Search results for: deep hole drilling

Authors: Xiaohui Wang, Zhichuan Guan, Roman Shor, Chuanbin Xu

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Early monitoring and real-time quantitative description of gas intrusion under the premise of ensuring the integrity of the drilling fluid circulation system will greatly improve the accuracy and effectiveness of deepwater gas-kick monitoring. Therefore, in order to study the propagation characteristics of ultrasonic waves in the gas-liquid two-phase flow within the marine riser, in this paper, a numerical simulation method of ultrasonic propagation in the annulus of the riser was established, and the credibility of the numerical analysis was verified by the experimental results of the established gas intrusion monitoring simulation experimental device. The numerical simulation can solve the sound field in the gas-liquid two-phase flow according to different physical models, and it is easier to realize the single factor control. The influence of each parameter on the received signal can be quantitatively investigated, and the law with practical guiding significance can be obtained.

Keywords: gas-kick detection, ultrasonic, void fraction, coda wave velocity

Procedia PDF Downloads 126

Authors: Chaima Babi, Said Gadri

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The rapid increase in fake news continues to grow at a very fast rate; this requires implementing efficient techniques that allow testing the re-liability of online content. For that, the current research strives to illuminate the fake news problem using deep learning DL and machine learning ML ap-proaches. We have developed the traditional LSTM (Long short-term memory), and the bidirectional BiLSTM model. A such process is to perform a training task on almost of samples of the dataset, validate the model on a subset called the test set to provide an unbiased evaluation of the final model fit on the training dataset, then compute the accuracy of detecting classifica-tion and comparing the results. For the programming stage, we used Tensor-Flow and Keras libraries on Python to support Graphical Processing Units (GPUs) that are being used for developing deep learning applications.

Keywords: machine learning, deep learning, natural language, fake news, Bi-LSTM, LSTM, multiclass classification

Procedia PDF Downloads 45

Authors: Nicola D. Cannon

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A range of field experiments have been conducted since 1991 to 2017 on organic land at the Royal Agricultural University’s Harnhill Manor Farm near Cirencester, UK to explore the impact of different management practices on weed infestation in organic winter and spring wheat. The experiments were designed using randomised complete block and some with split plot arrangements. Sowing date, variety choice, crop height and crop establishment technique have all shown a significant impact on weed infestations. Other techniques have also been investigated but with less clear, but, still often significant effects on weed control including grazing with sheep, undersowing with different legumes and mechanical weeding techniques. Tillage treatments included traditional plough based systems, minimum tillage and direct drilling. Direct drilling had significantly higher weed dry matter than the other two techniques. Taller wheat varieties which do not contain Rht1 or Rht2 had higher weed populations than the wheat without dwarfing genes. Early sown winter wheat had greater weed dry matter than later sown wheat. Grazing with sheep interacted strongly with sowing date, with shorter varieties and also late sowing dates providing much less forage but, grazing did reduce weed biomass in June. Undersowing had mixed impacts which were related to the success of establishment of the undersown legume crop. Weeds are most successfully controlled when a range of techniques are implemented to give the wheat crop the greatest chance of competing with weeds.

Keywords: crop establishment, drilling date, grazing, undersowing, varieties, weeds

Procedia PDF Downloads 159

Authors: A. Soualem

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The design of multi stage deep drawing processes requires the evaluation of many process parameters such as the intermediate die geometry, the blank shape, the sheet thickness, the blank holder force, friction, lubrication etc..These process parameters have to be determined for the optimum forming conditions before the process design. In general sheet metal forming may involve stretching drawing or various combinations of these basic modes of deformation. It is important to determine the influence of the process variables in the design of sheet metal working process. Especially, the punch and die corner for deep drawing will affect the formability. At the same time the prediction of sheet metals springback after deep drawing is an important issue to solve for the control of manufacturing processes. Nowadays, the importance of this problem increases because of the use of steel sheeting with high stress and also aluminum alloys. The aim of this paper is to give a better understanding of the springback and its effect in various sheet metals forming process such as expansion and restraint deep drawing in the cup drawing process, by varying radius die, lubricant for two commercially available materials e.g. galvanized steel and Aluminum sheet. To achieve these goals experiments were carried out and compared with other results. The original of our purpose consist on tests which are ensured by adapting a U-type stretching-bending device on a tensile testing machine, where we studied and quantified the variation of the springback.

Keywords: springback, deep drawing, expansion, restricted deep drawing

Procedia PDF Downloads 425

Authors: S. Sharma, Gaballa Aqeelah, Tawfig Alghbaili, Ali Elmessmari

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There were abrupt discontinuities in the Brute Stack in the northernmost locations during the acquisition of 2D (2007) and 3D (2021) seismic data in the northwest region of the Ghadames Basin, Libya. In both campaigns, complete fluid circulation loss was seen in these regions during up-hole drilling. Geophysics, sedimentology and shallow subsurface geology were all integrated to look into what was causing the seismic signal to disappear at shallow depths. The Upper Cretaceous Nalut Formation is the near-surface or surface formation in the studied area. It is distinguished by abnormally high resistivity in all the neighboring wells. The Nalut Formation in all the nearby wells from the present study and previous outcrop study suggests lithology of dolomite and chert/flint in nodular or layered forms. There are also reports of karstic caverns, vugs, and thick cracks, which all work together to produce the high resistivity. Four up-hole samples that were analyzed for microfacies revealed a near-coastal to tidal environment. Algal (Chara) infested deposits up to 30 feet thick and monotonous, very porous, are seen in two up-hole sediments; these deposits are interpreted to be scattered, continental algal travertine mounds. Chert/flint, dolomite, and calcite in varying amounts are confirmed by XRD analysis. Regional tracking of the high resistivity of the Nalut Formation, which is thought to be connected to the sea level drop that created the paleokarst layer, is possible. It is abruptly overlain by a blanket marine transgressive deposit caused by rapid sea level rise, which is a regional, relatively high radioactive layer of argillaceous limestone. The examined area's close proximity to the mountainous, E-W trending ridges of northern Libya made it easier for recent freshwater circulation, which later enhanced cavern development and mineralization in the paleokarst layer. Seismic signal loss at shallow depth is caused by extremely heterogeneous mineralogy of pore- filling or lack thereof. Scattering effect of shallow karstic layer on seismic signal has been well documented. Higher velocity inflection points at shallower depths in the northern part and deeper intervals in the southern part, in both cases at Nalut level, demonstrate the layer's influence on the seismic signal. During the Permian-Carboniferous, the Ghadames Basin underwent uplift and extensive erosion, which resulted in this karstic layer of the Nalut Formation uplifted to a shallow depth in the northern part of the studied area weakening the acoustic signal, whereas in the southern part of the 3D acquisition area the Nalut Formation remained at the deeper interval without affecting the seismic signal. Results from actions taken during seismic processing to deal with this signal loss are visible and have improved. This study recommends using denser spacing or dynamite to circumvent the karst layer in a comparable geographic area in order to prevent signal loss at lesser depths.

Keywords: well logging, seismic data acquisition, sesimic data processing, up-holes

Procedia PDF Downloads 51

Authors: Sumaiya Amir Khan, Shayma Al-Sharif, Samiha Mazher, Neha Intikhab Khan

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This research paper aims to evaluate the effectiveness of sleep-tracking AI applications in smart-watches. It focuses on comparing the sleep analyses of two different smartwatch brands, Samsung and Fitbit, and measuring sleep at three different stages – REM (Rapid-Eye-Movement), NREM (Non-Rapid-Eye-Movement), and deep sleep. The methodology involves the participation of different users and analyzing their sleep data. The results reveal that although light sleep is the longest stage, deep sleep is higher than average in the participants. The study also suggests that light sleep is not uniform, and getting higher levels of deep sleep can prevent debilitating health conditions. Based on the findings, it is recommended that individuals should aim to achieve higher levels of deep sleep to maintain good health. Overall, this research contributes to the growing literature on the effectiveness of sleep-tracking AI applications and their potential to improve sleep quality.

Keywords: sleep tracking, lifestyle, accuracy, health, AI, AI features, ML

Procedia PDF Downloads 47

Authors: Shi-Uk Lee, Chae Young Lim

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Objective: The aim of this study was to evaluate the changes of lumbar deep muscle thickness and cross-sectional area using ultrasonography with magnetic stimulation. Methods: To evaluate the changes of lumbar deep muscle by using magnetic stimulation, 12 healthy volunteers (39.6±10.0 yrs) without low back pain during 3 months participated in this study. All the participants were checked with X-ray and electrophysiologic study to confirm that they had no problems with their back. Magnetic stimulation was done on the L5 and S1 root with figure-eight coil as previous study. To confirm the proper motor root stimulation, the surface electrode was put on the tibialis anterior (L5) and abductor hallucis muscles (S1) and the hot spots of magnetic stimulation were found with 50% of maximal magnetic stimulation and determined the stimulation threshold lowering the magnetic intensity by 5%. Ultrasonography was used to assess the changes of L5 and S1 lumbar multifidus (superficial and deep) cross-sectional area and thickness with maximal magnetic stimulation. Cross-sectional area (CSA) and thickness was evaluated with image acquisition program, ImageJ software (National Institute of Healthy, USA). Wilcoxon signed-rank was used to compare outcomes between before and after stimulations. Results: The mean minimal threshold was 29.6±3.8% of maximal stimulation intensity. With minimal magnetic stimulation, thickness of L5 and S1 deep multifidus (DM) were increased from 1.25±0.20, 1.42±0.23 cm to 1.40±0.27, 1.56±0.34 cm, respectively (P=0.005, P=0.003). CSA of L5 and S1 DM were also increased from 2.26±0.18, 1.40±0.26 cm2 to 2.37±0.18, 1.56±0.34 cm2, respectively (P=0.002, P=0.002). However, thickness of L5 and S1 superficial multifidus (SM) were not changed from 1.92±0.21, 2.04±0.20 cm to 1.91±0.33, 1.96±0.33 cm (P=0.211, P=0.199) and CSA of L5 and S1 were also not changed from 4.29±0.53, 5.48±0.32 cm2 to 4.42±0.42, 5.64±0.38 cm2. With maximal magnetic stimulation, thickness of L5, S1 of DM and SM were increased (L5 DM, 1.29±0.26, 1.46±0.27 cm, P=0.028; L5 SM, 2.01±0.42, 2.24±0.39 cm, P=0.005; S1 DM, 1.29±0.19, 1.67±0.29 P=0.002; S1 SM, 1.90±0.36, 2.30±0.36, P=0.002). CSA of L5, S1 of DM and SM were also increased (all P values were 0.002). Conclusions: Deep lumbar muscles could be stimulated with lumbar motor root magnetic stimulation. With minimal stimulation, thickness and CSA of lumbosacral deep multifidus were increased in this study. Further studies are needed to confirm whether the similar results in chronic low back pain patients are represented. Lumbar magnetic stimulation might have strengthening effect of deep lumbar muscles with no discomfort.

Keywords: magnetic stimulation, lumbar multifidus, strengthening, ultrasonography

Procedia PDF Downloads 334

Authors: Mingmei Zhang, Xinyong Li

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Novel AgInS2/SnS2/RGO (AISR) heterojunctions photocatalysts were synthesized by simple hydrothermal method. The morphology and composition of the fabricated AISR nanocomposites were investigated by field-emission scanning electron microscopy (SEM), X-ray diffraction (XRD), high resolution transmission electron microscopy (HRTEM) and X-ray photoelectron spectroscopy (XPS). Moreover, the as-prepared AISR photocatalysts exhibited excellent photocatalytic activities for the degradation of Norfloxacin (NOR), mainly due to its high optical absorption and separation efficiency of photogenerated electron-hole pairs, as evidenced by UV–vis diffusion reflection spectra (DRS) and Surface photovoltage (SPV) spectra. Furthermore, laser flash photolysis technique was conducted to test the lifetime of charge carriers of the fabricated nanocomposites. The interfacial charges transfer mechanism was also discussed.

Keywords: AISR heterojunctions, electron-hole pairs, SPV spectra, charges transfer mechanism

Procedia PDF Downloads 146

Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

Procedia PDF Downloads 132

Authors: Yan Bachtiar Muslih, Hangga Wijaya, Trio Fani, Putri Agustin

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Demand of Energy in Indonesia always increases 5-6% a year, but production of conventional energy always decreases 3-5% a year, it means that conventional energy in 20-40 years ahead will not able to complete all energy demand in Indonesia, one of the solve way is using unconventional energy that is gas hydrate, gas hydrate is gas that form by biogenic process, gas hydrate stable in condition with extremely depth and low temperature, gas hydrate can form in two condition that is in pole condition and in deep-sea condition, wherein this research will focus in gas hydrate that association with methane form methane hydrate in deep-sea condition and usually form in depth between 150-2000 m, this research will focus in process of methane hydrate formation that is biogenic process and the important role of deep-sea sediment so can produce accumulation of methane hydrate, methane hydrate usually will be accumulated in find sediment in deep-sea environment with condition high-pressure and low-temperature this condition too usually make methane hydrate change into white nodule, methodology of this research is geology field work and laboratory analysis, from geology field work will get sample data consist of 10-15 samples from Kerek Formation outcrops as random for imagine the condition of deep-sea environment that influence the methane hydrate formation and also from geology field work will get data of measuring stratigraphy in outcrops Kerek Formation too from this data will help to imagine the process in deep-sea sediment like energy flow, supply sediment, and etc, and laboratory analysis is activity to analyze all data that get from geology field work, the result of this research can used to exploration activity of methane hydrate in another prospect deep-sea environment in Indonesia.

Keywords: methane hydrate, deep-sea sediment, kerek formation, sub-basin of kendeng, central java, Indonesia

Procedia PDF Downloads 441

Authors: Cahit Perkgöz

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Artificial intelligence has brought new approaches to solving problems in almost every research field in recent years. One of these topics is shape design and optimization, which has the possibility of applications in many fields, such as nanotechnology and electronics. A properly constructed cost function can eliminate the need for labeled data required in deep learning and create desired shapes. In this work, the network parameters are optimized differentially, which differs from traditional approaches. The methods are tested for physics-related structures and successful results are obtained. This work is supported by Eskişehir Technical University scientific research project (Project No: 20ADP090)

Keywords: deep learning, shape design, optimization, artificial intelligence

Procedia PDF Downloads 125

Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

Procedia PDF Downloads 178

Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

Procedia PDF Downloads 28

Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

Procedia PDF Downloads 147

Authors: Farshad Zeinali, Sajedeh Norouzi, Nader Mokari, Eduard Jorswieck

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The capacity of fifth-generation (5G) vehicle-to-everything (V2X) networks poses significant challenges. To ad- dress this challenge, this paper utilizes New Radio (NR) and New Radio Unlicensed (NR-U) networks to develop a heterogeneous vehicular network (HetNet). We propose a new framework, named joint BS assignment and resource allocation (JBSRA) for mobile V2X users and also consider coexistence schemes based on flexible duty cycle (DC) mechanism for unlicensed bands. Our objective is to maximize the average throughput of vehicles while guaranteeing the WiFi users' throughput. In simulations based on deep reinforcement learning (DRL) algorithms such as deep deterministic policy gradient (DDPG) and deep Q network (DQN), our proposed framework outperforms existing solutions that rely on fixed DC or schemes without consideration of unlicensed bands.

Keywords: vehicle-to-everything (V2X), resource allocation, BS assignment, new radio (NR), new radio unlicensed (NR-U), coexistence NR-U and WiFi, deep deterministic policy gradient (DDPG), deep Q-network (DQN), joint BS assignment and resource allocation (JBSRA), duty cycle mechanism

Procedia PDF Downloads 68

Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

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We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

Procedia PDF Downloads 42

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

Procedia PDF Downloads 231

Authors: Boris Kolev, Matt Kokan, Mohammad Deriszadeh, Farshid Bateni

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Due to the increasing demand for real estate and the need for efficient land utilization in Greater Vancouver, developers have been increasingly considering the construction of high-rise structures with multiple below-grade parking. The temporary excavations required to allow for the construction of underground levels have recently reached up to 40 meters in depth. One of the challenges with deep excavations is the prediction of wall displacements and ground settlements due to their effect on the integrity of City utilities, infrastructure, and adjacent buildings. A large database of survey monitoring data has been collected for deep excavations in various soil conditions and shoring systems. The majority of the data collected is for tie-back anchors and shotcrete lagging systems. The data were categorized, analyzed and the results were evaluated to find a relationship between the most dominant parameters controlling the displacement, such as depth of excavation, soil properties, and the tie-back anchor loading and arrangement. For a select number of deep excavations, finite element modeling was considered for analyses. The lateral displacements from the simulation results were compared to the recorded survey monitoring data. The study concludes with a discussion and comparison of the available empirical and numerical modeling methodologies for evaluating lateral displacements in deep excavations.

Keywords: deep excavations, lateral displacements, numerical modeling, shoring walls, tieback anchors

Procedia PDF Downloads 149

Authors: Mohammed Daher Albalwi

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The transcritical flow of a stratified fluid over an obstacle for negative forcing amplitude (hole) that generation upstream and downstream, connected by an unsteady solution, is examined. In the weakly nonlinear, weakly dispersive regime, the problem is formulated in the forced Korteweg-de Vries–Burgers framework. This is done by including the influence of the viscosity of the fluid beyond the Korteweg–de Vries approximation. The results show that the influence of viscosity is crucial in determining various wave properties, including the amplitudes of solitary waves in the upstream and downstream directions, as well as the widths of the bores. We focused here on weak damping, and the results are presented for transcritical, supercritical, and subcritical flows. In general, the outcomes are not qualitatively similar to those from the forced Korteweg-de–Vries equation when the value of the viscous is small, interesting differences emerge as the magnitude of the value of viscous increases.

Keywords: Korteweg–de Vries–Burgers equation, soliton, transcritical flow, viscous flow

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Authors: Shervin Alaei, Reza Moradi

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Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends.

Keywords: deep learning, LSTM, trend prediction, risk management, artificial neural networks

Procedia PDF Downloads 113

Authors: Selvavinayagan Babiharan

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This research aims to develop a deep learning platform to enhance mathematics education for secondary students in Sri Lanka. The platform will be designed to incorporate interactive and user-friendly features to engage students in active learning and promote their mathematical skills. The proposed platform will be developed using TensorFlow and Keras, two widely used deep learning frameworks. The system will be trained on a large dataset of math problems, which will be collected from Sri Lankan school curricula. The results of this research will contribute to the improvement of mathematics education in Sri Lanka and provide a valuable tool for teachers to enhance the learning experience of their students.

Keywords: information technology, education, machine learning, mathematics

Procedia PDF Downloads 56

Authors: Saeed Chamehsara, Seyed Mostafa Mirsalim, Mehdi Tajdari

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In this study, the effect of fuel injection discharge curve and injection pressure simultaneously for upgrading power of heavy duty diesel engine by simulation of combustion process in AVL-Fire software are discussed. Hence, the fuel injection discharge curve was changed from semi-triangular to rectangular which is usual in common rail fuel injection system. Injection pressure with respect to amount of injected fuel and nozzle hole diameter are changed. Injection pressure is calculated by an experimental equation which is for heavy duty diesel engines with common rail fuel injection system. Upgrading power for 1000 and 2000 bar injection pressure are discussed. For 1000 bar injection pressure with 188 mg injected fuel and 3 mm nozzle hole diameter in compare with first state which is semi-triangular discharge curve with 139 mg injected fuel and 3 mm nozzle hole diameter, upgrading power is about 19% whereas the special change has not been observed in cylinder pressure. On the other hand, both the NOX emission and the Soot emission decreased about 30% and 6% respectively. Compared with first state, for 2000 bar injection pressure that injected fuel and nozzle diameter are 196 mg and 2.6 mm respectively, upgrading power is about 22% whereas cylinder pressure has been fixed and NOX emission and the Soot emissions are decreased 36% and 20%, respectively.

Keywords: CFD simulation, HD diesel engine, upgrading power, injection pressure, fuel injection discharge curve, combustion process

Procedia PDF Downloads 488

Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

Procedia PDF Downloads 93

Authors: Donghee Noh, Seonhyeong Kim, Junhwan Choi, Heegon Kim, Sooho Jung, Keunho Park

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In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest.

Keywords: aerial image, image process, machine vision, open field smart farm, segmentation

Procedia PDF Downloads 46

Authors: Han Ik Park, Jeong Hyeon Seong, Dong Seop Han, Su-Chul Shin, Young Chul Park

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A Spudcan is mounted on the lower leg of the jack-up rig, a device for preventing a rollover of a structure and to support the structure in a stable sea floor. At the time of inserting the surface of the spud can to penetrate when the sand layer is stable and smoothly pulled to the clay layer, and at that time of recovery when uploading the spud can is equipped with a water injection device. In this study, it is significant to optimize the shape of pipelines holes for water injection device and it was set in two kinds of shape, the oval and round. Interpretation of the subject into the site of Gulf of Mexico offshore Wind Turbine Installation Vessels (WTIV)was chosen as a target platform. Using the ANSYS Workbench commercial programs, optimal design was conducted. The results of this study can be applied to the hole-shaped design of various marine structures.

Keywords: kriging method, jack-up rig, shape optimization, spudcan

Procedia PDF Downloads 476

Authors: Edgardo V. Gubatanga Jr., Mark Joshua Salvacion

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Most aerial LiDAR systems have accompanying aerial cameras in order to capture not only the terrain of the surveyed area but also its true-color appearance. However, the presence of atmospheric clouds, poor lighting conditions, and aerial camera problems during an aerial survey may cause absence of aerial photographs. These leave areas having terrain information but lacking aerial photographs. Intensity images can be derived from LiDAR data but they are only grayscale images. A deep learning model is developed to create a complex function in a form of a deep neural network relating the pixel values of LiDAR-derived intensity images and true-color images. This complex function can then be used to predict the true-color images of a certain area using intensity images from LiDAR data. The predicted true-color images do not necessarily need to be accurate compared to the real world. They are only intended to look realistic so that they can be used as base maps.

Keywords: aerial LiDAR, colorization, deep learning, intensity images

Procedia PDF Downloads 130

Authors: Youcef Faci, Ahmed Mebtouche, Badredine Maalem

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n the present work, an experimental study is carried out using acoustic emission and DIC techniques to analyze the damage of open hole woven composite carbon/epoxy under solicitations. Damage mechanisms were identified based on acoustic emission parameters such as amplitude, energy, and cumulative account. The findings of the AE measurement were successfully identified by digital image correlation (DIC) measurements. The evolution value of bolt angle inclination during tensile tests was studied and analyzed. Consequently, the relationship between the bolt inclination angles during tensile tests associated with failure modes of fastened joints of composite materials is determined. Moreover, there is an interaction between laminate pattern, laminate thickness, fastener size and type, surface strain concentrations, and out-of-plane displacement. Conclusions are supported by microscopic visualizations of the composite specimen.

Keywords: tensile test, damage, acoustic emission, digital image correlation

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Authors: Nabil Shawkat, Jamil Saquer

Abstract:

Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

Procedia PDF Downloads 102

Authors: Shafique Qadir Memon, Muhammad Safar Mirjat, Abdul Quadir Mughal, Nadeem Amjad

Abstract:

The aims of this study were to determine direct input energy and indirect energy in maize production, to evaluate the inputs energy consumption and outputs energy gained for maize production in Islamabad, Pakistan for spring 2013. Results showed that grain yield was maximum under deep tillage as compared to conventional and zero tillage. Total energy input/output were maximum in deep tillage as compared to conventional tillage while lowest in zero tillage, net energy gain were found maximum under deep tillage.

Keywords: tillage, energy, grain yield, net energy gain

Procedia PDF Downloads 433

Authors: G. Blugan, B. Jiang, J. Thornberry, P. Sturzenegger, U. Gonzenbach, M. Misson, D. Cartlidge, R. Stenerud, J. Kuebler

Abstract:

Low-cost ceramic spheres were developed and manufactured from the engineering ceramic aluminium oxide. Hollow spheres of 50 mm diameter with a wall thickness of 0.5-1.0 mm were produced via an adapted slip casting technique. It was possible to produce the spheres with good repeatability and with no defects or failures in the spheres due to the manufacturing process. The spheres were developed specifically for use in buoyancy devices for deep-sea exploration conditions at depths of 3000 m below sea level. The spheres with a 1.0 mm wall thickness exhibit a buoyancy of over 54% while the spheres with a 0.5 mm wall thickness exhibit a buoyancy of over 73%. The mechanical performance of the spheres was confirmed by performing a hydraulic burst pressure test on individual spheres. With a safety factor of 3, all spheres with 1.0 mm wall thickness survived a hydraulic pressure of greater than 150 MPa which is equivalent to a depth of more than 5000 m below sea level. The spheres were then incorporated into a buoyancy module. These hollow aluminium oxide ceramic spheres offer an excellent possibility of deep-sea exploration to depths greater than the currently used technology.

Keywords: buoyancy, ceramic spheres, deep-sea, oil exploration

Procedia PDF Downloads 392