A novel application of deep learning with image cropping: a smart city use case for flood monitoring

© 2020, The Author(s). Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms

Numerical Simulation of Plane Crack Problems Using Extended Isogeometric Analysis

AbstractThis paper presents the simulation of plane crack problems using extended isogeometric analysis (XIGA). In XIGA, both geometry and solution are approximated using NURBS basis functions. Discontinuous Heaviside function is used to model the crack face, while crack tip singularity is modeled using asymptotic crack tip enrichment functions. Few plane crack problems are solved in the presence of multiple holes and inclusions using XIGA. These simulations show that the SIFs obtained using XIGA gives more accurate results as compared to those obtained by XFEM

Explainable artificial intelligence for developing smart cities solutions

Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results

Substrate effect on the structural and electrochemical properties of electrolytic manganese dioxide deposited from sulphate solutions

We studied the effect of anode substrates such as pure lead (Pb), lead antimony (Pb-Sb), and lead-silver (Pb-Ag) on the structural and electrochemical properties of electrolytic manganese dioxide (EMD). X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and chemical analyses were used to determine the structural and chemical characteristics of the EMD samples. The charge–discharge profile was studied in 9 M KOH using a galvanostatic charge-discharge unit. In all the substrates the current efficiencies were more than 99% except with Pb-Sb where it was 90%. Results revealed the nature of the substrate strongly affected the morphology of the deposited material which in turn affected the electrochemical properties of the EMD samples. XRD analyses revealed that the nature of the anode did not affect the crystal structure of the deposited EMD and all the samples were predominantly γ-MnO 2 , which is electrochemically active for energy storage applications. The EMD deposited on lead substrate showed superior discharge capacity of 245 mAhg -1 when compared with other substrates

Recovering Relativistic Nuclear Phenomenology from the Quark-Meson Coupling Model

The quark-meson coupling (QMC) model for nuclear matter, which describes nuclear matter as non-overlapping MIT bags bound by the self-consistent exchange of scalar and vector mesons is modified by the introduction of a density dependent bag constant. The density dependence of the bag constant is related to that of the in-medium effective nucleon mass through a scaling ansatz suggested by partial chiral symmetry restoration in nuclear matter. This modification overcomes drawbacks of the QMC model and leads to the recovery of the essential features of relativistic nuclear phenomenology. This suggests that the modification of the bag constant in the nuclear medium may play an important role in low- and medium-energy nuclear physics.Comment: Revised version to appear in Phys. Lett.

Particle density fluctuations

Event-by-event fluctuations in the multiplicities of charged particles and photons at SPS energies are discussed. Fluctuations are studied by controlling the centrality of the reaction and rapidity acceptance of the detectors. Results are also presented on the event-by-event study of correlations between the multiplicity of charged particles and photons to search for DCC-like signals.Comment: Talk presented at Quark Matter 2002, Nantes, Franc

Search for DCC in 158A GeV Pb+Pb Collisions

A detailed analysis of the phase space distributions of charged particles and photons have been carried out using two independent methods. The results indicate the presence of nonstatistical fluctuations in localized regions of phase space.Comment: Talk at the PANIC99 Conference, June 9-16, 199

Pion Freeze-Out Time in Pb+Pb Collisions at 158 A GeV/c Studied via pi-/pi+ and K-/K+ Ratios

The effect of the final state Coulomb interaction on particles produced in Pb+Pb collisions at 158 A GeV/c has been investigated in the WA98 experiment through the study of the pi-/pi+ and K-/K+ ratios measured as a function of transverse mass. While the ratio for kaons shows no significant transverse mass dependence, the pi-/pi+ ratio is enhanced at small transverse mass values with an enhancement that increases with centrality. A silicon pad detector located near the target is used to estimate the contribution of hyperon decays to the pi-/pi+ ratio. The comparison of results with predictions of the RQMD model in which the Coulomb interaction has been incorporated allows to place constraints on the time of the pion freeze-out.Comment: 9 pages, 12 figure

Present Status and Future of DCC Analysis

Disoriented Chiral Condensates (DCC) have been predicted to form in high energy heavy ion collisions where the approximate chiral symmetry of QCD has been restored. This leads to large imbalances in the production of charged to neutral pions. Sophisticated analysis methods are being developed to disentangle DCC events out of the large background of events with conventionally produced particles. We present a short review of current analysis methods and future prospects.Comment: 12 pages, 5 figures. Invited talk presented at the 13th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter 97), Tsukuba, Japan, 1-5 Dec 199