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"Automatic repair"
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Repairbads: An automatic and adaptive method to repair bad channels and segments for OPM-MEG
The optically pumped magnetometer (OPM) based magnetoencephalography (MEG) system offers advantages such as flexible layout and wearability. However, the position instability or jitter of OPM sensors can result in bad channels and segments, which significantly impede subsequent preprocessing and analysis. Most common methods directly reject or interpolate to repair these bad channels and segments. Direct rejection leads to data loss, and when the number of sensors is limited, interpolation using neighboring sensors can cause significant signal distortion and cannot repair bad segments present in all channels. Therefore, most existing methods are unsuitable for OPM-MEG systems with fewer channels. We introduce an automatic bad segments and bad channels repair method for OPM-MEG, called Repairbads. This method aims to repair all bad data and reduce signal distortion, especially capable of automatically repairing bad segments present in all channels simultaneously. Repairbads employs Riemannian Potato combined with joint decorrelation to project out artifact components, achieving automatic bad segment repair. Then, an adaptive algorithm is used to segment the signal into relatively stable noise data chunks, and the source-estimate-utilizing noise-discarding algorithm is applied to each chunk to achieve automatic bad channel repair. We compared the performance of Repairbads with the Autoreject method on both simulated and real auditory evoked data, using five evaluation metrics for quantitative assessment. The results demonstrate that Repairbads consistently outperforms across all five metrics. In both simulated and real OPM-MEG data, Repairbads shows better performance than current state-of-the-art methods, reliably repairing bad data with minimal distortion. The automation of this method significantly reduces the burden of manual inspection, promoting the automated processing and clinical application of OPM-MEG.
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•Repairbads method automatically repairs bad channels and segments in OPM-MEG data.•Repairbads outperforms state-of-the-art methods, minimizing signal distortion.•This approach reduces manual intervention, promoting automated OPM-MEG processing.
Journal Article
Design and Development of Ground Wire Repair Robot for Transmission Line
To ensure the normal and stable operation of the power system, it is important to repair broken and loose strand lines in a timely manner. In view of the limitations and shortcomings in repairing broken and loose strands of overhead transmission lines, this paper designs a set of robots that can automatically repair overhead transmission lines online. First, research and design for the mechanical system of the ground wire repair device for overhead transmission lines, including the walking system and the pre-twisted wire winding system; then develop corresponding software and hardware control system, including the data, image transmitting, receiving modules and the operating system of the device, and develop corresponding control devices, mobile phone software and other wireless terminal control systems. The ground wire repair device of the overhead transmission line developed by the project meets the basic requirements for the repair of broken and loose strands of the line, solved a series of problems caused by current manual repair methods, improves the automation of the operation, and has the significance of promotion and application.
Journal Article
Styler: learning formatting conventions to repair Checkstyle violations
Ensuring the consistent usage of formatting conventions is an important aspect of modern software quality assurance. To do so, the source code of a project should be checked against the formatting conventions (or rules) adopted by its development team, and then the detected violations should be repaired if any. While the former task can be automatically done by format checkers implemented in linters, there is no satisfactory solution for the latter. Manually fixing formatting convention violations is a waste of developer time and code formatters do not take into account the conventions adopted and configured by developers for the used linter. In this paper, we present Styler, a tool dedicated to fixing formatting rule violations raised by format checkers using a machine learning approach. For a given project, Styler first generates training data by injecting violations of the project-specific rules in violation-free source code files. Then, it learns fixes by feeding long short-term memory neural networks with the training data encoded into token sequences. Finally, it predicts fixes for real formatting violations with the trained models. Currently, Styler supports a single checker, Checkstyle, which is a highly configurable and popular format checker for Java. In an empirical evaluation, Styler repaired 41% of 26,791 Checkstyle violations mined from 104 GitHub projects. Moreover, we compared Styler with the IntelliJ plugin CheckStyle-IDEA and the machine-learning-based code formatters Naturalize and CodeBuff. We found out that Styler fixes violations of a diverse set of Checkstyle rules (24/25 rules), generates smaller repairs in comparison to the other systems, and predicts repairs in seconds once trained on a project. Through a manual analysis, we identified cases in which Styler does not succeed to generate correct repairs, which can guide further improvements in Styler. Finally, the results suggest that Styler can be useful to help developers repair Checkstyle formatting violations.
Journal Article
DifFuzzAR: automatic repair of timing side-channel vulnerabilities via refactoring
Vulnerability detection and repair is a demanding and expensive part of the software development process. As such, there has been an effort to develop new and better ways to automatically detect and repair vulnerabilities. DifFuzz is a state-of-the-art tool for automatic detection of timing side-channel vulnerabilities, a type of vulnerability that is particularly difficult to detect and correct. Despite recent progress made with tools such as DifFuzz, work on tools capable of automatically repairing timing side-channel vulnerabilities is scarce. In this paper, we propose DifFuzzAR, a tool for automatic repair of timing side-channel vulnerabilities in Java code. The tool works in conjunction with DifFuzz and it is able to repair 56% of the vulnerabilities identified in DifFuzz’s dataset. The results show that the tool can automatically correct timing side-channel vulnerabilities, being more effective with those that are control-flow based. In addition, the results of a user study show that users generally trust the refactorings produced by DifFuzzAR and that they see value in such a tool, in particular for more critical code.
Journal Article
A DRC Automatic Repair Strategy for Standard Cell Layout Based on Improved Simulated Annealing Algorithm
As the integrated circuit process nodes are continuously reduced, higher complexity and accuracy requirements are imposed on the design rule checking (DRC) of standard cell layouts. Traditional manual repair methods are inefficient and prone to errors. A standard cell layout DRC automatic repair strategy based on an improved simulated annealing algorithm is proposed to address this issue. The proposed method quantifies the degree of graphic conflict by dynamically adjusting the annealing parameters; the high-conflict areas and repair paths are optimized. Meanwhile, the proposed method supports the repair of DRC rules at different process nodes ranging from MOSFET (28 nm) to FinFET (14 nm). Experiments results demonstrate that the proposed method outperforms traditional methods in both repair time and quality. Compared to manual repair, about 70% (MOSFET process) and 80% (FinFET process) of time can be saved by the proposed method, and new violations can be avoided during the repair process. Compared with traditional simulated annealing algorithms, approximately 40% (MOSFET process) and 50% (FinFET process) of the running time can be saved, and 100% elimination rate of DRC violations is achieved. The proposed method provides a fully automated and highly reliable DRC repair solution for integrated circuit layout design.
Journal Article
Automated patch assessment for program repair at scale
In this paper, we do automatic correctness assessment for patches generated by program repair systems. We consider the human-written patch as ground truth oracle and randomly generate tests based on it, a technique proposed by Shamshiri et al., called Random testing with Ground Truth (RGT) in this paper. We build a curated dataset of 638 patches for Defects4J generated by 14 state-of-the-art repair systems, we evaluate automated patch assessment on this dataset. The results of this study are novel and significant: First, we improve the state of the art performance of automatic patch assessment with RGT by 190% by improving the oracle; Second, we show that RGT is reliable enough to help scientists to do overfitting analysis when they evaluate program repair systems; Third, we improve the external validity of the program repair knowledge with the largest study ever.
Journal Article
TRIANGULAR MESH APPROACH FOR AUTOMATIC REPAIR OF MISSING SURFACES OF LOD2 BUILDING MODELS
3D city models are increasingly being used to represent the complexity of today’s urban areas, as they aid in understanding how different aspects of a city can function. For instance, several municipalities and governmental organisations have constructed their 3D city models for various purposes. These 3D models, which are normally complex and contain semantics information, have typically been used for visualisation and visual analysis purposes. However, most of the available 3D models open datasets contain many geometric and topological errors, e.g., missing surfaces (holes), self-intersecting surfaces, duplicate vertices, etc. These errors prevent the datasets from being used for advanced applications such as 3D spatial analysis which requires valid datasets and topology to calculate its volume, detect surface orientation, area calculation, etc. Therefore, certain repairs must be done before taking these models into actual applications, and hole-filling (of missing surfaces) is an important one among them. Several studies on the topic of automatic repair of the 3D model have been conducted by various researchers, with different approaches have been developed. Thus, this paper describes a triangular mesh approach for automatically repair invalid (missing surfaces) 3D building model (LOD2). The developed approach demonstrates an ability to repair missing surfaces (with holes) in a 3D building model by reconstructing geometries of the holes of the affected model. The repaired model is validated and produced a closed-two manifold model.
Journal Article
Artificial Intelligence‐Assisted Repair System for Structural and Electrical Restoration Using 3D Printing
The characteristic of material accumulation makes 3D printing competitive in remanufacturing and repairing. However, conventional repair methods require additional equipment and manual intervention to sequentially finish complicated processes such as global scanning and reverse modeling, which results in efficiency reduction and usage restriction. To address the existing shortcomings, an automatic repair system with artificial intelligence (AI) assistance is developed, which includes a semantic segmentation module, deep reinforcement learning (DRL) module, and composite printing device. The damaged part features are extracted by semantic segmentation from the captured real‐time images to establish DRL maps, where the print motion is simulated and transmitted to the printer. The results indicate that the applied bilateral segmentation network (BiSeNetV2) is 59.03% and 29.90% faster than pyramid scene parsing network (PSPNet) and DeepLabV3+ architecture with satisfying accuracy. The established DRL model based on actual printing achieves the optimization of agent learning speed and print quality. The automatic system improves the repair efficiency by 294% compared to the conventional methods, and enables both structural and electrical repair through high‐temperature polymer–metal printing. This intelligent system enables industrial robots to independently handle unexpected tasks in complex and changeable environments through interdisciplinary knowledge integration of advanced manufacturing and AI. An automatic manufacturing system combining artificial intelligence for multifunctional repair is demonstrated. The parallel execution mode of the automatic system is much more efficient than the conventional methods with work efficiency improved by 294%. The system adapts high‐temperature printing to fabricate polymer–metal composites and enables both structural and electrical restoration.
Journal Article
Automatic Buffer Overflow Warning Validation
Static buffer overflow detection techniques tend to report too many false positives fundamentally due to the lack of software execution information. It is very time consuming to manually inspect all the static warnings. In this paper, we propose BovInspector, a framework for automatically validating static buffer overflow warnings and providing suggestions for automatic repair of true buffer overflow warnings for C programs. Given the program source code and the static buffer overflow warnings, BovInspector first performs warning reachability analysis. Then, BovInspector executes the source code symbolically under the guidance of reachable warnings. Each reachable warning is validated and classified by checking whether all the path conditions and the buffer overflow constraints can be satisfied simultaneously. For each validated true warning, BovInspector provides suggestions to automatically repair it with 11 repair strategies. BovInspector is complementary to prior static buffer overflow discovery schemes. Experimental results on real open source programs show that BovInspector can automatically validate on average 60% of total warnings reported by static tools.
Journal Article