Detection and correction of design defects in object-oriented designs

This paper presents a survey on various novel defect and noise detection and correction algorithms used in CMOS image sensors. This class of sensors is extremely relevant in number of applications in the areas of gaming, security, medical, automotive, high-end camera market, etc. This survey outlines the algorithms and the hardware implementation of novel defect and noise detection and correction schemes to detect and correct defective pixels, and discusses their performance and advantages in terms of their applications. Experimental results on various images illustrate the capabilities of the studied approaches.

Software engineers often need to identify and correct design defects, i.e., recurring design problems that hinder development and maintenance by making programs harder to comprehend and/or evolve. While detection of design defects is an actively researched area, their correction — mainly a manual and time-consuming activity — is yet to be extensively investigated for automation. In this paper, we propose an automated approach for suggesting defect-correcting refactorings using relational concept analysis (rca). The added value of rca consists in exploiting the links between formal objects which abound in a software re-engineering context. We validated our approach on instances of the Blob design defect taken from four different open-source programs.KeywordsDesign DefectsRefactoringRelational Concept Analysis

Design defects are one of the main reasons for the decline of software design quality. Effective detection of design defects plays an important role in improving software maintainability and scalability. On the basis of defining software design defects, according to C&K design metrics and heuristics, this paper extracts the relevant features of design defects. Based on classical machine learning methods, classifiers are trained for design defect, and candidate designs are classified by classifiers, so as to identify whether there is a design defect in the design. Experiments show that the method has high accuracy and recall rate in identifying design defects.

As the high voltage circuit breaker works, the metal parts are prone to many types of mechanical defects, such as plastic deformation, metal loss, corrosion, and so on. The defects will inevitably decrease the performance, mechanical reliability, lifetime, and other critical parameters of the breaker. Therefore, the detection of mechanical defects is of great help to reduce the mechanical failure rate of circuit breakers. In the present paper, a novel method based on edge detection and deep learning is proposed to detect the mechanical defects of the high voltage circuit breaker. Firstly, the high resolution images of the circuit breaker are photographed, and the components in the images are segmented in the minimum range by the contour detection algorithm. After binarization and morphological processing of the segmented images, the edge is drawn with the improved edge detection algorithm. However, the components of the circuit breaker are complicated, which results that the edge features of the critical component are difficult to extract. Then, the specific contour of components is detected by the depth estimation and segmentation methods. Secondly, a deep learning platform base on Tensorflow is established, which is an optimization method based on Convolutional Neural Network. In order to enhance the feature extraction capacity, a convolution kernel is inputted into a sparse self-coding network to proceeds with the optimal pre-training. Based on the above method, the features of images are automatically learned, and each convolution carries for the equilibrium of image entropy of convolution kernel by introducing similarity constraint rule. Finally, 3 types of mechanical defects, including plastic deformation, metal loss, corrosion, images and non-defect images as input are exploited to validate the CNN with convolution kernel optimized. The experimental results show that the average accuracy of the approach is better than the traditional convolution neural network model and has good feature extraction ability and generalization ability. In conclusion, combined with the improved edge detection and deep learning algorithms, the detection of typical mechanical defects can be well performed. For the collected images of circuit breaker, so as to reduce the impact of noise on defect accuracy of mechanical, the proposed method provides a reference for the non-destructive mechanical defect detection of high voltage circuit breaker.

Software metrics are of great importance in object-oriented design assessment. They quantify various aspects of design entities and play an important role in predicting design quality. Despite the fact that software metrics have become increasingly useful, they raise several issues. Among them, relevant to our research are the issue of setting threshold values and the problem of measurement results interpretation. Fuzzy clustering analysis is used to overcome the limitations of the existing approaches that are using threshold values for metrics and to provide a better interpretation of the obtained measurement results. This paper focuses on metrics-based design flaw detection in object-oriented design. A new metric, Design Flaw Entropy which measures the distribution of design flaws into the analyzed system is introduced. To validate the proposed approach, a case study was also proposed.

A fully automatic redeye detection and correction algorithm is presented to address the redeye artifacts in digital photos. The algorithm contains a redeye detection part and a correction part. The detection part is modeled as a feature based object detection problem. Adaboost is used to simultaneously select features and train the classifier. A new feature set is designed to address the orientation-dependency problem associated with the Haar-like features commonly used for object detection design. For each detected redeye, a correction algorithm is applied to do adaptive desaturation and darkening over the redeye region.

Improving the design quality of large object‐oriented systems during maintenance and evolution is widely regarded as a high‐priority objective. Furthermore, for such systems that are subject to frequent modifications, detection and correction of design defects may easily become a very complex task that is even not tractable for manual handling. Therefore, the use of automatic or semi‐automatic detection and correction techniques and tools can assist reengineering activities. This paper proposes a framework whereby object‐oriented metrics can be used as indicators for automatically detecting situations for particular transformations to be applied in order to improve specific design quality characteristics. The process is based both on modeling the dependencies between design qualities and source code features, and on analyzing the impact that various transformations have on software metrics that quantify the design qualities being improved. Copyright © 2004 John Wiley & Sons, Ltd.

Purpose: Flat-panel radiography detectors employ thin-film transistor (TFT) panels to acquire high-quality x-ray images. Pixel defects occur due to circuit shorts or opens in the TFT panel. The defects may degrade the image quality, as well as lower the production yield, and eventually raise the production cost. Hence, it is important to develop an appropriate defect correction algorithm for acquired images. Traditional correction algorithms are based on a complicated adaptive filtering technique, which exploits neighbor pixels, to faithfully preserve the edge components. Because of the complexity of the traditional sophisticated approaches, optimizing their correction performances is difficult. Approach: We considered various pixel-defect correction algorithms based on different deep learning models, such as the artificial neural network (ANN), convolutional neural network (CNN), concatenate CNN, and generative adversarial networks (GAN). We considered two cases of maximal defect sizes, and , and conducted extensive learning experiments to find the best structures of the learning models using the mean square error (MSE) as the loss function. Results: To conduct experiments, practical chest x-ray images were acquired from a general radiography detector. The MSE values of the correction results from ANN, CNN, concatenate CNN, and GAN were 69.40, 75.13, 68.21, and 73.77, respectively, and were much smaller than that of the conventional template match correction method. Conclusions: A concatenate CNN showed the best defect-correction performance. However, ANN could achieve a similar correction performance with much smaller encoding complexity. Therefore, the single-layer ANN can efficiently conduct defect corrections in terms of both correction and complexity.

This paper reports on an image processing algorithm for simultaneous photometric correction and defect detection in semiconductor manufacturing. We note that this problem has some resemblance to change detection in real time image analysis. In particular, the changes between the two images are analogous to the defects in our machine vision system. We therefore applied several detection methods and examined their applicability to defect detection. We first performed a sub-pixel image registration, using a phase correlation method together with a singular value decomposition factorization of the correlation matrix to compute the necessary alignment. We then tested a few change detection methods, including the shading model, derivative model, statistical change detection, linear dependence change detector and Wronskian change detection model. We subjected this system to our collection of raw data acquired from an industrial system, and we evaluated the different methods with respect to the detection accuracy, robustness, and speed of the system. We have promising results at this stage, especially in detecting the blob and line defects that are most commonly found, and when the lighting variation is within a certain threshold.

В статье рассматривается эффективность проектных решений BIM. В работе представлены результаты исследований в области использования инновационных технологий при проектировании зданий. Рассмотрены современные технологии информационного моделирования, в частности BIM, которые позволяют представить здание не только в трехмерном пространстве, но и позволяют проектировать его в динамической системе «проектирование – строительство – эксплуатация». Опыт использования BIM-технологий основан на выявлении ошибок на начальном этапе проектирования и возможности их устранения на этом же этапе. Анализ механизмов обнаружения проектных ошибок и дефектов лег в основу поставленной в работе задачи. Проанализирована модель об обнаружении дефектов. The paper discusses the effectiveness of BIM design solutions. The paper presents the results of research in the field of using innovative technologies in building design. Modern information modeling technologies, in particular BIM, which allow to represent a building not only in three-dimensional space, but also allow to design it in a dynamic system "design - construction - operation" are considered. The experience of using BIM-technologies is based on the detection of errors at the initial stage of design and the possibility of their elimination at the same stage. The analysis of mechanisms of detection of design errors and defects formed the basis of the task set in the paper. The model about defect detection is analyzed.

Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet

The system catalog of a database with explicit schemas contains among other things information about the structure of the database. Queries based on the system catalog allow us to search occurrences of database design antipatterns (database design flaws). In this paper, we present the results of an evaluation of a set of SQL databases. We used the queries that were presented in the previous paper on this topic. A goal of the research is to further experimentally evaluate the queries. We present findings about the queries as well as evaluated databases. In addition, we propose more questions about the design of conceptual schemas of SQL databases that can be answered by querying their system catalogs. The use of the queries would allow us to partially automate the process of evaluating structure and constraints of existing databases and detecting design flaws.

Skew Correction of Textural Documents

A fully automatic redeye detection and correction algorithm was developed at Eastman Kodak Company Research Laboratories. The algorithm is highly sophisticated so that it is able to distinguish most redeye pairs from scene content. It is also highly optimized for execution speed and memory usage enabling it to be included in a variety of products. Detected redeyes are corrected so that the red color is removed, but the eye maintains a natural look.

<div class="htmlview paragraph">Thermal testing of spacecraft electronic units prior to flight provides effective detection of design, process and workmanship defects. Thermal testing subjects units to cold and hot thermal environments beyond those expected in flight. The strength of screening effectiveness depends upon the number of cycles, the temperature range, and the temperature transition rate. MIL-HDBK-344 provides insight into the incurred stresses and quantitative value (precipitation efficiency) of the screening environment using these three test parameters.</div> <div class="htmlview paragraph">In this paper, MIL-HDBK-344 topics applicable to thermal testing of space hardware are summarized and comparisons are made between test environment strengths computed from MIL-HDBK-344 and MIL-STD-1540E. The weighting of these aforementioned test parameters in the precipitation efficiency equation are discussed and assessed. It was found that the fatigue equivalency equations in MIL-STD-1540E specify a significantly larger number of required cycles for various test temperature ranges as compared to cycles computed from MIL-HDBK-344 equations.</div>