results showed that JPEG compression, even at low and high energy values, leads to the destruction of information hidden in the container. It was found that the best results are achieved when using the Cutter–Jordan–Bossen method with optimal embedding parameters, which allows you to save hidden information when compressing an image. Conclusion. In conclusion, we can say that the study of digital shorthand methods and their application to conceal information in images is an urgent and important topic. The Cutter–Jordan–Bossen method has shown good results in hiding information in the spatial area of the image, but for each specific case it is necessary to choose the optimal embedding parameters. It was found that JPEG compression can significantly affect the quality of information extraction, so it is necessary to take this factor into account when choosing a method for hiding data in an image. In general, the study of digital shorthand techniques and their application to conceal information in images can be useful for various fields, such as the protection of confidential information and digital watermark.

PixelSteganalysis: Pixel-Wise Hidden Information Removal With Low Visual Degradation

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10–20 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in both the decoded rate and the DT.

Double-Layered Dual-Syndrome Trellis Codes Utilizing Channel Knowledge for Robust Steganography

Robust steganography aims to hide message in cover data with high security and guarantee the success of its message extraction although it is disturbed in transmission channel. In this paper we propose a framework of coding scheme extended from Dual-Syndrome Trellis Codes (Dual-STCs) for robust adaptive steganography. We use the conditional probability distribution of correct stego bits conditioned on disturbed stego data as channel knowledge, and formulate error-correcting as maximizing this probability. By extending Dual-STCs to double-layered embedding, we design an iteratively decoding scheme for error-correcting two layer stego bits from their joint conditional probabilities, and strictly prove its convergence. Besides, we design a method to estimate these probability distributions from stego data pairs uploaded/downloaded from the lossy transmission channel. The channel knowledge can also be used by steganographer, and we propose a universal method to revise steganographic distortion values for higher robustness under the guidance of the channel knowledge. Compared with existing coding methods for robust steganography, our method can make use of channel knowledge to improve error correcting ability and meanwhile maintain high security, which is demonstrated by experimental results.

Improved Video Steganography with Dual Cover Medium, DNA and燙omplex燜rames

The most valuable resource on the planet is no longer oil, but data. The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value. In order to transmit sensitive information securely, researchers are combining robust cryptography and steganographic approaches. The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid (DNA) for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility. In the previous approach, DNA was used only for frame selection. If this DNA is compromised, then our frames with the hidden and unencrypted data will be exposed. Moreover the frame selected in this way were random frames, and no consideration was made to the contents of frames. Hiding data in this way introduces visible artifacts in video. In the proposed approach rather than using DNA for frame selection we have created a fake DNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames. Using chaotic maps and linear congruential generators, a unique pixel set is selected each time only from the identified complex frames, and encrypted data is embedded in these random locations. Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance. Further, the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio (PSNR) and reduced mean squared error (MSE) values that indicate improved results. The proposed methodology has been implemented in Matlab.