Search results for: deep beams
1917 Research on Design Methods for Riverside Spaces of Deep-cut Rivers in Mountainous Cities: A Case Study of Qingshuixi River in Chongqing City
Authors: Luojie Tang
Abstract:
Riverside space is an important public space and ecological corridor in urban areas, but mountainous urban rivers are often overlooked due to their deep valleys and poor accessibility. This article takes the Qing Shui Xi River in Chongqing as an example, and through long-term field inspections, measurements, interviews, and online surveys, summarizes the problems of poor accessibility, limited space for renovation, lack of waterfront facilities, excessive artificial intervention, low average runoff, severe river water pollution, and difficulty in integrated watershed management in riverside space. Based on the current situation and drawing on relevant experiences, this article summarizes the design methods for riverside space in deep valley rivers in mountainous urban areas. Regarding spatial design techniques, the article emphasizes the importance of integrating waterfront spaces into the urban public space system and vertical linkages. Furthermore, the article suggests different design methods and improvement strategies for the already developed areas and new development areas. Specifically, the article proposes a planning and design strategy of "protection" and "empowerment" for new development areas and an updating and transformation strategy of "improvement" and "revitalization" for already developed areas. In terms of ecological restoration methods, the article suggests three focus points: increasing the runoff of urban rivers, raising the landscape water level during dry seasons, and restoring vegetation and wetlands in the riverbank buffer zone while protecting the overall pattern of the watershed. Additionally, the article presents specific design details of the Qingshuixi River to illustrate the proposed design and restoration techniques.Keywords: deep-cut river, design method, mountainous city, Qingshuixi river in Chongqing, waterfront space design
Procedia PDF Downloads 1001916 Deep Reinforcement Learning for Advanced Pressure Management in Water Distribution Networks
Authors: Ahmed Negm, George Aggidis, Xiandong Ma
Abstract:
With the diverse nature of urban cities, customer demand patterns, landscape topologies or even seasonal weather trends; managing our water distribution networks (WDNs) has proved a complex task. These unpredictable circumstances manifest as pipe failures, intermittent supply and burst events thus adding to water loss, energy waste and increased carbon emissions. Whilst these events are unavoidable, advanced pressure management has proved an effective tool to control and mitigate them. Henceforth, water utilities have struggled with developing a real-time control method that is resilient when confronting the challenges of water distribution. In this paper we use deep reinforcement learning (DRL) algorithms as a novel pressure control strategy to minimise pressure violations and leakage under both burst and background leakage conditions. Agents based on asynchronous actor critic (A2C) and recurrent proximal policy optimisation (Recurrent PPO) were trained and compared to benchmarked optimisation algorithms (differential evolution, particle swarm optimisation. A2C manages to minimise leakage by 32.48% under burst conditions and 67.17% under background conditions which was the highest performance in the DRL algorithms. A2C and Recurrent PPO performed well in comparison to the benchmarks with higher processing speed and lower computational effort.Keywords: deep reinforcement learning, pressure management, water distribution networks, leakage management
Procedia PDF Downloads 771915 Particle Size Effect on Shear Strength of Granular Materials in Direct Shear Test
Authors: R. Alias, A. Kasa, M. R. Taha
Abstract:
The effect of particle size on shear strength of granular materials are investigated using direct shear tests. Small direct shear test (60 mm by 60 mm by 24 mm deep) were conducted for particles passing the sieves with opening size of 2.36 mm. Meanwhile, particles passing the standard 20 mm sieves were tested using large direct shear test (300 mm by 300 mm by 200 mm deep). The large direct shear tests and the small direct shear tests carried out using the same shearing rate of 0.09 mm/min and similar normal stresses of 100, 200, and 300 kPa. The results show that the peak and residual shear strength decreases as particle size increases.Keywords: particle size, shear strength, granular material, direct shear test
Procedia PDF Downloads 4791914 Comparison of Machine Learning and Deep Learning Algorithms for Automatic Classification of 80 Different Pollen Species
Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie
Abstract:
Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approachesKeywords: pollens identification, features extraction, pollens classification, automated palynology
Procedia PDF Downloads 1281913 Seashore Debris Detection System Using Deep Learning and Histogram of Gradients-Extractor Based Instance Segmentation Model
Authors: Anshika Kankane, Dongshik Kang
Abstract:
Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching
Procedia PDF Downloads 981912 Impact of Integrated Signals for Doing Human Activity Recognition Using Deep Learning Models
Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo
Abstract:
Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps
Procedia PDF Downloads 891911 Development of Digital Twin Concept to Detect Abnormal Changes in Structural Behaviour
Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling
Abstract:
Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model
Procedia PDF Downloads 941910 Analysis of Thermal Effect on Functionally Graded Micro-Beam via Mixed Finite Element Method
Authors: Cagri Mollamahmutoglu, Ali Mercan, Aykut Levent
Abstract:
Studies concerning the microstructures are becoming more important as the utilization of various micro-electro mechanical systems (MEMS) are increasing. Thus in recent years, thermal buckling and vibration analysis of microstructures have been subject to many investigations that are utilizing different numerical methods. In this study, thermal effects on mechanical response of a functionally graded (FG) Timoshenko micro-beam are presented in the framework of a mixed finite element formulation. Size effects are taken into consideration via modified couple stress theory. The mixed formulation is based on a function which in turn is derived via Gateaux Differential scientifically. After the resolution of all field equations of the beam, a potential operator is carefully constructed. Then this operator is used for the manufacturing of the functional. Usual procedures of finite element approximation are utilized for the derivation of the mixed finite element equations once the potential is obtained. Resulting finite element formulation allows usage of C₀ type simple linear shape functions and avoids shear-locking phenomena, which is a common shortcoming of the displacement-based formulations of moderately thick beams. The developed numerical scheme is used to obtain the effects of thermal loads on the static bending, free vibration and buckling of FG Timoshenko micro-beams for different power-law parameters, aspect ratios and boundary conditions. The versatility of the mixed formulation is presented over other numerical methods such as generalized differential quadrature method (GDQM). Another attractive property of the formulation is that it allows direct calculation of the contribution of micro effects on the overall mechanical response.Keywords: micro-beam, functionally graded materials, thermal effect, mixed finite element method
Procedia PDF Downloads 1331909 Characterization of the MOSkin Dosimeter for Accumulated Dose Assessment in Computed Tomography
Authors: Lenon M. Pereira, Helen J. Khoury, Marcos E. A. Andrade, Dean L. Cutajar, Vinicius S. M. Barros, Anatoly B. Rozenfeld
Abstract:
With the increase of beam widths and the advent of multiple-slice and helical scanners, concerns related to the current dose measurement protocols and instrumentation in computed tomography (CT) have arisen. The current methodology of dose evaluation, which is based on the measurement of the integral of a single slice dose profile using a 100 mm long cylinder ionization chamber (Ca,100 and CPPMA, 100), has been shown to be inadequate for wide beams as it does not collect enough of the scatter-tails to make an accurate measurement. In addition, a long ionization chamber does not offer a good representation of the dose profile when tube current modulation is used. An alternative approach has been suggested by translating smaller detectors through the beam plane and assessing the accumulated dose trough the integral of the dose profile, which can be done for any arbitrary length in phantoms or in the air. For this purpose, a MOSFET dosimeter of small dosimetric volume was used. One of its recently designed versions is known as the MOSkin, which is developed by the Centre for Medical Radiation Physics at the University of Wollongong, and measures the radiation dose at a water equivalent depth of 0.07 mm, allowing the evaluation of skin dose when placed at the surface, or internal point doses when placed within a phantom. Thus, the aim of this research was to characterize the response of the MOSkin dosimeter for X-ray CT beams and to evaluate its application for the accumulated dose assessment. Initially, tests using an industrial x-ray unit were carried out at the Laboratory of Ionization Radiation Metrology (LMRI) of Federal University of Pernambuco, in order to investigate the sensitivity, energy dependence, angular dependence, and reproducibility of the dose response for the device for the standard radiation qualities RQT 8, RQT 9 and RQT 10. Finally, the MOSkin was used for the accumulated dose evaluation of scans using a Philips Brilliance 6 CT unit, with comparisons made between the CPPMA,100 value assessed with a pencil ionization chamber (PTW Freiburg TW 30009). Both dosimeters were placed in the center of a PMMA head phantom (diameter of 16 cm) and exposed in the axial mode with collimation of 9 mm, 250 mAs and 120 kV. The results have shown that the MOSkin response was linear with doses in the CT range and reproducible (98.52%). The sensitivity for a single MOSkin in mV/cGy was as follows: 9.208, 7.691 and 6.723 for the RQT 8, RQT 9 and RQT 10 beams qualities respectively. The energy dependence varied up to a factor of ±1.19 among those energies and angular dependence was not greater than 7.78% within the angle range from 0 to 90 degrees. The accumulated dose and the CPMMA, 100 value were 3,97 and 3,79 cGy respectively, which were statistically equivalent within the 95% confidence level. The MOSkin was shown to be a good alternative for CT dose profile measurements and more than adequate to provide accumulated dose assessments for CT procedures.Keywords: computed tomography dosimetry, MOSFET, MOSkin, semiconductor dosimetry
Procedia PDF Downloads 3061908 The Influence of Steel Connection on Fire Resistance of Composite Steel-Framed Buildings
Authors: Mohammed Kadhim, Zhaohui Huang
Abstract:
Steel connections can play an important role in enhancing the robustness of structures under fire conditions. Therefore, it is significant to examine the influence of steel connections on the fire resistance of composite steel-framed buildings. In this paper, both the behavior of steel connections and their influence on composite steel frame are analyzed using the non-linear finite element computer software VULCAN at ambient and elevated temperatures. The chosen frame is subjected to ISO834 fire. The comparison between end plate connections, pinned connection, and rigid connection has been carried out. By applying different compartment fires, some cases are studied to show the behavior of steel connection when the fire is applied at certain beams. In addition, different plate thickness and deferent applied loads have been analyzed to examine the behavior of chosen steel connection under ISO834 fire. It was found from the analytical results that the beam with extended end plate is stronger and has better performance in terms of axial forces than those beams with flush end plate connection. It was also found that extended end plate connection has highest limiting temperatures compared to the flush end plate connection. In addition, it was found that the performance of end-plate connections is very close to rigid connection and very far from pinned connections. Furthermore, plate thickness has less effect on the influence of steel connection on fire resistance. In conclusion, the behavior of composite steel framed buildings is largely dependent on the steel connection due to their high impact under fire condition. It is recommended to consider the extended end-plate in the design proposes because of its higher properties compared to the flush end plate connection. Finally, this paper shows a steel connection has an important effect on the fire resistance of composite steel framed buildings.Keywords: composite steel-framed buildings, connection behavior, end-plate connections, finite element modeling, fire resistance
Procedia PDF Downloads 1541907 FE Modelling of Structural Effects of Alkali-Silica Reaction in Reinforced Concrete Beams
Authors: Mehdi Habibagahi, Shami Nejadi, Ata Aminfar
Abstract:
A significant degradation factor that impacts the durability of concrete structures is the alkali-silica reaction. Engineers are frequently charged with the challenges of conducting a thorough safety assessment of concrete structures that have been impacted by ASR. The alkali-silica reaction has a major influence on the structural capacities of structures. In most cases, the reduction in compressive strength, tensile strength, and modulus of elasticity is expressed as a function of free expansion and crack widths. Predicting the effect of ASR on flexural strength is also relevant. In this paper, a nonlinear three-dimensional (3D) finite-element model was proposed to describe the flexural strength degradation induced byASR.Initial strains, initial stresses, initial cracks, and deterioration of material characteristics were all considered ASR factors in this model. The effects of ASR on structural performance were evaluated by focusing on initial flexural stiffness, force–deformation curve, and load-carrying capacity. Degradation of concrete mechanical properties was correlated with ASR growth using material test data conducted at Tech Lab, UTS, and implemented into the FEM for various expansions. The finite element study revealed a better understanding of the ASR-affected RC beam's failure mechanism and capacity reduction as a function of ASR expansion. Furthermore, in this study, decreasing of the residual mechanical properties due to ASRisreviewed, using as input data for the FEM model. Finally, analysis techniques and a comparison of the analysis and the experiment results are discussed. Verification is also provided through analyses of reinforced concrete beams with behavior governed by either flexural or shear mechanisms.Keywords: alkali-silica reaction, analysis, assessment, finite element, nonlinear analysis, reinforced concrete
Procedia PDF Downloads 1541906 Water-Controlled Fracturing with Fuzzy-Ball Fluid in Tight Gas Reservoirs of Deep Coal Measures in Sulige
Authors: Xiangchun Wang, Lihui Zheng, Maozong Gan, Peng Zhang, Tong Wu, An Chang
Abstract:
The deep coal measure tight gas reservoir in Sulige is usually reformed by fracturing, because the reservoir thickness is small, the water layers can be easily communicated during fracturing, which will lead to water production of gas wells and lower production of gas wells. Therefore, it is necessary to control water during fracturing in deep coal measure tight gas reservoir. Using fuzzy-ball fluid to control water fracturing can not only increase the output but also reduce the water output. The fuzzy-ball fluid was prepared indoors to carry out evaluation experiments. The fuzzy ball fluid was mixed in equal volume with the pre-fluid and formation water to test its compatibility. The core displacement device was used to test the gas and water breaking through the matrix and fractured cores blocked by fuzzy-ball fluid. The breakthrough pressure of the plunger tests its water blocking performance. The experimental results show that there is no precipitation after the fuzzy-ball fluid is mixed with the pad fluid and the formation water, respectively. The breakthrough pressure gradients of gas and water after the fuzzy-ball fluid plugged the cracks were 0.02MPa/cm and 0.04MPa/cm, respectively, and the breakthrough pressure gradients of gas and water after the matrix was plugged were 0.03MPa/cm and 0.2MPa/cm, respectively, which meet the requirements of field operation. Two wells A and B in the Sulige Gas Field were used on site to implement water control fracturing. After the pre-fluid was injected into the two wells, 50m3 of fuzzy-ball fluid was pumped to plug the water. The construction went smoothly. After water control and fracturing, the average daily output in 161 days was increased by 13.71% and 6.99% compared with that of adjacent wells in the same layer. The adjacent wells were bubbled for 3 times and 63 times respectively, while there was no effusion in A and B construction wells. The results show that fuzzy-ball fluid is a water plugging material suitable for water control fracturing in tight gas wells, and its water control mechanism can also provide a new idea for the development of water control fracturing materials.Keywords: coal seam, deep layer, fracking, fuzzy-ball fluid, reservoir reconstruction
Procedia PDF Downloads 2171905 Keyframe Extraction Using Face Quality Assessment and Convolution Neural Network
Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba
Abstract:
Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network
Procedia PDF Downloads 2231904 Investigation of the Catalytic Role of Surfactants on Carbon Dioxide Hydrate Formation in Sediments
Authors: Ehsan Heidaryan
Abstract:
Gas hydrate sediments are ice like permafrost in deep see and oceans. Methane production in sequestration process and reducing atmospheric carbon dioxide, a main source of greenhouse gas, has been accentuated recently. One focus is capture, separation, and sequestration of industrial carbon dioxide. As a hydrate former, carbon dioxide forms hydrates at moderate temperatures and pressures. This phenomenon could be utilized to capture and separate carbon dioxide from flue gases, and also has the potential to sequester carbon dioxide in the deep seabeds. This research investigated the effect of synthetic surfactants on carbon dioxide hydrate formation, catalysis and consequently, methane production from hydrate permafrosts in sediments. It investigated the sequestration potential of carbon dioxide hydrates in ocean sediments. Also, the catalytic effect of biosurfactants in these processes was investigated.Keywords: carbon dioxide, hydrate, sequestration, surfactant
Procedia PDF Downloads 4281903 Empirical Evaluation of Gradient-Based Training Algorithms for Ordinary Differential Equation Networks
Authors: Martin K. Steiger, Lukas Heisler, Hans-Georg Brachtendorf
Abstract:
Deep neural networks and their variants form the backbone of many AI applications. Based on the so-called residual networks, a continuous formulation of such models as ordinary differential equations (ODEs) has proven advantageous since different techniques may be applied that significantly increase the learning speed and enable controlled trade-offs with the resulting error at the same time. For the evaluation of such models, high-performance numerical differential equation solvers are used, which also provide the gradients required for training. However, whether classical gradient-based methods are even applicable or which one yields the best results has not been discussed yet. This paper aims to redeem this situation by providing empirical results for different applications.Keywords: deep neural networks, gradient-based learning, image processing, ordinary differential equation networks
Procedia PDF Downloads 1541902 Evaluating the Dosimetric Performance for 3D Treatment Planning System for Wedged and Off-Axis Fields
Authors: Nashaat A. Deiab, Aida Radwan, Mohamed S. Yahiya, Mohamed Elnagdy, Rasha Moustafa
Abstract:
This study is to evaluate the dosimetric performance of our institution's 3D treatment planning system for wedged and off-axis 6MV photon beams, guided by the recommended QA tests documented in the AAPM TG53; NCS report 15 test packages, IAEA TRS 430 and ESTRO booklet no.7. The study was performed for Elekta Precise linear accelerator designed for clinical range of 4, 6 and 15 MV photon beams with asymmetric jaws and fully integrated multileaf collimator that enables high conformance to target with sharp field edges. Ten tests were applied on solid water equivalent phantom along with 2D array dose detection system. The calculated doses using 3D treatment planning system PrecisePLAN were compared with measured doses to make sure that the dose calculations are accurate for simple situations such as square and elongated fields, different SSD, beam modifiers e.g. wedges, blocks, MLC-shaped fields and asymmetric collimator settings. The QA results showed dosimetric accuracy of the TPS within the specified tolerance limits. Except for large elongated wedged field, the central axis and outside central axis have errors of 0.2% and 0.5%, respectively, and off- planned and off-axis elongated fields the region outside the central axis of the beam errors are 0.2% and 1.1%, respectively. The dosimetric investigated results yielded differences within the accepted tolerance level as recommended. Differences between dose values predicted by the TPS and measured values at the same point are the result from limitations of the dose calculation, uncertainties in the measurement procedure, or fluctuations in the output of the accelerator.Keywords: quality assurance, dose calculation, wedged fields, off-axis fields, 3D treatment planning system, photon beam
Procedia PDF Downloads 4341901 MIMIC: A Multi Input Micro-Influencers Classifier
Authors: Simone Leonardi, Luca Ardito
Abstract:
Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media
Procedia PDF Downloads 1741900 Clinical Impact of Ultra-Deep Versus Sanger Sequencing Detection of Minority Mutations on the HIV-1 Drug Resistance Genotype Interpretations after Virological Failure
Authors: S. Mohamed, D. Gonzalez, C. Sayada, P. Halfon
Abstract:
Drug resistance mutations are routinely detected using standard Sanger sequencing, which does not detect minor variants with a frequency below 20%. The impact of detecting minor variants generated by ultra-deep sequencing (UDS) on HIV drug-resistance (DR) interpretations has not yet been studied. Fifty HIV-1 patients who experienced virological failure were included in this retrospective study. The HIV-1 UDS protocol allowed the detection and quantification of HIV-1 protease and reverse transcriptase variants related to genotypes A, B, C, E, F, and G. DeepChek®-HIV simplified DR interpretation software was used to compare Sanger sequencing and UDS. The total time required for the UDS protocol was found to be approximately three times longer than Sanger sequencing with equivalent reagent costs. UDS detected all of the mutations found by population sequencing and identified additional resistance variants in all patients. An analysis of DR revealed a total of 643 and 224 clinically relevant mutations by UDS and Sanger sequencing, respectively. Three resistance mutations with > 20% prevalence were detected solely by UDS: A98S (23%), E138A (21%) and V179I (25%). A significant difference in the DR interpretations for 19 antiretroviral drugs was observed between the UDS and Sanger sequencing methods. Y181C and T215Y were the most frequent mutations associated with interpretation differences. A combination of UDS and DeepChek® software for the interpretation of DR results would help clinicians provide suitable treatments. A cut-off of 1% allowed a better characterisation of the viral population by identifying additional resistance mutations and improving the DR interpretation.Keywords: HIV-1, ultra-deep sequencing, Sanger sequencing, drug resistance
Procedia PDF Downloads 3261899 Measuring Human Perception and Negative Elements of Public Space Quality Using Deep Learning: A Case Study of Area within the Inner Road of Tianjin City
Authors: Jiaxin Shi, Kaifeng Hao, Qingfan An, Zeng Peng
Abstract:
Due to a lack of data sources and data processing techniques, it has always been difficult to quantify public space quality, which includes urban construction quality and how it is perceived by people, especially in large urban areas. This study proposes a quantitative research method based on the consideration of emotional health and physical health of the built environment. It highlights the low quality of public areas in Tianjin, China, where there are many negative elements. Deep learning technology is then used to measure how effectively people perceive urban areas. First, this work suggests a deep learning model that might simulate how people can perceive the quality of urban construction. Second, we perform semantic segmentation on street images to identify visual elements influencing scene perception. Finally, this study correlated the scene perception score with the proportion of visual elements to determine the surrounding environmental elements that influence scene perception. Using a small-scale labeled Tianjin street view data set based on transfer learning, this study trains five negative spatial discriminant models in order to explore the negative space distribution and quality improvement of urban streets. Then it uses all Tianjin street-level imagery to make predictions and calculate the proportion of negative space. Visualizing the spatial distribution of negative space along the Tianjin Inner Ring Road reveals that the negative elements are mainly found close to the five key districts. The map of Tianjin was combined with the experimental data to perform the visual analysis. Based on the emotional assessment, the distribution of negative materials, and the direction of street guidelines, we suggest guidance content and design strategy points of the negative phenomena in Tianjin street space in the two dimensions of perception and substance. This work demonstrates the utilization of deep learning techniques to understand how people appreciate high-quality urban construction, and it complements both theory and practice in urban planning. It illustrates the connection between human perception and the actual physical public space environment, allowing researchers to make urban interventions.Keywords: human perception, public space quality, deep learning, negative elements, street images
Procedia PDF Downloads 1061898 Behaviour of Rc Column under Biaxial Cyclic Loading-State of the Art
Authors: L. Pavithra, R. Sharmila, Shivani Sridhar
Abstract:
Columns severe structural damage needs proportioning a significant portion of earthquake energy can be dissipated yielding in the beams. Presence of axial load along with cyclic loading has a significant influence on column. The objective of this paper is to present the analytical results of columns subjected to biaxial cyclic loading.Keywords: RC column, Seismic behaviour, cyclic behaviour, biaxial testing, ductile behaviour
Procedia PDF Downloads 3561897 Speech Detection Model Based on Deep Neural Networks Classifier for Speech Emotions Recognition
Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov
Abstract:
Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset
Procedia PDF Downloads 921896 Face Recognition Using Body-Worn Camera: Dataset and Baseline Algorithms
Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani
Abstract:
Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.Keywords: face recognition, body-worn cameras, deep learning, person identification
Procedia PDF Downloads 1581895 Instance Segmentation of Wildfire Smoke Plumes using Mask-RCNN
Authors: Jamison Duckworth, Shankarachary Ragi
Abstract:
Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands
Procedia PDF Downloads 1201894 Searching the Relationship among Components that Contribute to Interactive Plight and Educational Execution
Authors: Shri Krishna Mishra
Abstract:
In an educational context, technology can prompt interactive plight only when it is used in conjunction with interactive plight methods. This study, therefore, examines the relationships among components that contribute to higher levels of interactive plight and execution, such as interactive Plight methods, technology, intrinsic motivation and deep learning. 526 students participated in this study. With structural equation modelling, the authors test the conceptual model and identify satisfactory model fit. The results indicate that interactive Plight methods, technology and intrinsic motivation have significant relationship with interactive Plight; deep learning mediates the relationships of the other variables with Execution.Keywords: searching the relationship among components, contribute to interactive plight, educational execution, intrinsic motivation
Procedia PDF Downloads 4431893 Progress in Combining Image Captioning and Visual Question Answering Tasks
Authors: Prathiksha Kamath, Pratibha Jamkhandi, Prateek Ghanti, Priyanshu Gupta, M. Lakshmi Neelima
Abstract:
Combining Image Captioning and Visual Question Answering (VQA) tasks have emerged as a new and exciting research area. The image captioning task involves generating a textual description that summarizes the content of the image. VQA aims to answer a natural language question about the image. Both these tasks include computer vision and natural language processing (NLP) and require a deep understanding of the content of the image and semantic relationship within the image and the ability to generate a response in natural language. There has been remarkable growth in both these tasks with rapid advancement in deep learning. In this paper, we present a comprehensive review of recent progress in combining image captioning and visual question-answering (VQA) tasks. We first discuss both image captioning and VQA tasks individually and then the various ways in which both these tasks can be integrated. We also analyze the challenges associated with these tasks and ways to overcome them. We finally discuss the various datasets and evaluation metrics used in these tasks. This paper concludes with the need for generating captions based on the context and captions that are able to answer the most likely asked questions about the image so as to aid the VQA task. Overall, this review highlights the significant progress made in combining image captioning and VQA, as well as the ongoing challenges and opportunities for further research in this exciting and rapidly evolving field, which has the potential to improve the performance of real-world applications such as autonomous vehicles, robotics, and image search.Keywords: image captioning, visual question answering, deep learning, natural language processing
Procedia PDF Downloads 661892 A Deep Learning Approach to Online Social Network Account Compromisation
Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang
Abstract:
The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.Keywords: computer security, network security, online social network, account compromisation
Procedia PDF Downloads 1081891 Feature Engineering Based Detection of Buffer Overflow Vulnerability in Source Code Using Deep Neural Networks
Authors: Mst Shapna Akter, Hossain Shahriar
Abstract:
One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.Keywords: cyber security, vulnerability detection, neural networks, feature extraction
Procedia PDF Downloads 811890 Understanding and Improving Neural Network Weight Initialization
Authors: Diego Aguirre, Olac Fuentes
Abstract:
In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques.Keywords: deep learning, image classification, supervised learning, weight initialization
Procedia PDF Downloads 1251889 Experimental Study on Stabilisation of a Soft Soil by Alkaline Activation of Industrial By-Products
Authors: Mohammadjavad Yaghoubi, Arul Arulrajah, Mahdi M. Disfani, Suksun Horpibulsuk, Myint W. Bo, Stephen P. Darmawan
Abstract:
Utilising waste materials, such as fly ash (FA) and slag (S) stockpiled in landfills, has drawn the attention of researchers and engineers in the recent years. There is a great potential for usage of these wastes in ground improvement projects, especially where deep deposits of soft compressible soils exist. This paper investigates the changes in the strength development of a high water content soft soil stabilised with alkaline activated FA and S, termed as geopolymer binder, to use in deep soil mixing technology. The strength improvement and the changes in the microstructure of the mixtures have been studied. The results show that using FA and S-based geopolymers can increases the strength significantly. Furthermore, utilising FA and S in ground improvement projects, where large amounts of binders are required, can be a solution to the disposal of these wastes.Keywords: alkaline activation, fly ash, geopolymer, slag, strength development
Procedia PDF Downloads 2591888 Visual Inspection of Road Conditions Using Deep Convolutional Neural Networks
Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas
Abstract:
This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems
Procedia PDF Downloads 125