Search results for: deep plane

Authors: Padmalochan Panda, R. Ramaseshan

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Aluminum nitride (AlN) is a potential candidate for semiconductor industry due to its wide band gap (6.2 eV), high thermal conductivity and low thermal coefficient of expansion. A-plane oriented AlN film finds an important role in deep UV-LED with higher isotropic light extraction efficiency. Also, Cr-doped AlN films exhibit dilute magnetic semiconductor property with high Curie temperature (300 K), and thus compatible with modern day microelectronics. In this work, highly a-axis oriented wurtzite AlN and Al1-xMxN (M = Cr, Ti) films have synthesized by reactive co-sputtering technique at different concentration. Crystal structure of these films is studied by Grazing incidence X-ray diffraction (GIXRD) and Transmission electron microscopy (TEM). Identification of binding energy and concentration (x) in these films is carried out by X-ray photoelectron spectroscopy (XPS). Local crystal structure around the Cr and Ti atom of these films are investigated by X-ray absorption spectroscopy (XAS). It is found that Cr and Ti replace the Al atom in AlN lattice and the bond lengths in first and second coordination sphere with N and Al, respectively, decrease concerning doping concentration due to strong p-d hybridization. The nano-indentation hardness of Cr and Ti-doped AlN films seems to increase from 17.5 GPa (AlN) to around 23 and 27.5 GPa, respectively. An-isotropic optical properties of these films are studied by the Spectroscopic Ellipsometry technique. Refractive index and extinction coefficient of these films are enhanced in normal dispersion region as compared to the parent AlN film. The optical band gap energies also seem to vary between deep UV to UV regions with the addition of Cr, thus by bringing out the usefulness of these films in the area of optoelectronic device applications.

Keywords: ellipsometry, GIXRD, hardness, XAS

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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

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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

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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

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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

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Authors: Majid Saaly, Shahriar Tavousi Tafreshi, Mehdi Nazari Afshar

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The structural systems with the sandwich composite wall (SCSSC) are of very popular due to their ductileness and competency to swallow more energy and power than standard reinforced concrete shear walls. The purpose of this enhanced system is in high-rise building, Nuclear power plant facilities, and bridge slabs are much more. SCSSCs showed acceptable seismic performance under experimental tests and cyclic loading from the points of view of in-plane and out-of-plane shear and flexural interaction, in-plane punching shear, and compressive behavior. The use of sandwich composite walls with J-hook connectors has a significant effect on energy dissipation and reduction of dynamic responses of mid-rise and high-rise structural models. By changing the systems of the building from SW to SCWJ, the maximum inter-story drift values of ten- and fifteen-story models are reduced by up to 25% and 35%, respectively.

Keywords: J-Hook sandwich composite walls, fling step, directivity, IDA analyses, fractile curves

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Authors: Sigal Portnoy, Yael Kaufman-Cohen, Yafa Levanon

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Introduction: Rehabilitation following wrist fractures often includes exercising flexion-extension movements with a dynamic splint. However, during daily activities, we combine most of our wrist movements with radial and ulnar deviations. Also, the multi-plane wrist motion, named the ‘dart throw motion’ (DTM), was found to be a more stable motion in healthy individuals, in term of the motion of the proximal carpal bones, compared with sagittal wrist motion. The aim of this study was therefore to explore the pathomechanics of the wrist in a common multi-plane movement pattern (DTM) and design a novel splint for rehabilitation following distal radius fractures. Methods: First, a multi-axis electro-goniometer was used to quantify the plane angle of motion of the dominant and non-dominant wrists during various activities, e.g. drinking from a glass of water and answering a phone in 43 healthy individuals. The following protocols were then implemented with a population following distal radius fracture. Two dynamic scans were performed, one of the sagittal wrist motion and DTM, in a 3T magnetic resonance imaging (MRI) device, bilaterally. The scaphoid and lunate carpal bones, as well as the surface of the distal radius, were manually-segmented in SolidWorks and the angles of motion of the scaphoid and lunate bones were calculated. Subsequently, a patient-specific splint was designed using 3D scans of the hand. The brace design comprises of a proximal attachment to the arm and a distal envelope of the palm. An axle with two wheels is attached to the proximal part. Two wires attach the proximal part with the medial-palmar and lateral-ventral aspects of the distal part: when the wrist extends, the first wire is released and the second wire is strained towards the radius. The opposite occurs when the wrist flexes. The splint was attached to the wrist using Velcro and constrained the wrist movement to the desired calculated multi-plane of motion. Results: No significant differences were found between the multi-plane angles of the dominant and non-dominant wrists. The most common daily activities occurred at a plane angle of approximately 20° to 45° from the sagittal plane and the MRI studies show individual angles of the plane of motion. The printed splint fitted the wrist of the subjects and constricted movement to the desired multi-plane of motion. Hooks were inserted on each part to allow the addition of springs or rubber bands for resistance training towards muscle strengthening in the rehabilitation setting. Conclusions: It has been hypothesized that activation of the wrist in a multi-plane movement pattern following distal radius fractures will accelerate the recovery of the patient. Our results show that this motion can be determined from either the dominant or non-dominant wrists. The design of the patient-specific dynamic splint is the first step towards assessing whether splinting to induce combined movement is beneficial to the rehabilitation process, compared to conventional treatment. The evaluation of the clinical benefits of this method, compared to conventional rehabilitation methods following wrist fracture, are a part of a PhD work, currently conducted by an occupational therapist.

Keywords: distal radius fracture, rehabilitation, dynamic magnetic resonance imaging, dart throw motion

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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

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Authors: H. S. Haroyan, V. R. Tadevosyan

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High gain broadband plasmonic slot nano-antenna has been considered. The theory of plasmonic slot nano-antenna (PSNA) has been developed. The analytical model takes into account also the electrical field inside the metal due to imperfectness of metal in optical range, as well as numerical investigation based on FEM method has been realized. It should be mentioned that Yagi-Uda configuration improves directivity in the plane of structure. In contrast, in this paper the possibility of directivity improvement of proposed PSNA in perpendicular plane of structure by using reflection metallic surface placed under the slot in fixed distance has been demonstrated. It is well known that a directivity improvement brings to the antenna gain increasing. This method of diagram improving is also well known from RF antenna design theory. Moreover the improvement of directivity in the perpendicular plane gives more flexibility in such application as improving the light and atom, ion, molecule interactions by using such type of plasmonic slot antenna. By the analogy of dipole type optical antennas the widening of working wavelengths has been realized by using bowtie geometry of slots, which made the antenna broadband.

Keywords: broadband antenna, high gain, slot nano-antenna, plasmonics.

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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

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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

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Authors: Zelalem Abebe Bekele, Kun Lei, Xiukai Lan, Xiangyu Liu, Hui Wen, Kaiyou Wang

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Spin-polarized currents offer an efficient means of manipulating the magnetization of a ferromagnetic layer for big data and neuromorphic computing. Research has shown that the orbital Hall effect (OHE) can produce orbital currents, potentially surpassing the counter spin currents induced by the spin Hall effect. However, it’s essential to note that orbital currents alone cannot exert torque directly on a ferromagnetic layer, necessitating a conversion process from orbital to spin currents. Here, we present an efficient method for achieving perpendicularly magnetized spin-orbit torque (SOT) switching by harnessing the localized orbital Hall current generated from a Mo layer within a Mo/CoGd device. Our investigation reveals a remarkable enhancement in the interface-induced planar Hall effect (PHE) within the Mo/CoGd bilayer, resulting in the generation of a z-polarized planar current for manipulating the magnetization of CoGd layer without the need for an in-plane magnetic field. Furthermore, the Mo layer induces out-of-plane orbital current, boosting the in-plane and out-of-plane spin polarization by converting the orbital current into spin current within the dual-property CoGd layer. At the optimal Mo layer thickness, a low critical magnetization switching current density of 2.51×10⁶ A cm⁻² is achieved. This breakthrough opens avenues for all-electrical control energy-efficient magnetization switching through orbital current, advancing the field of spin-orbitronics.

Keywords: spin-orbit torque, orbital hall effect, spin hall current, orbital hall current, interface-generated planar hall current, anisotropic magnetoresistance

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Authors: Larisa Liamina, Vasily Lavrushin, Salvatore Inguaggiato

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This study presents and summarizes the results of researching the isotopic signature of helium in the deep fluid eastern part of the Southern slope of the Greater Caucasus and the Lesser Caucasus (Azerbaijan and Armenia) for the period from 2010 to 2016. The results of isotope ratios of 3He/4He in 59 samples of the gas phase of geothermal fluids and mud volcanoes are presented. New data have been obtained not only on the isotopic ratios of helium, but also neon and argon. The R/Ra ratio was analyzed along the Ankara-Sevan ophiolite structure. The patterns of lateral variations of the 3He/4He ratio of different geological structural elements of the studied region are revealed.

Keywords: isotopes helium, deep fluids, tectonic structures, Caucasus

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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

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Authors: Manojkumar Sabanayagam

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The Riemann hypothesis is an unsolved millennium problem and the search for a solution to the Riemann hypothesis is to study the pattern of prime number distribution. The scope of this paper is to identify the solution for the critical strip and the critical line axis, which has the non-trivial zero solutions using complex plane functions. The Riemann graphical plot is constructed using a linear complex variable function (X+iY) and is applicable only when X>1. But the investigation shows that complex variable behavior has two zones. The first zone is the transformation zone, where the definition of the complex plane should be a non-linear variable which is the critical strip zone in the graph (X=0 to 1). The second zone is the transformed zone (X>1) defined using linear variables conventionally. This paper deals with the Non-linear function in the transformation zone derived using cosine and sinusoidal time lag w.r.t imaginary number ‘i’. The alternate complex variable (Cosθ+i Sinθ) is used to understand the variables in the critical strip zone. It is concluded that the non-trivial zeros present in the Real part 0.5 are because the linear function is not the correct approach in the critical strip. This paper provides the solution to Reimann's hypothesis.

Keywords: Reimann hypothesis, critical strip, complex plane, transformation zone

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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

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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

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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

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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

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Authors: Mohammed Berka, Khaled Merit

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Recent works on microwave filters show that the constituent materials such filters are very important in the design and realization. Several solutions have been proposed to improve the qualities of filtering. In this paper, we propose a new dual band-pass filter based on the coupling of a composite (CRLH) coplanar waveguide with complementary split ring resonators (CSRRs). The (CRLH) CPW is composed of two resonators, each one has an interdigital capacitor (CID) and two short-circuited stubs parallel to top ground plane. On the lower ground plane, we use defected ground structure technology (DGS) to engrave two (CSRRs) offered with different shapes and dimensions. Between the top ground plane and the substrate, we place a ferrite layer to control the electromagnetic coupling between (CRLH) CPW and (CSRRs). The global filter that has coplanar access will have a dual band-pass behavior around the magnetic resonances of (CSRRs). Since there’s no scientific or experimental result in the literature for this kind of complicated structure, it was necessary to perform simulation using HFSS Ansoft designer.

Keywords: complementary split ring resonators, coplanar waveguide, ferrite, filter, stub.

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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

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Authors: M. Beusink, E. W. C. Coenen

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The increased application of novel structural materials, such as high grade asphalt, concrete and laminated composites, has sparked the need for a better understanding of the often complex, non-linear mechanical behavior of such materials. The effective macroscopic mechanical response is generally dependent on the applied load path. Moreover, it is also significantly influenced by the microstructure of the material, e.g. embedded fibers, voids and/or grain morphology. At present, multiscale techniques are widely adopted to assess micro-macro interactions in a numerically efficient way. Computational homogenization techniques have been successfully applied over a wide range of engineering cases, e.g. cases involving first order and second order continua, thin shells and cohesive zone models. Most of these homogenization methods rely on Representative Volume Elements (RVE), which model the relevant microstructural details in a confined volume. Imposed through kinematical constraints or boundary conditions, a RVE can be subjected to a microscopic load sequence. This provides the RVE's effective stress-strain response, which can serve as constitutive input for macroscale analyses. Simultaneously, such a study of a RVE gives insight into fine scale phenomena such as microstructural damage and its evolution. It has been reported by several authors that the type of boundary conditions applied to the RVE affect the resulting homogenized stress-strain response. As a consequence, dedicated boundary conditions have been proposed to appropriately deal with this concern. For the specific case of a planar assumption for the analyzed structure, e.g. plane strain, axisymmetric or plane stress, this assumption needs to be addressed consistently in all considered scales. Although in many multiscale studies a planar condition has been employed, the related impact on the multiscale solution has not been explicitly investigated. This work therefore focuses on the influence of the planar assumption for multiscale modeling. In particular the plane stress case is highlighted, by proposing three different implementation strategies which are compatible with a first-order computational homogenization framework. The first method consists of applying classical plane stress theory at the microscale, whereas with the second method a generalized plane stress condition is assumed at the RVE level. For the third method, the plane stress condition is applied at the macroscale by requiring that the resulting macroscopic out-of-plane forces are equal to zero. These strategies are assessed through a numerical study of a thin walled structure and the resulting effective macroscale stress-strain response is compared. It is shown that there is a clear influence of the length scale at which the planar condition is applied.

Keywords: first-order computational homogenization, planar analysis, multiscale, microstrucutures

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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

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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

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Authors: N. H. Hamid, A. Azmi, M. I. Adiyanto

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This main purpose of this paper is to evaluate the seismic performance of double bay two-storey reinforced concrete frame under in-plane lateral cyclic loading which designed using Eurocode 8 (EC8) by taking into account of seismic loading. The prototype model of reinforced concrete frame was constructed in one-half scale tested under in-plane lateral cyclic loading starts with ±0.2% drift, ±0.25% up to ±3.0% drift with the increment of ±0.25%. The performance of the RC frame is evaluated in terms of the hysteresis loop (load vs. displacement), stiffness, ductility, lateral strength, stress-strain relationship and equivalent viscous damping. Visual observation of the crack pattern after testing were observed where the beam- column joint suffer the most severe damage as it is the critical part in moment resisting frame. Spalling of concrete starts occurred at ±2.0% drift and become worse at ±2.5% drift. The experimental result shows that the maximum lateral strength of specimen is 99.98 kN and ductility of the specimen is µ=4.07 which lies between 3≤µ≤6 in order to withstand moderate to severe earthquakes.

Keywords: ductility, equivalent viscous damping, hysteresis loops, lateral strength, stiffness

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Authors: Karina C. Azzolin, Luis E. Kosteski, Alisson S. Milani, Raquel C. Zydeck

Abstract:

This work aims to represent Numerically tests experimentally developed in reduced scale walls with horizontal and inclined cuts by using the Lattice Discrete Element Method (LDEM) implemented On de Abaqus/explicit environment. The cuts were performed with depths of 20%, 30%, and 50% On the walls subjected to centered and eccentric loading. The parameters used to evaluate the numerical model are its strength, the failure mode, and the in-plane and out-of-plane displacements.

Keywords: structural masonry, wall chases, small scale, numerical model, lattice discrete element method

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Authors: Seyed Hamed Mousavi, Juha M. Hijmans, Reza Rajabi, Ron Diercks, Johannes Zwerver, Henk van der Worp

Abstract:

Introduction: Running is one of the most popular sports activity among people. Improper sagittal plane ankle, knee and hip kinematics are considered to be associated with the increase of injury risk in runners. Motion assessing smart-phone applications are increasingly used to measure kinematics both in the field and laboratory setting, as they are cheaper, more portable, accessible, and easier to use relative to 3D motion analysis system. The aims of this study are 1) to compare the results of 3D gait analysis system and CE; 2) to evaluate the test-retest and intra-rater reliability of coach’s eye (CE) app for the sagittal plane hip, knee, and ankle angles in the touchdown and toe-off while running. Method: Twenty subjects participated in this study. Sixteen reflective markers and cluster markers were attached to the subject’s body. Subjects were asked to run at a self-selected speed on a treadmill. Twenty-five seconds of running were collected for analyzing kinematics of interest. To measure sagittal plane hip, knee and ankle joint angles at touchdown (TD) and toe off (TO), the mean of first ten acceptable consecutive strides was calculated for each angle. A smartphone (Samsung Note5, android) was placed on the right side of the subject so that whole body was simultaneously filmed with 3D gait system during running. All subjects repeated the task with the same running speed after a short interval of 5 minutes in between. The CE app, installed on the smartphone, was used to measure the sagittal plane hip, knee and ankle joint angles at touchdown and toe off the stance phase. Results: Intraclass correlation coefficient (ICC) was used to assess test-retest and intra-rater reliability. To analyze the agreement between 3D and 2D outcomes, the Bland and Altman plot was used. The values of ICC were for Ankle at TD (TRR=0.8,IRR=0.94), ankle at TO (TRR=0.9,IRR=0.97), knee at TD (TRR=0.78,IRR=0.98), knee at TO (TRR=0.9,IRR=0.96), hip at TD (TRR=0.75,IRR=0.97), hip at TO (TRR=0.87,IRR=0.98). The Bland and Altman plots displaying a mean difference (MD) and ±2 standard deviation of MD (2SDMD) of 3D and 2D outcomes were for Ankle at TD (MD=3.71,+2SDMD=8.19, -2SDMD=-0.77), ankle at TO (MD=-1.27, +2SDMD=6.22, -2SDMD=-8.76), knee at TD (MD=1.48, +2SDMD=8.21, -2SDMD=-5.25), knee at TO (MD=-6.63, +2SDMD=3.94, -2SDMD=-17.19), hip at TD (MD=1.51, +2SDMD=9.05, -2SDMD=-6.03), hip at TO (MD=-0.18, +2SDMD=12.22, -2SDMD=-12.59). Discussion: The ability that the measurements are accurately reproduced is valuable in the performance and clinical assessment of outcomes of joint angles. The results of this study showed that the intra-rater and test-retest reliability of CE app for all kinematics measured are excellent (ICC ≥ 0.75). The Bland and Altman plots display that there are high differences of values for ankle at TD and knee at TO. Measuring ankle at TD by 2D gait analysis depends on the plane of movement. Since ankle at TD mostly occurs in the none-sagittal plane, the measurements can be different as foot progression angle at TD increases during running. The difference in values of the knee at TD can depend on how 3D and the rater detect the TO during the stance phase of running.

Keywords: reliability, running, sagittal plane, two dimensional

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

Abstract:

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

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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

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Authors: M. Abou Al-alaa, H. A. Elsadek, E. A. Abdallah, E. A. Hashish

Abstract:

A square patch antenna with both frequency and polarization reconfigurability is presented. The antenna consists of a square patch with coplanar feed on the ground plane. On the patch side, there is a parasitic square ring that is responsible for changing the antenna polarization. On the ground plane, there is a rectangular slot. By changing of length of this slot, the antenna resonance frequency can be changed. The antenna operates at 1.57 and 2.45 GHz that used in GPS and Bluetooth applications, respectively. The length of the slot in the proposed antenna is 40 mm, and the antenna operates at the lower frequency (1.57 GHz). By using switches in the ground plane the slot length can be adjust to 24 mm, so the antenna operates at upper frequency (2.45 GHz). Two switches are mounted on the parasitic ring at optimized positions. By switching between the different states of these two switches, the proposed antenna operates with linear polarization (LP) and circular polarization (CP) at each operating frequency. The antenna gain at 1.57 and 2.45 GHz are 5.9 and 7.64 dBi, respectively. The antenna is analyzed using the CST Microwave Studio. The proposed antenna was fabricated and measured. Results comparison shows good agreement. The antenna has applications in several wireless communication systems.

Keywords: microstrip patch antenna, reconfigurable antenna, frequency reconfigurability, polarization reconfigurability, parasitic square ring, linear polarization, circular polarization

Procedia PDF Downloads 505