Search results for: edge detection method

Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City

Procedia PDF Downloads 437

Authors: Yatimah Alias, Tilagam Marimuthu, M. R. Mahmoudian, Sharifah Mohamad

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The rapid growth of science and technology in energy and environmental fields has enlightened the substantial importance of the conducting polymer and metal composite materials engineered at nano-scale. In this study, polypyrrole-cobalt composites (PPy-Co Cs) and polypyrrole-nickel oxide composites (PPy-NiO Cs) were prepared by a simple and facile chemical polymerization method with an aqueous solution of pyrrole monomer in the presence of metal salt. These composites then fabricated into non-enzymatic hydrogen peroxide (H2O2) and glucose sensor. The morphology and composition of the composites are characterized by the Field Emission Scanning Electron Microscope, Fourier Transform Infrared Spectrum and X-ray Powder Diffraction. The obtained results were compared with the pure PPy and metal oxide particles. The structural and morphology properties of synthesized composites are different from those of pure PPy and metal oxide particles, which were attributed to the strong interaction between the PPy and the metal particles. Besides, a favorable micro-environment for the electrochemical oxidation of H2O2 and glucose was achieved on the modified glassy carbon electrode (GCE) coated with PPy-Co Cs and PPy-NiO Cs respectively, resulting in an enhanced amperometric response. Both PPy-Co/GCE and PPy-NiO/GCE give high response towards target analyte at optimum condition of 500 μl pyrrole monomer content. Furthermore, the presence of pyrrole monomer greatly increases the sensitivity of the respective modified electrode. The PPy-Co/GCE could detect H2O2 in a linear range of 20 μM to 80 mM with two linear segments (low and high concentration of H2O2) and the detection limit for both ranges is 2.05 μM and 19.64 μM, respectively. Besides, PPy-NiO/GCE exhibited good electrocatalytic behavior towards glucose oxidation in alkaline medium and could detect glucose in linear ranges of 0.01 mM to 0.50 mM and 1 mM to 20 mM with detection limit of 0.33 and 5.77 μM, respectively. The ease of modifying and the long-term stability of this sensor have made it superior to enzymatic sensors, which must kept in a critical environment.

Keywords: metal oxide, composite, non-enzymatic sensor, polypyrrole

Procedia PDF Downloads 267

Authors: Evgen Prokhorov, Gabriel Luna-Barcenas

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The polysaccharide chitosan (CS) is an attractive biopolymer for the stabilization of several nanoparticles in acidic aqueous media. This is due in part to the presence of abundant primary NH2 and OH groups which may lead to steric or chemical stabilization. Applications of most CS nanocomposites are based upon the interaction of high surface area nanoparticles (NPs) with different substance. Therefore, agglomeration of NPs leads to decreasing effective surface area such that it may decrease the efficiency of nanocomposites. The aim of this work is to measure nanocomposite’s electrical conductivity phenomena that will allow one to formulate optimal concentrations of conductivity NPs in CS-based nanocomposites. Additionally, by comparing the efficiency of such nanocomposites, one can guide applications in the biomedical (antibacterial properties and tissue regeneration) and sensor fields (detection of copper and nitrate ions in aqueous solutions). It was shown that the best antibacterial (CS-AgNPs, CS-AgNPs-carbon nanotubes) and would healing properties (CS-AuNPs) are observed in nanocomposites with concentrations of NPs near the percolation threshold. In this regard, the best detection limit in potentiometric and impedimetric sensors for detection of copper ions (using CS-AuNPs membrane) and nitrate ions (using CS-clay membrane) in aqueous solutions have been observed for membranes with concentrations of NPs near percolation threshold. It is well known that at the percolation concentration of NPs an abrupt increasing of conductivity is observed due to the presence of physical contacts between NPs; above this concentration, agglomeration of NPs takes place such that a decrease in the effective surface and performance of nanocomposite appear. The obtained relationship between electrical percolation threshold and performance of polymer nanocomposites with conductivity NPs is important for the design and optimization of polymer-based nanocomposites for different applications.

Keywords: chitosan, conductivity nanoparticles, percolation threshold, polymer nanocomposites

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Authors: Mulugeta Gurum Gerechal

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Nowadays, the photocatalytic mechanism of water purification using nanoparticles has gained wider acceptance. For this purpose, the crystal form of N- TiO₂ and Ag-TiO₂ was prepared from TiCl₄, urea, NH₄OH, and AgNO₃ by sol-gel method and simple solid phase reaction followed by calcination at a temperature of 400°C for 4h at each. The synthesized photocatalysts were characterized using XRD, SEM, and UV-visible diffuse reflectance spectra. In the experiment, it was found that the absorption edge of N-TiO₂ was an efficient shift to visible light as compared to Ag-TiO₂. The XRD diffraction makes the particle size of N-TiO₂ smaller than Ag-TiO₂. The effect of catalyst loading and the effect of temperature on the photocatalytic efficiency of the prepared samples was tested using methylene blue as a target pollutant. The photocatalytic degradation efficiency of the catalysts for methylene blue was increased from 57.05 to 96.02% under solar radiation as the amount of the catalyst increased from 0.15 to 0.45 gram for N-TiO₂. Similarly, photocatalytic degradation of methylene blue was increased from 40.32 to 81.21% as the amount of Ag-TiO₂ increased from 0.05g to 0.1g. In addition, the photocatalytic degradation efficiency of the catalysts for the removal of methylene blue was increased from 58.00 to 98.00 and 47.00 to 81.21% under solar radiation as the calcination temperature of the catalyst increased from 300 to 500 for N-TiO₂ for Ag-TiO₂ 300 to 400⁰C. However, a further increase in catalyst loading and calcination temperature was found to decrease the degradation efficiency.

Keywords: photocatalysis, degradation, nanoparticles, catalyst loading, calcination, methylene blue

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

Procedia PDF Downloads 234

Authors: M. O. Olayiwola

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Numerical solutions of the generalized Burger-Fisher are obtained using a Modified Variational Iteration Method (MVIM) with minimal computational efforts. The computed results with this technique have been compared with other results. The present method is seen to be a very reliable alternative method to some existing techniques for such nonlinear problems.

Keywords: burger-fisher, modified variational iteration method, lagrange multiplier, Taylor’s series, partial differential equation

Procedia PDF Downloads 433

Authors: Rima A. Ajlouni

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The discovery of quasi-periodic structures in material science is revealing an exciting new class of symmetries, which has never been explored before. Due to their unique structural and visual properties, these symmetries are drawing interest from many scientific and design disciplines. Especially, in art and architecture, these symmetries can provide a rich source of geometry for exploring new patterns, forms, systems, and structures. However, the structural systems of these complicated symmetries are still posing a perplexing challenge. While much of their local order has been explored, the global governing system is still unresolved. Understanding their unique global long-range order is essential to their generation and application. The recent discovery of dodecagonal quasi-periodic patterns in historical Islamic architecture is generating a renewed interest into understanding the mathematical principles of traditional Islamic geometry. Astonishingly, many centuries before its description in the modern science, ancient artists, by using the most primitive tools (a compass and a straight edge), were able to construct patterns with quasi-periodic formations. These ancient patterns can be found all over the ancient Islamic world, many of which exhibit formations with 5, 8, 10 and 12 quasi-periodic symmetries. Based on the examination of these historical patterns and derived from the generating principles of Islamic geometry, a global multi-level structural model is presented that is able to describe the global long-range order of dodecagonal quasi-periodic formations in Islamic Architecture. Furthermore, this method is used to construct new quasi-periodic tiling systems as well as generating their deflation and inflation rules. This method can be used as a general guiding principle for constructing infinite patches of dodecagon-based quasi-periodic formations, without the need for local strategies (tiling, matching, grid, substitution, etc.) or complicated mathematics; providing an easy tool for scientists, mathematicians, teachers, designers and artists, to generate and study a wide range of dodecagonal quasi-periodic formations.

Keywords: dodecagonal, Islamic architecture, long-range order, quasi-periodi

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Authors: Eva Savy, Anthony Ruiz

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In extrusion blow molding, there is great variability in product quality due to the sensitivity of the machine settings. These variations lead to unnecessary rejects and loss of time. Yet production control is a major challenge for companies in this sector to remain competitive within their market. Current quality control methods only apply to finished products (vision control, leak test...). It has been shown that material melt temperature, blowing pressure, and ambient temperature have a significant impact on the variability of product quality. Since blowing is a key step in the process, we have studied this parameter in this paper. The objective is to determine if airflow analysis allows the identification of quality problems before the full completion of the manufacturing process. We conducted tests to determine if it was possible to identify a leakage defect and an obstructed defect, two common defects on products. The results showed that it was possible to identify a leakage defect by airflow analysis.

Keywords: extrusion blow molding, signal, sensor, defects, detection

Procedia PDF Downloads 156

Authors: Rossana Taravella, Gilberto M. Cellura, Giuseppe Cirrincione, Salvatore Asciutto, Marco Caruso, Massimo Benedetto, Renato Ciofalo, Giuliana Pace, Salvatore Novo

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Objectives: This retrospective analysis sought to evaluate 1-month outcomes and therapy effectiveness of a population of patients treated with MitraClip therapy. We describe in this article the preliminary results of primary effectiveness endpoint. Background: Percutaneous Mitral Repair is being developed to treat severe mitral regurgitation (MR), with increasing real-world cases of functional MR (FMR). In the EVEREST (Endovascular Valve Edge-to-Edge Repair Study)II trial, the percutaneous device showed superior safety but less reduction in MR at 1year. 4-year outcomes from EVEREST II trial showed no difference in the prevalence of moderate-severe and severe MR or mortality at 4years between surgical mitral repair and percutaneous approach. Methods: We analysed retrospectively collected data from one center experience in Italy enrolled from January 2011 to December 2016. The study included 62 patients [mean age 74±11years, 43 men (69%)] with MR of at least grade3+. Most of the patients had functional MR, were in New York Heart Association (NYHA) functional class III or IV, with a large portion (78%) of mild-to-moderate Tricuspid Regurgitation (TR). One or more clips were implanted in 67 procedures (62 patients). Results and Conclusions: Severity of MR was reduced in all successfully treated patients,54(90%) were discharged with MR≤2+ (primary effectiveness endpoint). Clinical 1-month follow-up data showed an improvement in NYHA functional class (42 patients (70%) in NYHA class I-II). 60 of 62 (97 %) successfully treated patients were free from death and mitral valve surgery at 1-month follow-up. MitraClip therapy reduces functional MR with acute MR reduction to <2+ in the great majority of patients, with a large freedom from death, surgery or recurrent MR in a great portion of patients.

Keywords: MitraClip, mitral regurgitation, heart valves, catheter-based therapy

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Authors: Li Ziming, He Cunfu, Liu Zenghua

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With aim for delamination type defects detection in manufacturing process of seamless pipe,this paper studies the interaction of the circumferential lamb wave with delamination in aluminum pipe.The delamination is located in the middle of pipe wall.A numerical study is carried out,the circumferential lamb wave used here is CL0 mode,which is generated with a finite element method code.Wave structures from the simulation are compared with theoretical results to verify the model’s accuracy.Delamination along the circumferential direction is established by demerging nodes of the same coordinates.When CL0 mode is incident at the entrance and exit of a delamination,it generates new mode-CL1,undergoes multiple reverberation and mode conversions between the two ends of the delamination. Signals of different receptions are obtained to provide insight in using CL0 mode for locating the delamination.

Keywords: circumferential lamb wave, delamination, FEM, seamless pipe

Procedia PDF Downloads 316

Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

Procedia PDF Downloads 698

Authors: Rupesh K. Mishra, Gaëlle Catanante, Akhtar Hayat, Jean-Louis Marty

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Ochratoxins (OTA) are secondary metabolites present in a wide variety of food stuff. They are dangerous by-products mainly produced by several species of storage fungi including the Aspergillus and Penicillium genera. OTA is known to have nephrotoxic, immunotoxic, teratogenic and carcinogenic effects. Thus, needs a special attention for a highly sensitive and selective detection system that can quantify these organic toxins in various matrices such as cocoa beans. This work presents a folding based aptasensors by employing an aptamer conjugated redox probe (methylene blue) specifically designed for OTA. The aptamers were covalently attached to the screen printed carbon electrodes using diazonium grafting. Upon sensing the OTA, it binds with the immobilized aptamer on the electrode surface, which induces the conformational changes of the aptamer, consequently increased in the signal. This conformational change of the aptamer before and after biosensing of target OTA could produce the distinguishable electrochemical signal. The obtained limit of detection was 0.01 ng/ml for OTA samples with recovery of up to 88% in contaminated cocoa samples.

Keywords: ochratoxin A, cocoa, DNA aptamer, labelled probe

Procedia PDF Downloads 287

Authors: Mulugeta Gurum Gerechal

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Nowadays, the photocatalytic mechanism of water purification using nanoparticles has gained wider acceptance. For this purpose, the Crystal form of N- TiO₂ and Ag-TiO₂ was prepared from TiCl₄, Urea, NH₄OH and AgNO₃ by sol-gel method and simple solid phase reaction followed by calcination at a temperature of 400 °C for 4h at each. The synthesized photocatalysts were characterized using XRD, SEM and UV-visible diffuse reflectance spectra. In the experiment, it was found that the absorption edge of N-TiO₂ was a well efficient shift to visible light as compared to Ag-TiO₂. The XRD diffraction makes the particle size of N-TiO₂ smaller than Ag-TiO₂. The effect of catalyst loading and the effect of temperature on the photocatalytic efficiency of the prepared samples was tested using methylene blue as a target pollutant. The photocatalytic degradation efficiency of the catalysts for methylene blue was increased from 57.05 to 96.02% under solar radiation as the amount of the catalyst increased from 0.15 to 0.45 gram for N-TiO₂. Similarly, photocatalytic degradation of methylene blue was increased from 40.32 to 81.21% as the amount of Ag-TiO₂ increased from 0.05g to 0.1g. In addition, the photocatalytic degradation efficiency of the catalysts for the removal of methylene blue was increased from 58.00 to 98.00 and 47.00 to 81.21 % under solar radiation as the calcination temperature of the catalyst increased from 300 to 500 for N-TiO₂ for Ag-TiO₂ 300 to 4000C. However, a further increase in catalyst loading and calcination temperature was found to decrease the degradation efficiency.

Keywords: photocatalysis, degradation, nanoparticles, catalyst loading, calcination and methylene blue

Procedia PDF Downloads 67

Authors: Maha Al-Soudani, Gilles Klysz, Jean-Paul Balayssac

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The aim of this paper is to use Ground Penetrating Radar (GPR) as a non-destructive testing (NDT) method to increase its accuracy in recognizing the geometric reinforced concrete structures and in particular, the position of steel bars. This definition will help the managers to assess the state of their structures on the one hand vis-a-vis security constraints and secondly to quantify the need for maintenance and repair. Several configurations of acquisition and processing of the simulated signal were tested to propose and develop an appropriate imaging algorithm in the propagation medium to locate accurately the rebar. A subsequent experimental validation was used by testing the imaging algorithm on real reinforced concrete structures. The results indicate that, this algorithm is capable of estimating the reinforcing steel bar position to within (0-1) mm.

Keywords: GPR, NDT, Reinforced concrete structures, Rebar location.

Procedia PDF Downloads 506

Authors: Amani Abdallah, Isam Shahrour

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The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration.

Keywords: distribution system, drinking water, refraction index, sensor, real-time

Procedia PDF Downloads 358

Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

Procedia PDF Downloads 390

Authors: Mohammad Ahmad, Dayalan Kasilingam

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In this paper, a spectral domain implementation of the fast multipole method is presented. It is shown that the aggregation, translation, and disaggregation stages of the fast multipole method (FMM) can be performed using the spectral domain (SD) analysis. The spectral domain fast multipole method (SD-FMM) has the advantage of eliminating the near field/far field classification used in conventional FMM formulation. The study focuses on the application of SD-FMM to one-dimensional (1D) and two-dimensional (2D) electric field integral equation (EFIE). The case of perfectly conducting strip, circular and square cylinders are numerically analyzed and compared with the results from the standard method of moments (MoM).

Keywords: electric field integral equation, fast multipole method, method of moments, wave scattering, spectral domain

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Authors: Alkaben Patel

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The proposed RP-HPLC method is easy, rapid, economical, precise and accurate stability indicating RP-HPLC method for simultaneous estimation of Astorvastatin and Methylcobalamine in their combined dosage form has been developed.The separation was achieved by LC-20 AT C18(250mm*4.6mm*2.6mm)Colum and water (pH 3.5): methanol 70:30 as mobile phase, at a flow rate of 1ml/min. wavelength of this dosage form is 215nm.The drug is related to stress condition of hydrolysis, oxidation, photolysis and thermal degradation.

Keywords: RP- HPLC, atorvastatin, methylcobalamine, method, development, validation

Procedia PDF Downloads 338

Authors: Sneha Leslie

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The current situation in the cyber world is that crimes performed by Botnets are increasing and the masterminds (botmaster) are not detectable easily. The botmaster in the botnet compromises the legitimate host machines in the network and make them bots or zombies to initiate the cyber-attacks. This paper will focus on the live detection of the botmaster in the network by using the strong framework 'metasploit', when distributed denial of service (DDOS) attack is performed by the botnet. The affected victim machine will be continuously monitoring its incoming packets. Once the victim machine gets to know about the excessive count of packets from any IP, that particular IP is noted and details of the noted systems are gathered. Using the vulnerabilities present in the zombie machines (already compromised by botmaster), the victim machine will compromise them. By gaining access to the compromised systems, applications are run remotely. By analyzing the incoming packets of the zombies, the victim comes to know the address of the botmaster. This is an effective and a simple system where no specific features of communication protocol are considered.

Keywords: bonet, DDoS attack, network security, detection system, metasploit framework

Procedia PDF Downloads 255

Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

Procedia PDF Downloads 541

Authors: Hanene Sahli, Aymen Mouelhi, Marwa Hajji, Amine Ben Slama, Mounir Sayadi, Farhat Fnaiech, Radhwane Rachdi

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Fetal abnormality is still a public health problem of interest to both mother and baby. Head defect is one of the most high-risk fetal deformities. Fetal head categorization is a sensitive task that needs a massive attention from neurological experts. In this sense, biometrical measurements can be extracted by gynecologist doctors and compared with ground truth charts to identify normal or abnormal growth. The fetal head biometric measurements such as Biparietal Diameter (BPD), Occipito-Frontal Diameter (OFD) and Head Circumference (HC) needs to be monitored, and expert should carry out its manual delineations. This work proposes a new approach to automatically compute BPD, OFD and HC based on morphological characteristics extracted from head shape. Hence, the studied data selected at the same Gestational Age (GA) from the fetal Ultrasound images (US) are classified into two categories: Normal and abnormal. The abnormal subjects include hydrocephalus, microcephaly and dolichocephaly anomalies. By the use of a support vector machines (SVM) method, this study achieved high classification for automated detection of anomalies. The proposed method is promising although it doesn't need expert interventions.

Keywords: biometric measurements, fetal head malformations, machine learning methods, US images

Procedia PDF Downloads 290

Authors: Sebastian Töpfer, Marta Gilaberte Basset, Jorge Fuenzalida, Fabian Steinlechner, Juan P. Torres, Markus Gräfe

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This work introduces a combination of digital phase-shifting holography with a non-linear interferometer using undetected photons. Non-linear interferometers can be used in combination with a measurement scheme called quantum imaging with undetected photons, which allows for the separation of the wavelengths used for sampling an object and detecting it in the imaging sensor. This method recently faced increasing attention, as it allows to use of exotic wavelengths (e.g., mid-infrared, ultraviolet) for object interaction while at the same time keeping the detection in spectral areas with highly developed, comparable low-cost imaging sensors. The object information, including its transmission and phase influence, is recorded in the form of an interferometric pattern. To collect these, this work combines the method of quantum imaging with undetected photons with digital phase-shifting holography with a minimal sampling of the interference. With this, the quantum imaging scheme gets extended in its measurement capabilities and brings it one step closer to application. Quantum imaging with undetected photons uses correlated photons generated by spontaneous parametric down-conversion in a non-linear interferometer to create indistinguishable photon pairs, which leads to an effect called induced coherence without induced emission. Placing an object inside changes the interferometric pattern depending on the object’s properties. Digital phase-shifting holography records multiple images of the interference with determined phase shifts to reconstruct the complete interference shape, which can afterward be used to analyze the changes introduced by the object and conclude its properties. An extensive characterization of this method was done using a proof-of-principle setup. The measured spatial resolution, phase accuracy, and transmission accuracy are compared for different combinations of camera exposure times and the number of interference sampling steps. The current limits of this method are shown to allow further improvements. To summarize, this work presents an alternative holographic measurement method using non-linear interferometers in combination with quantum imaging to enable new ways of measuring and motivating continuing research.

Keywords: digital holography, quantum imaging, quantum holography, quantum metrology

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Authors: Lotfi O. Gargab, Ruichong R. Zhang

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A wave-based framework is presented for modeling seismic motion in multistory buildings and using measured response for system identification which can be utilized to extract important information regarding structure integrity. With one pair of building response at two locations, a generalized model response is formulated based on wave propagation features and expressed as frequency and time response functions denoted, respectively, as GFRF and GIRF. In particular, GIRF is fundamental in tracking arrival times of impulsive wave motion initiated at response level which is dependent on local model properties. Matching model and measured-structure responses can help in identifying model parameters and infer building properties. To show the effectiveness of this approach, the Millikan Library in Pasadena, California is identified with recordings of the Yorba Linda earthquake of September 3, 2002.

Keywords: system identification, continuous-discrete mass modeling, damage detection, post-earthquake

Procedia PDF Downloads 371

Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

Abstract:

Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

Procedia PDF Downloads 102

Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

Abstract:

Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

Procedia PDF Downloads 183

Authors: Alaa Abdullah Altaee

Abstract:

This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

Procedia PDF Downloads 125

Authors: Zakia Fatima, Liu Lu, Donghao Li

Abstract:

The analysis of biological fluid metabolites holds significant importance in various areas, such as medical research, food science, and public health. Investigating the levels and distribution of nutrients and their metabolites in biological samples allows researchers and healthcare professionals to determine nutritional status, find hypovitaminosis or hypervitaminosis, and monitor the effectiveness of interventions such as dietary supplementation. Moreover, analysis of nutrient metabolites provides insight into their metabolism, bioavailability, and physiological processes, aiding in the clarification of their health roles. Hence, the exploration of a distinct, efficient, eco-friendly, and simpler methodology is of great importance to evaluate the metabolic content of complex biological samples. In this work, a green and rapid analytical method based on an automated online two-dimensional microscale carbon fiber/activated carbon fiber fractionation system and time-of-flight mass spectrometry (2DμCFs-TOF-MS) was used to evaluate metabolites of urine samples after oral administration of food supplements. The automated 2DμCFs instrument consisted of a microcolumn system with bare carbon fibers and modified carbon fiber coatings. Carbon fibers and modified carbon fibers exhibit different surface characteristics and retain different compounds accordingly. Three kinds of mobile-phase solvents were used to elute the compounds of varied chemical heterogeneities. The 2DμCFs separation system has the ability to effectively separate different compounds based on their polarity and solubility characteristics. No complicated sample preparation method was used prior to analysis, which makes the strategy more eco-friendly, practical, and faster than traditional analysis methods. For optimum analysis results, mobile phase composition, flow rate, and sample diluent were optimized. Water-soluble vitamins, fat-soluble vitamins, and amino acids, as well as 22 vitamin metabolites and 11 vitamin metabolic pathway-related metabolites, were found in urine samples. All water-soluble vitamins except vitamin B12 and vitamin B9 were detected in urine samples. However, no fat-soluble vitamin was detected, and only one metabolite of Vitamin A was found. The comparison with a blank urine sample showed a considerable difference in metabolite content. For example, vitamin metabolites and three related metabolites were not detected in blank urine. The complete single-run screening was carried out in 5.5 minutes with the minimum consumption of toxic organic solvent (0.5 ml). The analytical method was evaluated in terms of greenness, with an analytical greenness (AGREE) score of 0.72. The method’s practicality has been investigated using the Blue Applicability Grade Index (BAGI) tool, obtaining a score of 77. The findings in this work illustrated that the 2DµCFs-TOF-MS approach could emerge as a fast, sustainable, practical, high-throughput, and promising analytical tool for screening and accurate detection of various metabolites, pharmaceuticals, and ingredients in dietary supplements as well as biological fluids.

Keywords: metabolite analysis, sustainability, carbon fibers, urine.

Procedia PDF Downloads 31

Authors: Zzibu Mark Brian

Abstract:

As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

Procedia PDF Downloads 41