Search results for: openings in deep beams

Authors: Salah R. Al Zaidee, Ali S. Mahdi

Abstract:

Except for simple problems of statically determinate structures, optimum design problems in structural engineering have implicit objective functions where structural analysis and design are essential within each searching loop. With these implicit functions, the structural engineer is usually enforced to write his/her own computer code for analysis, design, and searching for optimum design among many feasible candidates and cannot take advantage of available software for structural analysis, design, and searching for the optimum solution. The meta-model is a regression model used to transform an implicit objective function into objective one and leads in turn to decouple the structural analysis and design processes from the optimum searching process. With the meta-model, well-known software for structural analysis and design can be used in sequence with optimum searching software. In this paper, the meta-model has been used to develop an explicit objective function for plane steel frames subjected to dead, live, and seismic forces. Frame topology is assumed as predefined based on architectural and functional requirements. Columns and beams sections and different connections details are the main design variables in this study. Columns and beams are grouped to reduce the number of design variables and to make the problem similar to that adopted in engineering practice. Data for the implicit objective function have been generated based on analysis and assessment for many design proposals with CSI SAP software. These data have been used later in SPSS software to develop a pure quadratic nonlinear regression model for the explicit objective function. Good correlations with a coefficient, R², in the range from 0.88 to 0.99 have been noted between the original implicit functions and the corresponding explicit functions generated with meta-model.

Keywords: meta-modal, objective function, steel frames, seismic analysis, design

Procedia PDF Downloads 227

Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

Abstract:

This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

Procedia PDF Downloads 123

Authors: Jonathan Sohn

Abstract:

Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods.

Keywords: deep neural network, adversarial attack, adversarial defense, adversarial machine learning

Procedia PDF Downloads 177

Authors: Mojebullah Wahidy, Murat Olgun

Abstract:

In this study, four different types of cement are used to investigate the permeability of DMC (Deep Mixing Column) in the clay. The clay used in this research is in the kaolin group, and the types of cement are; CEM I 42.5.R. normal portland cement, CEM II/A-M (P-L) pozzolan doped cement, CEM III/A 42.5 N blast furnace slag cement and DMFC-800 fine-grained portland cement. Firstly, some rheological tests are done on every cement, and a 0.9 water/cement ratio is selected as the appropriate ratio. This ratio is used to prepare the small-scale DMCs for all types of cement with %6, %9, %12, and %15, which are determined as the dry weight of the clay. For all the types of cement, three samples were prepared in every percentage and were kept on curing for 7, 14, and 28 days for permeability tests. As a result of the small-scale DMCs, permeability tests, a %12 selected for big-scale DMCs. A total of five big scales DMC were prepared by using a %12-cement and were kept for 28 days curing for permeability tests. The results of the permeability tests show that by increasing the cement percentage and curing time of all DMCs, the permeability coefficient (k) is decreased. Despite variable results in different cement ratios and curing time in general, samples treated by DMFC-800 fine-grained cement have the lowest permeability coefficient. Samples treated with CEM II and CEM I cement types were the second and third lowest permeable samples. The highest permeability coefficient belongs to the samples that were treated with CEM III cement type.

Keywords: deep mixing column, rheological test, DMFC-800, permeability test

Procedia PDF Downloads 57

Authors: Jui-Chien Hsieh

Abstract:

Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

Procedia PDF Downloads 102

Authors: Swapnil A. Padvi, Dipak S. Dalal

Abstract:

The chemistry of tetrazoles has been grown tremendously in the past few years because tetrazoles are important and useful class of heterocyclic compounds which have a widespread application such as anticancer, antimicrobial, analgesics, antibacterial, antifungal, antihypertensive, and anti-allergic drugs in medicinal chemistry. Furthermore, tetrazoles have application in material sciences as explosives, rocket propellants, and in information recording systems. In addition to this, they have a wide range of application in coordination chemistry as a ligand. Deep eutectic solvents (DES) have emerged over the current decade as a novel class of green reaction media and applied in various fields of sciences because of their unique physical and chemical properties similar to the ionic liquids such as low vapor pressure, non-volatility, high thermal stability and recyclability. In addition, the reactants of DES are cheaply available, low-toxic, and biodegradable, which makes them predominantly required for large-scale applications effectively in industrial production. Herein we report the [2+3] cycloaddition reaction of organic nitriles with sodium azide affords the corresponding 5-substituted 1H-tetrazoles in six different types of choline chloride based deep eutectic solvents under mild reaction condition. Choline chloride: ZnCl2 (1:2) showed the best results for the synthesis of 5-substituted 1 H-tetrazoles. This method reduces the disadvantages such as: the use of toxic metals and expensive reagents, drastic reaction conditions and the presence of dangerous hydrazoic acid. The approach provides environment-friendly, short reaction times, good to excellent yields; safe process and simple workup make this method an attractive and useful contribution to present green organic synthesis of 5-substituted-1H-tetrazoles. All synthesized compounds were characterized by IR, 1H NMR, 13C NMR and Mass spectroscopy. DES can be recovered and reused three times with very little loss in activity.

Keywords: click chemistry, choline chloride, green chemistry, deep eutectic solvent, tetrazoles

Procedia PDF Downloads 214

Authors: Richard O’Riordan, Saritha Unnikrishnan

Abstract:

Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

Procedia PDF Downloads 83

Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

Abstract:

Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

Procedia PDF Downloads 42

Authors: Michael Wright, Tiago Costa

Abstract:

The construction industry in SE Asia has been relying on S355 MPa “as rolled” H-beams for many years now. It is an easily sourced, metallurgically simple, reliable product that all designers, fabricators and constructors are familiar with. However, as the Global demand to better use our finite resources gets stronger, the need for an as-rolled S460 MPa H-Beam is becoming more apparent. The Financial benefits of an “as-rolled” S460 MPa H-beam are obvious. The S460 MPa beam which is currently available and used is fabricated from rolled strip. However, making H-beam from 3 x 460 MPa strips requires costly equipment, valuable welding skills & production time, all of which can be in short supply or better used for other purposes. The Metallurgical benefits of an “as-rolled” S460 MPa H-beam are consistency in the product. Fabricated H-beams have inhomogeneous areas where the strips have been welded together - parent metal, heat affected zone and weld metal all in the one body. They also rely heavily on the skill of the welder to guarantee a perfect, defect free weld. If this does not occur, the beam is intrinsically flawed and could lead to failure in service. An as-rolled beam is a relatively homogenous product, with the optimum strength and ductility produced by delivering steel with as fine as possible uniform cross sectional grain size. This is done by cost effective alloy design coupled with proper metallurgical process control implemented into an existing mill’s equipment capability and layout. This paper is designed to highlight the benefits of bring an “as-rolled” S460 MPa H-beam to the construction market place in SE Asia, and hopefully encourage the current “as-rolled” H-beam producers to rise to the challenge and produce an innovative high quality product for the local market.

Keywords: fine grained, As-rolled, long products, process control, metallurgy

Procedia PDF Downloads 285

Authors: Yong Chen

Abstract:

To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

Procedia PDF Downloads 107

Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

Abstract:

Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

Procedia PDF Downloads 144

Authors: A. Degale Desta, Cheng Jian

Abstract:

Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

Procedia PDF Downloads 143

Authors: Majid Bagherinia

Abstract:

Lime and cement are frequently used as binders in the Deep Mixing Method (DMM) to improve soft clay soils. The most significant disadvantages of these materials are carbon dioxide emissions and the consumption of natural resources. In this study, three different biopolymers, guar gum, locust bean gum, and sodium alginate, were investigated for the improvement of soft clay using DMM. In the experimental study, the effects of the additive ratio and curing time on the Unconfined Compressive Strength (UCS) of stabilized specimens were investigated. According to the results, the UCS values of the specimens increased as the additive ratio and curing time increased. The most effective additive was sodium alginate, and the highest strength was obtained after 28 days.

Keywords: deep mixing method, soft clays, ground improvement, biopolymers, unconfined compressive strength

Procedia PDF Downloads 57

Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

Abstract:

Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

Procedia PDF Downloads 97

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

Abstract:

Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

Procedia PDF Downloads 125

Authors: Mojtaba Barzegar, Alireza Shirazi, Saied Rabi Mahdavi

Abstract:

Intraoperative radiation therapy (IORT) is an innovative treatment modality that the delivery of a large single dose of radiation to the tumor bed during the surgery. The radiotherapy success depends on the absorbed dose delivered to the tumor. The achievement better accuracy in patient treatment depends upon the measured dose by standard dosimeter such as ionization chamber, but because of the high density of electric charge/pulse produced by the accelerator in the ionization chamber volume, the standard correction factor for ion recombination Ksat calculated with the classic two-voltage method is overestimated so the use of dose/pulse independent dosimeters such as chemical Fricke and ethanol chlorobenzene (ECB) dosimeters have been suggested. Dose measurement is usually calculated and calibrated in the Zmax. Ksat calculated by comparison of ion chamber response and ECB dosimeter at each applicator degree, size, and dose. The relative output factors for IORT applicators have been calculated and compared with experimentally determined values and the results simulated by Monte Carlo software. The absorbed doses have been calculated and measured with statistical uncertainties less than 0.7% and 2.5% consecutively. The relative differences between calculated and measured OF’s were up to 2.5%, for major OF’s the agreement was better. In these conditions, together with the relative absorbed dose calculations, the OF’s could be considered as an indication that the IORT electron beams have been well simulated. These investigations demonstrate the utility of the full Monte Carlo simulation of accelerator head with ECB dosimeter allow us to obtain detailed information of clinical IORT beams.

Keywords: intra operative radiotherapy, ethanol chlorobenzene, ksat, output factor, monte carlo simulation

Procedia PDF Downloads 461

Authors: Mohammad R. Jafari, Francois G. Bernardeau

Abstract:

With its arid climate, Qatar experiences low annual rainfall, intense storms, and high evaporation rates. However, the fast-paced rate of infrastructure development in the capital city of Doha has led to recurring instances of surface water flooding as well as rising groundwater levels. Public Work Authority (PWA/ASHGHAL) has implemented an approach to collect and discharge the flood water into a) positive gravity systems; b) Emergency Flooding Area (EFA) – Evaporation, Infiltration or Storage off-site using tankers; and c) Discharge to deep injection wells. As part of the flood prevention scheme, 21 deep injection wells have been constructed to discharge the collected surface and groundwater table in Doha city. These injection wells function as an alternative in localities that do not possess either positive gravity systems or downstream networks that can accommodate additional loads. These injection wells are 400-m deep and are constructed in a complex karstic subsurface condition with large cavities. The injection well system will discharge collected groundwater and storm surface runoff into the permeable Umm Er Radhuma Formation, which is an aquifer present throughout the Persian Gulf Region. The Umm Er Radhuma formation contains saline water that is not being used for water supply. The injection zone is separated by an impervious gypsum formation which acts as a barrier between upper and lower aquifer. State of the art drilling, grouting, and geophysical techniques have been implemented in construction of the wells to assure that the shallow aquifer would not be contaminated and impacted by injected water. Injection and pumping tests were performed to evaluate injection well functionality (injectability). The results of these tests indicated that majority of the wells can accept injection rate of 200 to 300 m³ /h (56 to 83 l/s) under gravity with average value of 250 m³ /h (70 l/s) compared to design value of 50 l/s. This paper presents design and construction process and issues associated with these injection wells, performing injection/pumping tests to determine capacity and effectiveness of the injection wells, the detailed design of collection system and conveying system into the injection wells, and the operation and maintenance process. This system is completed now and is under operation, and therefore, construction of injection wells is an effective option for flood control.

Keywords: deep injection well, flood prevention scheme, geophysical tests, pumping and injection tests, wellhead assembly

Procedia PDF Downloads 104

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 84

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 61

Authors: Seyed Ali Mahdipour, Maryam Shafeei Sarvestani

Abstract:

Radiation pressure acceleration (RPA) and target normal sheath acceleration (TNSA) are the most important methods of Laser-accelerated proton beams (LAP) planning systems.LAP has inspired novel applications that can benefit from proton bunch properties different from conventionally accelerated proton beams. The secondary neutron and photon produced in the collision of protons with beamline components are of the important concern in proton therapy. Various published Monte Carlo researches evaluated the beamline and shielding considerations for TNSA method, but there is no studies directly address secondary neutron and photon production from RPA method in LAP. The purpose of this study is to calculate the flux distribution of neutron and photon secondary radiations on the first area ofLAP and to determine the optimize thickness and radius of the energyselector in a LAP planning system based on RPA method. Also, we present the Monte Carlo calculations to determine the appropriate beam pipe for shielding a LAP planning system. The GEANT4 Monte Carlo toolkit has been used to simulate a secondary radiation production in LAP. A section of new multifunctional LAP beamlinehas been proposed, based on the pulsed power solenoid scheme as a GEANT4 toolkit. The results show that the energy selector is the most important source of neutron and photon secondary particles in LAP beamline. According to the calculations, the pure Tungsten energy selector not be the proper case, and using of Tungsten+Polyethylene or Tungsten+Graphitecomposite selectors will reduce the production of neutron and photon intensities by approximately ~10% and ~25%, respectively. Also the optimal radiuses of energy selectors were found to be ~4 cm and ~6 cm for a 3 degree and 5 degree proton deviation angles, respectively.

Keywords: neutron, photon, flux distribution, energy selector, GEANT4 toolkit

Procedia PDF Downloads 87

Authors: R. Al-Dujele, K. A. Cashell

Abstract:

A tubular flange girder is an I-shaped steel girder with either one of both of the usual flat flange plates replaced with a hollow section. Typically, these hollow sections are either rectangular or circular in shape. Concrete filled tubular flange girders (CFTFGs) are unconventional I-shaped beams that use a hollow structural section as the top flange which is filled with concrete. The resulting section offers very high lateral torsional buckling strength and stiffness compared with conventional steel I-beams of similar depth, width and weight, typically leading to a reduction in lateral bracing requirements. This paper is focussed on investigating the ultimate capacity of concrete filled rectangular tubular flange girders (CFRTFGs). These are complex members and their behaviour is governed by a number of inter-related parameters. The FE model is developed using ABAQUS software, 3-D finite element (FE) model for simply supported CFRTFGs subjected to two point loads applied at the third-span points is built. An initial geometrical imperfection of (L/1000), as well as geometrical and material nonlinearities, are introduced into the model, where L denotes the span of the girder. In this numerical model, the concrete and steel materials are modelled using eight-node solid and four-node shell elements, respectively. In addition to the FE model, simplified analytical expressions for the flexural capacity are also proposed, and the results are compared to those from the FE analyses. The analytical expressions, which are suitable for design, are also shown to be capable of providing an accurate depiction of the bending moment capacity.

Keywords: concrete-filled rectangular tubular flange girders, ultimate capacity, confining effect, finite element analysis

Procedia PDF Downloads 131

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 470

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 approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

Procedia PDF Downloads 119

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 89

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 80

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 81

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 205

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 207

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 418

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 144