Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

Scientific Advances in STEM: From Professor to Students

This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

Material Management Framework utilizing Near Real-Time Monitoring of Construction Operations

Materials management is a vital process in the delivery of construction facilities. Studies by the Construction Industry Institute (CII) have demonstrated that materials and installed equipment can constitute 40– 70% of the total construction hard cost and affect 80% of the project schedule. Despite its significance, most of the construction industry sectors are suffering from poor material management processes including inaccurate warehouse records, over-ordering and large surpluses of material at project completion, poor site storage practices, running out of materials, late deliveries, double-handling of components, out-of-specification material, and out of sequence deliveries which all result in low productivity, delay in construction and cost overruns. Inefficient material management can be attributed to the complex, unstructured, and dynamic nature of the construction industry, which has not been considered in a large number of studies available in this field. The literature reveals that available computer-based materials management systems focus on (1) integration of the materials management functions, and (2) application of Automated Data Collection (ADC) technologies to collect materials localization and tracking data for their computerized materials management systems. Moreover in studies that focused on applying ADC technologies in construction materials management, positioning and tracking critical resources in construction sites, and identifying unique materials received at the job site are the main applications of their used technologies. Even though, various studies have improved materials management processes copiously in the construction industry, the benefits of considering the dynamic nature of construction (in terms of near real-time progress monitoring using state of the art technologies and techniques) and its integration with a dynamic materials management system have been left out. So, in contrast with other studies, this research presents a construction materials management framework capable of considering the dynamic nature of construction projects. It includes a vital component to monitor project progress in near real-time to estimate the installation and consumption of materials. This framework consists of three models: “preconstruction model,” “construction model,” and “data analysis and reporting model.” This framework enables (1) generation of optimized material delivery schedules based on Material Requirement Planning (MRP) and minimum total cost, (2) issuance of material Purchase Orders (POs) according to optimized delivery schedules, (3) tracking the status of POs (Expediting methods), (4) collection and assessment of material data as it arrives on site, (5) considering the inherent dynamics of construction operations by monitoring project progress to update project schedule and estimate near real-time consumption of materials and eventually (6) updating MRP and optimized delivery schedule frequently throughout the construction phase. An optimized material delivery schedule and an optimized purchase schedule with the least cost are generated by the preconstruction model to avoid consequences of early/late purchasing and excess/inadequate purchasing. Accurate assessment of project progress and estimation of installed or consumed materials are essential for an effective construction material management system. The construction model focuses on the collection of near real-time site data using ADC technologies. Project progress is visualized from two different perspectives, comparing as-built with as-planned and comparing various as-built status captured on consecutive points of time. Due to the recent improvements in digital photography and webcams, which made this technology more cost-effective and practical for monitoring project progress, digital imaging (including 360° images) is selected and applied for project progress monitoring in the construction (data acquisition) model. In the last model, which is the data analysis and reporting model, Deep Learning (DL) and image processing algorithms are proposed to visualize and detect actual progress in terms of built elements in near real-time. In contrast with the other studies in which conventional computer vision algorithms are often used to monitor projects progress, in this research, a deep Convolutional Auto-Encoder (CAE) and Mask Region-based Convolutional Neural Network (R-CNN) are utilized to facilitate vision-based indoor and outdoor progress monitoring of construction operations. The updated project schedule based on the actual progress is the output of this model, and it is used as the primary input for the developed material management framework to update MRP, optimized material delivery, and purchase schedules, respectively. Applicability of the models in the developed material management framework has been tested through laboratory and field experiments. The results demonstrated the accuracy and capabilities of the developed models in the framework