Geometric Approaches to Statistical Defect Prediction and Learning

Software quality is directly correlated with the number of defects in software systems. As the complexity of software increases, manual inspection of software becomes prohibitively expensive. Thus, defect prediction is of paramount importance to project managers in allocating the limited resources effectively as well as providing many advantages such as the accurate estimation of project costs and schedules. This thesis addresses the issues of defect prediction and learning in the geometric framework using statistical quality control and genetic algorithms. A software defect prediction model using the geometric concept of operating characteristic curves is proposed. The main idea behind this predictor is to use geometric insight in helping construct an efficient prediction method to reliably predict the cumulative number of defects during the software development process. The performance of the proposed approach is validated on real data from actual software projects, and the experimental results demonstrate a much improved performance of the proposed statistical method in predicting defects. In the same vein, two defect learning predictors based on evolutionary algorithms are also proposed. These predictors use genetic programming as feature constructor method. The first predictor constructs new features based primarily on the geometrical characteristics of the original data. Then, an independent classifier is applied and the performance of feature selection method is measured. The second predictor uses a built-in classifier which automatically gets tuned for the constructed features. Experimental results on a NASA static metric dataset demonstrate the feasibility of the proposed genetic programming based approaches

Optimized Surface Code Communication in Superconducting Quantum Computers

Quantum computing (QC) is at the cusp of a revolution. Machines with 100 quantum bits (qubits) are anticipated to be operational by 2020 [googlemachine,gambetta2015building], and several-hundred-qubit machines are around the corner. Machines of this scale have the capacity to demonstrate quantum supremacy, the tipping point where QC is faster than the fastest classical alternative for a particular problem. Because error correction techniques will be central to QC and will be the most expensive component of quantum computation, choosing the lowest-overhead error correction scheme is critical to overall QC success. This paper evaluates two established quantum error correction codes---planar and double-defect surface codes---using a set of compilation, scheduling and network simulation tools. In considering scalable methods for optimizing both codes, we do so in the context of a full microarchitectural and compiler analysis. Contrary to previous predictions, we find that the simpler planar codes are sometimes more favorable for implementation on superconducting quantum computers, especially under conditions of high communication congestion.Comment: 14 pages, 9 figures, The 50th Annual IEEE/ACM International Symposium on Microarchitectur

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines

The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples

Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry. View Full-TextPeer ReviewedPostprint (published version

Learning models of plant behavior for anomaly detection and condition monitoring

Providing engineers and asset managers with a too] which can diagnose faults within transformers can greatly assist decision making on such issues as maintenance, performance and safety. However, the onus has always been on personnel to accurately decide how serious a problem is and how urgently maintenance is required. In dealing with the large volumes of data involved, it is possible that faults may not be noticed until serious damage has occurred. This paper proposes the integration of a newly developed anomaly detection technique with an existing diagnosis system. By learning a Hidden Markov Model of healthy transformer behavior, unexpected operation, such as when a fault develops, can be flagged for attention. Faults can then be diagnosed using the existing system and maintenance scheduled as required, all at a much earlier stage than would previously have been possible

Influence of ion implantation on the magnetic and transport properties of manganite films

We have used oxygen ions irradiation to generate controlled structural disorder in thin manganite films. Conductive atomic force microscopy CAFM), transport and magnetic measurements were performed to analyze the influence of the implantation process in the physical properties of the films. CAFM images show regions with different conductivity values, probably due to the random distribution of point defect or inhomogeneous changes of the local Mn3+/4+ ratio to reduce lattice strains of the irradiated areas. The transport and magnetic properties of these systems are interpreted in this context. Metal-insulator transition can be described in the frame of a percolative model. Disorder increases the distance between conducting regions, lowering the observed TMI. Point defect disorder increases localization of the carriers due to increased disorder and locally enhanced strain field. Remarkably, even with the inhomogeneous nature of the samples, no sign of low field magnetoresistance was found. Point defect disorder decreases the system magnetization but doesn t seem to change the magnetic transition temperature. As a consequence, an important decoupling between the magnetic and the metal-insulator transition is found for ion irradiated films as opposed to the classical double exchange model scenario.Comment: 27 pages, 11 Figure