A novel approach for detection and classification of re-entrant crack using modified CNNetwork

Purpose In the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Design/methodology/approach In the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method. Findings In the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results. Originality/value The originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.

Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.

Formal Analysis of Database Trigger Systems Using Event-B

Most modern relational database systems use triggers to implement automatic tasks in response to specific events happening inside or outside a system. A database trigger is a human readable block code without any formal semantics. Frequently, people can check if a trigger is designed correctly after it is executed or with human inspection. In this article, the authors introduce a new method to model and verify database trigger systems using Event-B formal method at early design phase. First, the authors make use of the similar mechanism between triggers and Event-B events to propose a set of rules translating a database trigger system into Event-B constructs. Then, the authors show how to verify data constraint preservation properties and detect infinite loops of trigger execution with RODIN/Event-B. The authors also illustrate the proposed method on a case study. Finally, a tool named Trigger2B which partly supports the automatic modeling process is presented.

Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities.

Detection of Carrageenan in Cheese Using Lectin Histochemistry

Carrageenan is a substance widely used as an additive in the food industry. Among other things, it is often added to processed cheese, where it has a positive effect on texture. Processing of such cheese involves grinding, melting and emulsifying the cheese. There is currently no official method by which carrageenan can be detected in foodstuffs, but there are several studies describing its negative health impact on consumers. Lectin histochemistry is a method that is used mainly in medical fields, but it has great potential to be used in food analysis as well. It has been demonstrated that lectin histochemistry can be used to detect carrageenan in processed cheese by Human Inspection and Computer-Assisted Analysis (CIE L*a*b*). The limit of detection (LoD) was established at 100 mg kg−1 for Human Inspection and 43.64 for CIE L*a*b*. The CIE L*a*b* results indicate that Computer-Assisted Analysis may be an appropriate alternative to Human Inspection. The most suitable parameter for Computer-Assisted Analysis was the b* parameter in the CIE L*a*b* color space.

Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM

A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.

Alvis: a tool for contig and read ALignment VISualisation and chimera detection

Background The analysis of long reads or the assessment of assembly or target capture data often necessitates running alignments against reference genomes or gene sets. The aligner outputs are often parsed automatically by scripts, but many kinds of analysis can benefit from the understanding that can follow human inspection of individual alignments. Additionally, diagrams are a useful means of communicating assembly results to others. Results We developed Alvis, a simple command line tool that can generate visualisations for a number of common alignment analysis tasks. Alvis is a fast and portable tool that accepts input in a variety of alignment formats and will output production ready vector images. Additionally, Alvis will highlight potentially chimeric reads or contigs, a common source of misassemblies. Conclusion Alvis diagrams facilitate improved understanding of assembly quality, enable read coverage to be visualised and potential errors to be identified. Additionally, we found that splitting chimeric reads using the output provided by Alvis can improve the contiguity of assemblies, while maintaining correctness.

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

Sustainable supply chains based on supplier selection and HRM practices

PurposeThe purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.Design/methodology/approachA mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.FindingsThis study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.Practical implicationsThis study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.Originality/valueThis study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

An overview of non-destructive approaches for quality determination in pineapples

Pineapple is one of the healthful and popular tropical fruits in the world. The quality determination of pineapples was mostly evaluated by human inspection which is inconsistent and subjective. The increasing demand for pineapples creates more opportunities for the advancement of rapid and non-destructive approaches to seek quality evaluation of the fruit. This review gives an overview of the non-destructive approaches on the quality determination of pineapples including computer vision, imaging-based approaches, spectroscopy-based approaches, acoustic impulse, and electronic nose. The advance of non-destructive techniques to evaluate the quality of pineapple can produce better yield and improve postharvest handling. This paper also highlighted the recent works on the quality determination of pineapple fruit using non-destructive approaches along with the abundant information that can be explored for real-time purposes. This information is expected to be useful not only for pineapples growers/industries but also for other agro-food commodities.