Determination of high-temperature elastoplastic properties of welded joints by indentation test

Abstract

The stress and strain fields formed in the heat-affected zone (HAZ) of welds in pipes in thermal power plants has recently attracted attention as a key plant management issue. The microstructure in the HAZ changes locally as a result of the thermal history imposed by the welding procedure; these changes are manifested in the form of a stress–strain response different from that of the base metal. In the present study, a method based on indentation tests is proposed to estimate the variation of yield stress over the HAZ. The well-known indentation tests to infer the stress–strain curve from indentation load and penetration depth measurements are familiar. However, there are difficulties in applying this approach at high temperatures. An alternative procedure based on impression size is proposed and applied to evaluate the variation of mechanical properties of Grade 122 steel over the HAZ in a welded part at 873?K.     

Content-based image retrieval using color vector angle difference histogram

Abstract

This paper presents an innovative method to represent an image for image retrieval in Content-Based Image Retrieval (CBIR), using a color vector angle differences histogram approach (CVADH), which is different from the existing histograms, such as color histograms, gray level co-occurrences (GLCM), color distribution entropy (CDE), or just counting the frequency number of pixels or neighbors. In contrast to other methods, the CVADH takes account of the color difference and spatial information of two pixels in RGB color space. Furthermore, it directly leverages color clues to extract shape features to show differences given different backgrounds. Experimental results show that the scheme is more efficient than traditional ones that have been developed for CBIR.     

The research of unsupervised change detection method based on the clustering characteristic of 3D histogram

Abstract

One of the main problems related to unsupervised change detection methods based on the ‘difference image’ lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change detection process. To overcome such drawbacks, in this paper, we propose an automatic techniques (based on the clustering characteristic of 3D histogram) for the analysis of the difference image. The 3D histogram is formed by pixel grey levels, contiguous average grey levels and local average grey levels of the difference image. First, the optimal plane threshold and plane direction are searched by using maximal entropy principle based on the clustering characteristic of 3D histogram. Then, by using the optimal plane threshold and plane direction, a plane is established to segment the 3D histogram into changed clustering and unchanged clustering. Finally, the changed pixels in the difference image are discriminated according to the segmentation of 3D histogram. The theoretical analysis and experiment results confirm the effectiveness of the proposed method.     

Contrast enhancement via texture region based histogram equalization

Abstract

This paper presents two novel contrast enhancement approaches using texture regions-based histogram equalization (HE). In HE-based contrast enhancement methods, the enhanced image often contains undesirable artefacts because an excessive number of pixels in the non-textured areas heavily bias the histogram. The novel idea presented in this paper is to suppress the impact of pixels in non-textured areas and to exploit texture features for the computation of histogram in the process of HE. The first algorithm named as Dominant Orientation-based Texture Histogram Equalization (DOTHE), constructs the histogram of the image using only those image patches having dominant orientation. DOTHE categories image patches into smooth, dominant or non-dominant orientation patches by using the image variance and singular value decomposition algorithm and utilizes only dominant orientation patches in the process of HE. The second method termed as Edge-based Texture Histogram Equalization, calculates significant edges in the image and constructs the histogram using the grey levels present in the neighbourhood of edges. The cumulative density function of the histogram formed from texture features is mapped on the entire dynamic range of the input image to produce the contrast-enhanced image. Subjective as well as objective performance assessment of proposed methods is conducted and compared with other existing HE methods. The performance assessment in terms of visual quality, contrast improvement index, entropy and measure of enhancement reveals that the proposed methods outperform the existing HE methods.     

Forty-Sixth Annual Exhibition of the Institute of Physics and the Physical Society

Abstract

The frequency histogram of connected elements (FHCE) is a recently proposed algorithm that has successfully been applied in various medical image segmentation tasks. The FHCE is based on the idea that most pixels belong to the same class as their neighbouring pixels. However, the FHCE performance relies to a great extent on the optimal selection of a threshold parameter. Since evaluating segmentation results is a highly subjective process, a collection of threshold values must typically be examined. No algorithm has been proposed to automate the determination of the threshold parameter value of the FHCE. This study presents a method based on the fuzzy C-means clustering algorithm, designed to automatically generate optimal threshold values for the FHCE. This new approach was applied as a part of a structured sequence of image processing steps in order to facilitate segmentation of microcalcifications in digitized mammograms. A unique threshold value was generated for each mammogram, taking into account the different grey-level patterns based on different compositions of various breast tissues in it. The segmentation algorithm was tested on 100 mammograms (50 collected from the Mammographic Image Analysis Society and 50 normal mammograms onto which a number of simulated microcalcifications were generated). The algorithm was able to detect subtle microcalcifications with sensitivity ranging from 93 to 98%, False alarm ratio from 3 to 5% and false negatives variability from 2 to 3%.     

Modeling and analysis of soft-test/repair for CCD-based digital X-ray systems

Modern X-ray imaging systems evolve toward digitization for reduced cost, faster time-to-diagnosis, and improved diagnostic confidence. For the digital X-ray systems, charge coupled device (CCD) technology is commonly used to detect and digitize optical X-ray image. This paper presents a novel soft-test/repair approach to overcome the defective pixel problem in CCD-based digital X-ray systems through theoretical modeling and analysis of the test/repair process. There are two possible solutions to cope with the defective pixel problem in CCDs: one is the hard-repair approach and another is the proposed soft-test/repair approach. Hard-repair approach employs a high-yield, expensive reparable CCD to minimize the impact of hard defects on the CCD, which occur in the form of noise propagated through A/D converter to the frame memory. Therefore, less work is needed to filter and correct the image at the end-user level while it maybe exceedingly expensive to practice. On the other hand, the proposed soft-test/repair approach is to detect and tolerate defective pixels at the digitized image level; thereby, it is inexpensive to practice and on-line repair can be done for noninterrupted service. It tests the images to detect the detective pixels and filter noise at the frame memory level and caches them in a flash memory in the controller for future repair. The controller cache keeps accumulating all the noise coordinates and preprocesses the incoming image data from the A/D converter by repairing them. The proposed soft-test/repair approach is particularly devised to facilitate hardware level implementation ultimately for real-time telediagnosis. Parametric simulation results demonstrate the speed and virtual yield enhancement by using the proposed approach; thereby highly reliable, yet inexpensive, soft-test/repair of CCD-based digital X-ray systems can be ultimately realized.     

Path entropy method for multiple-source water distribution networks

Over the past decade, one of the key research areas for water distribution networks has been the quantification of network reliability. An interesting approach in this research topic is the use of informational entropy as a surrogate measure for the reliability of water distribution network. Research on water distribution network entropy has progressed to the stage where practical applications are possible, but the actual meaning of the network entropy has never been fully elucidated. Recently, an alternative approach to calculate network entropy was proposed, which was termed the path entropy method (PEM). This alternative method was shown to be useful for the case of single-source water distribution networks. The purpose of this article is to explore the use of the PEM on multiple-source networks. A two-source and two-demand water distribution network is analysed to gain insight into the differences between single-source and multiple-source networks in terms of maximum-entropy flow distribution. This leads to the formal proof of the principle governing maximum-entropy flow ratios in multiple-source water distribution networks.     

Subpixel Speckle Displacement Measurement Using a Digital Processing Technique

Abstract

This paper deals with the optical measurement of deformations and displacements of diffusing rough surfaces using changes in the speckle pattern obtained by laser illumination. A digitized speckle signal is obtained using a linear charge-coupled device photodetector that produces samples of one-dimensional signals of N pixels. A sequential numerical processing procedure is applied to detect the relative displacement of two speckle signals, corresponding to two different states of the surface; the accuracy is better than one micrometre. The principles of the method and the corresponding algorithms are described. Experimental results are given and these include an analysis of the deformation of video structure under directional traction.     

Robust CMAC control schemes for dynamic trajectory following

Abstract

Improved robust CMAC control schemes are proposed for tracing dynamic trajectories in this paper. There are two main structures in the proposed control schemes: one is the robust controller and the other is the improved CMAC network. The robust controller technique can achieve a certain goal without concern for instability of the controlled system in the presence of significant plant uncertainties if the nominal parameter is roughly estimated. Next, in order to reduce the tracing error, a suitable nominal parameter needs to be chosen. Thus, the improved CMAC learning approach under the robust control structure, using the concept of credit assignment, will be employed to determine control variables that can trace other states repeatedly during control processes. Finally, simulation results demonstrate the capability of the proposed control schemes to trace dynamic trajectories.     

Data hiding based on two-stage referencing for two-colour images

Abstract

Data hiding in two-colour images is difficult since 1 pixel requires only 1 bit representation and it is easy to detect for pixel distortion. In this paper, we describe a new data hiding method for two-colour images by two-stage referencing. The cover image is partitioned into n×n non-overlapping sub-blocks, and we calculate difference values by two stages for all pixels to find the suitable replacement pixel. The two-stage referencing is to obtain difference values that are different with the current pixel value for neighbouring pixels with n×n and (n+2)×(n+2). These two difference values are used to embed a secret bit on the sub-block. The experimental results show that the proposed method achieves a good visual quality for the stego-image.     

Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including Phytophthora infestans, Fusarium graminearum, Puccinia graminis, tomato yellow leaf curl. Therefore, this work uses the Support vector machine (SVM) classifier to detect and classify the plant disease using various steps like image acquisition, Pre-processing, Segmentation, feature extraction, and classification. The gray level co-occurrence matrix (GLCM) and the local binary pattern features (LBP) are used to identify the disease-affected portion of the plant leaf. According to experimental data, the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.     

Exploitation of image coherency and topology preservation to accelerate the pixel mapping stage of SOM-based image quantisation

Abstract

SOM-based image quantisation requires a considerable amount of processing time even during the pixel mapping stage. Basically, a full search algorithm is employed to find a codeword, within a codebook, whose distance to the queried pixel is minimum. In this paper, we present a novel approach to accelerate the pixel mapping stage by utilisation of the spatial redundancy of pixels in the image and the inherent topological preservation nature of the resulting codebook. The experimental results confirm that the proposed approach outperforms ordinary solutions and is comparable to state-of-the-art solutions in terms of execution time. In addition, as the proposed approach does not require codebook sorting and a complex data structure with variable sizes, this simplifies its implementation and makes it feasible for hardware realisation.     

Moving/motionless foreground object detection using fast statistical background updating

Abstract

In video surveillance, the detection of foreground objects in an image sequence from a still camera is very important for object tracking, activity recognition and behaviour understanding. The conventional background subtraction cannot respond promptly to dynamic changes in the background, and temporal difference cannot accurately extract the object shapes and detect motionless objects. In this paper, we propose a fast statistical process control scheme for foreground segmentation. The proposed method can promptly calculate the exact grey-level mean and standard deviation of individual pixels in both short- and long-term image sequences by simply deleting the earliest one among the set of images and adding the current image scene in the image sequence. A short-term updating process can be highly responsive to dynamic changes of the environment, and a long-term updating process can well extract the shape of a moving object. The detection results from both the short- and long-term processes are incorporated to detect motionless objects and eliminate non-stationary background objects. Experimental results have shown that the proposed scheme can be well applied to both indoor and outdoor environments. It can effectively extract foreground objects with various moving speeds or without motion at a high process frame rate.     

Capacity investment decision by Monte Carlo approach in a cooperation network

This paper proposes a Monte Carlo approach to make the capacity investment decision. The environment concerns the capacity expansion issue for independent plants operating in a collaborative network. The plants that compose the network can share capacity through a negotiation protocol in order to balance under and over utilisation due to the customer demand. The plants apply the capacity expansion process following a periodic review policy. The generic plant makes the decision using a Monte Carlo approach that takes into account the information of the plant and the information of the collaboration with the plants of the network. The Monte Carlo simulator provides the information to each plant to support the capacity investment decision. The proposed approach is compared with a case characterised by information sharing among the plants proposed in literature and a case without information sharing. A simulation environment based on the JAVA package has been developed in order to test the approach in several market conditions. Several customer demand scenarios have been tested. The simulation results highlight these main findings: the robustness of the proposed approach; the reduction of the capacity investment keeping the same level of total profit performance; and the higher utilisation of the cooperation with the other plants of the network.     

海上远景小目标检测方法的研究

海水本身运动的复杂性以及海水与其它环境因素所产生的干扰物理场,往往使得海上小目标的探测尤为困难.因此,以可见光图像序列中海面小目标检测为研究对象,提出了核函数-马尔科夫随机场(Markov Random Fields,MRF)前景分割模型.该模型利用所提出的邻域相关核函数背景模型对图像序列的求解结果作为MRF的观测值,...     

Maximum entropy methods in coherent radar imaging

There are some compelling reasons for viewing the problem of image reconstruction from noisy or incomplete data as one of statistical estimation, i.e., of choosing, from the infinity of images consistent with the data, that image which, in some statistical sense, is most plausible. Among these reasons are the soundness of the philosophical underpinning of the resulting image reconstruction process, a greater realization of the image resolution which is inherent in the data, and freedom from many of the artifacts encountered in commonly used ad hoc reconstruction schemes. One successful technique employing a principle of statistical inference is the maximum entropy technique, in which the data-consistent image with maximum configurational entropy is chosen. It is a computationally intensive approach involving a conjugate gradient search over a convex function of a vector in a space of dimensionality equal to the number of image pixels. This technique has been employed with success in situations where the data samples are modeled as linearly related to a real non-negative object. We investigate application of maximum entropy image reconstruction to the problem of high-resolution radar diagnostic imaging. The problem differs from others in which maximum entropy has been applied in that the object to be imaged is complex. Although the desired image is of the magnitude of the complex object and is thus real and non-negative, there is no linear relationship beween object magnitude and data. Rather, the data are linearly related to the complex object. Several earlier proposed methods for applying the maximum entropy principle to this problem are identified and analyzed. A method that more closely approximates true Bayesian estimation is proposed.     

Acoustoelastic Effects in Elastic Media with Nonuniform Initial Stress

Abstract

A perturbative formalism for the treatment of the acoustoelastic effect in material plates with a stress profile in the thickness direction is proposed and compared with a numerical method. The latter consists of an application of the local interaction simulation approach (LISA). The reliability of both the analytical treatment and the numerical approach is tested by comparing the respective results for several cases of propagation of Rayleigh waves in specimens with various kinds of stress fields, including welded steel plates. Finally, the linearization obtained from the perturbation treatment is exploited for the solution of the “inverse problem,” i.e., the determination of the stress field from a set of RW times of flight with different wavelengths. A numerical example is provided to illustrate the applicability of the method, starting from a set of synthetic data obtained with LISA.     

Development of hybrid ROCOF and RPV method for anti-islanding protection

ABSTRACT

Distributed generation (DG) must have the capability to quickly and accurately detect islanding operations under various operating conditions. An ideal islanding detection method should be without a non-detection zone (NDZ) and exempt from deteriorating power quality. A novel Anti-islanding protection technology that is a hybrid of rate of change of frequency (ROCOF) and reactive power variation (RPV) is proposed in this paper. The method can differentiate between a DG that falls into the NDZ for ROCOF and one that does not, and the RPV method will be initiated when the DG is within the NDZ. The amplitude of the reactive power variable can be accurately calculated using the proposed method. Thus, the degree of deterioration of power quality and power consumption can be effectively limited. The method has been implemented on a 3 kW DG to prove its feasibility. The experimental results demonstrated that in the proposed method, the DG was able to use the minimal amplitude of RPV to detect islanding in the NDZ. The breaker tripping time under various operating conditions conformed to the criteria specified in IEEE Std. 1547.     

Elasto-plastic finite element analysis with fuzzy parameters

In this paper, an elastoplastic analysis based on fuzzy mathematics and using finite element method will be described. The Drucker–Prager yield criterion and the elasto-plastic matrix are fuzzified for the non-linear analyses which adopt the initial stress method for the solution. A numerical example is given to illustrate the possible variations in the displacements and the extent of plastic zones at the discrete membership functions. A reliability index of a membership grade based on the concept of non-probabilistic entropy is also proposed.     

Hybrid retinal image registration using phase correlation

Abstract

This paper deals with registration of retinal images, which were taken by high-resolution digital colour fundus cameras. The proposed method describes successful application of phase correlation method. It combines several basic steps — global correction of shift, rotation and scaling, detection of landmarks, their correspondences and finally image registration using second-order polynomial model. The method is tested on two sets of images. The first set contains images from the diabetic patients where many retinal pathologies can disturb the registration process. The second set contains images from healthy subjects, which were acquired by different illumination conditions. The method was evaluated using four different criteria - tree objective and one subjective. These criteria are also compared. The achieved registration accuracy of the landmarks position error is 1·13 and 0·93 pixels for respective image sets. Finally, the simple scheme for retinal pathology visualisation of registered fundus pairs is presented.