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

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

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

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Defect Detection and Classification in Sewer Pipeline Inspection Videos Using Deep Neural Networks

Sewer pipelines as a critical civil infrastructure become a concern for municipalities as they are getting near to the end of their service lives. Meanwhile, new environmental laws and regulations, city expansions, and budget constraints make it harder to maintain these networks. On the other hand, access and inspect sewer pipelines by human-entry based methods are problematic and risky. Current practice for sewer pipeline assessment uses various types of equipment to inspect the condition of pipelines. One of the most used technologies for sewer pipelines inspection is Closed Circuit Television (CCTV). However, application of CCTV method in extensive sewer networks involves certified operators to inspect hours of videos, which is time-consuming, labor-intensive, and error prone. The main objective of this research is to develop a framework for automated defect detection and classification in sewer CCTV inspection videos using computer vision techniques and deep neural networks. This study presents innovative algorithms to deal with the complexity of feature extraction and pattern recognition in sewer inspection videos due to lighting conditions, illumination variations, and unknown patterns of various sewer defects. Therefore, this research includes two main sub-models to first identify and localize anomalies in sewer inspection videos, and in the next phase, detect and classify the defects among the recognized anomalous frames. In the first phase, an innovative approach is proposed for identifying the frames with potential anomalies and localizing them in the pipe segment which is being inspected. The normal and anomalous frames are classified utilizing a one-class support vector machine (OC-SVM). The proposed approach employs 3D Scale Invariant Feature Transform (SIFT) to extract spatio-temporal features and capture scene dynamic statistics in sewer CCTV videos. The OC-SVM is trained by the frame-features which are considered normal, and the outliers to this model are considered abnormal frames. In the next step, the identified anomalous frames are located by recognizing the present text information in them using an end-to-end text recognition approach. The proposed localization approach is performed in two steps, first the text regions are detected using maximally stable extremal regions (MSER) algorithm, then the text characters are recognized using a convolutional neural network (CNN). The performance of the proposed model is tested using videos from real-world sewer inspection reports, where the accuracies of 95% and 86% were achieved for anomaly detection and frame localization, respectively. Identifying the anomalous frames and excluding the normal frames from further analysis could reduce the time and cost of detection. It also ensures the accuracy and quality of assessment by reducing the number of neglected anomalous frames caused by operator error. In the second phase, a defect detection framework is proposed to provide defect detection and classification among the identified anomalous frames. First, a deep Convolutional Neural Network (CNN) which is pre-trained using transfer learning, is used as a feature extractor. In the next step, the remaining convolutional layers of the constructed model are trained by the provided dataset from various types of sewer defects to detect and classify defects in the anomalous frames. The proposed methodology was validated by referencing the ground truth data of a dataset including four defects, and the mAP of 81.3% was achieved. It is expected that the developed model can help sewer inspectors in much faster and more accurate pipeline inspection. The whole framework would decrease the condition assessment time and increase the accuracy of sewer assessment reports

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well