Search results for: deep plate loading test

Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

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Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Ajay Kumar Choubey, Geeta Agnihotri, C. Sasikumar, Rashmi Dwivedi

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Deep-drawing operations are performed widely in industrial applications. It is very important for efficiency to achieve parts with no or minimum defects. Deep drawn parts are used in high performance, high strength and high reliability applications where tension, stress, load and human safety are critical considerations. Wrinkling is a kind of defect caused by stresses in the flange part of the blank during metal forming operations. To avoid wrinkling appropriate blank-holder pressure/force or drawbead can be applied. Now-a-day computer simulation plays a vital role in the field of manufacturing process. So computer simulation of manufacturing has much advantage over previous conventional process i.e. mass production, good quality of product, fast working etc. In this study, a two dimensional elasto-plastic Finite Element (F.E.) model for Mild Steel material blank has been developed to study the behavior of the flange wrinkling and deep drawing parameters under different Blank-Holder Pressure (B.H.P.). For this, commercially available Finite Element software ANSYS 14 has been used in this study. Simulation results are critically studied and salient conclusions have been drawn.

Keywords: ANSYS, deep drawing, BHP, finite element simulation, wrinkling

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Hesheng Wang, Haoyu Wang, Chungang Zhuang

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Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.

Keywords: pose estimation, deep learning, point cloud, bin-picking, 3D computer vision

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Authors: M. Cangi, T. Dolar, C. Ersoy, Y. E. Aydogdu, A. I. Aydeniz, A. Mugan

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The drum is one of the critical parts in a washing machine in which the clothes are washed and spin by the rotational movement. It is activated by the drum shaft which is attached to an electric motor and subjected to dynamic loading. Being one of the critical components, failures of the drum require costly repairs of dynamic components. In this study, tolerance bands between the drum shaft and its two bearings were examined to develop a relationship between the fatigue life of the shaft and the interaction tolerances. Optimization of tolerance bands was completed in consideration of the fatigue life of the shaft as the cost function. The following methodology is followed: multibody dynamic model of a washing machine was constructed and used to calculate dynamic loading on the components. Then, these forces were used in finite element analyses to calculate the stress field in critical components which was used for fatigue life predictions. The factors affecting the fatigue life were examined to find optimum tolerance grade for a given test condition. Numerical results were verified by experimental observations.

Keywords: fatigue life, finite element analysis, tolerance analysis, optimization

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Authors: Def Primal, Sasanty Kusumaningtyas, Ermita I. Ibrahim

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Purpose: The main objective of this study is to determine the pes planus plantaris (flat foot) condition can contribute to the disturbance of postural stability in basketball athletes in static and dynamic activities. Methods: This cross-sectional quantitative analytical retrospective study on 47 subjects of basketball student-athletes identified the foot arch index by extensive footprint area and AMTI (Advanced Mechanical Technology Inc.) Force flat-form (force plate) determined their postural stability. Subjects were conducted in three activities (static, dynamic vertical jump, and dynamic loading response) for ground reaction force (GRF) resultant vectors towards the vertical plane of body mass (W). Results Analytical results obtained that 80.9% of subjects had pes planus plantaris. It shows no significant differences in pes planus plantaris incidence in both sexes subject (p>0.005); however, there are differences in athlete’s exercise period aspect. Athlete students who have practiced strictly for more than four years’ experience over 50% of pes planus plantaris; furthermore, a long period of exercise was believed to stimulate pes planus. The average value of GRF vectors of pes planus plantaris subjects on three different basketball movements shows a significant correlation to postural stability. Conclusions Pes planus plantaris affected almost basketball athletes regarding the length and intensity of exercise performed. The condition significantly contributes to postural stability disturbance on a static condition, dynamic vertical jump, and dynamic vertical jump loading response.

Keywords: pes planus plantaris, flatfoot, ground reaction force, static and dynamic stability

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Piroska Biró, Mária Csernoch, János Máth, Kálmán Abari

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Testing the first year students of Informatics at the University of Debrecen revealed that students start their tertiary studies in programming with a low level of programming knowledge and algorithmic skills. The possible reasons which lead the students to this very unfortunate result were examined. The results of the test were compared to the students’ results in the school leaving exams and to their self-assessment values. It was found that there is only a slight connection between the students’ results in the test and in the school leaving exams, especially at intermediate level. Beyond this, the school leaving exams do not seem to enable students to evaluate their own abilities.

Keywords: deep and surface approaches, metacognitive abilities, programming and algorithmic skills, school leaving exams, tracking code

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Authors: Aboussalih Amira, Zarza Tahar, Fedaoui Kamel, Baroura Lazhar, Hammoudi Salah

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316 L steel is a stainless steel frequently used in orthopedic surgery; in the design of implants (hip, knee, shoulder, ankle, etc.), in dental surgery, cardiology, ophthalmology. Before any use, it is essential to predict the macroscopic phenomenological behavior of the material, and to analyze its response. The behavior of 316 L steel in low cycle fatigue, under uniaxial cyclic loading of tension/compression, producing significant plastic deformations leading to material damage. This investigation allowed us to characterize the behavior of the 316L steel employed in the locking of the compression plates (LCP), of which they are generally used in orthopedics to stabilize the fractured bone parts. And to perceive the phenomenon of Ratcheting leading to the damage of LCP by an excess of plastic deformation under nonsymmetrical alternated imposed constraint in low cycle fatigue.

Keywords: 316L SS, locking compression plate, low cycle fatigue, ratcheting

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Authors: Xueming Xu, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

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A combat system-of-systems (CSoS) comprises various types of functional combat entities that interact to meet corresponding task requirements in the present and future. Enhancing the resilience of CSoS holds significant military value in optimizing the operational planning process, improving military survivability, and ensuring the successful completion of operational tasks. Accordingly, this research proposes an integrated framework called CSoS resilience enhancement (CSoSRE) to enhance the resilience of CSoS from a recovery perspective. Specifically, this research presents a two-phase resilience optimization model to define a resilience optimization objective for CSoS. This model considers not only task baseline, recovery cost, and recovery time limit but also the characteristics of emergency recovery and comprehensive recovery. Moreover, the research extends it from the deterministic case to the stochastic case to describe the uncertainty in the recovery process. Based on this, a resilience-oriented recovery optimization method based on deep reinforcement learning (RRODRL) is proposed to determine a set of entities requiring restoration and their recovery sequence, thereby enhancing the resilience of CSoS. This method improves the deep Q-learning algorithm by designing a discount factor that adapts to changes in CSoS state at different phases, simultaneously considering the network’s structural and functional characteristics within CSoS. Finally, extensive experiments are conducted to test the feasibility, effectiveness and superiority of the proposed framework. The obtained results offer useful insights for guiding operational recovery activity and designing a more resilient CSoS.

Keywords: combat system-of-systems, resilience optimization model, recovery optimization method, deep reinforcement learning, certainty and uncertainty

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Korosh Khorshidi, Mohammad Khodadadi

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In this paper, exact close form solution for out of plate free flexural vibration of moderately thick rectangular nanoplates are presented based on nonlocal modified trigonometric shear deformation theory, with assumptions of the Levy's type boundary conditions, for the first time. The aim of this study is to evaluate the effect of small-scale parameters on the frequency parameters of the moderately thick rectangular nano-plates. To describe the effects of small-scale parameters on vibrations of rectangular nanoplates, the Eringen theory is used. The Levy's type boundary conditions are combination of six different boundary conditions; specifically, two opposite edges are simply supported and any of the other two edges can be simply supported, clamped or free. Governing equations of motion and boundary conditions of the plate are derived by using the Hamilton’s principle. The present analytical solution can be obtained with any required accuracy and can be used as benchmark. Numerical results are presented to illustrate the effectiveness of the proposed method compared to other methods reported in the literature. Finally, the effect of boundary conditions, aspect ratios, small scale parameter and thickness ratios on nondimensional natural frequency parameters and frequency ratios are examined and discussed in detail.

Keywords: exact solution, nonlocal modified sinusoidal shear deformation theory, out of plane vibration, moderately thick rectangular plate

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Authors: Farnaz Fotovvatikhah, Javad Akbari

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Test time of a test architecture is an important factor which depends on the architecture's delay and test patterns. Here a new architecture to store the test results based on network on chip is presented. In addition, simple analytical model is proposed to calculate link test time for built in self-tester (BIST) and external tester (Ext) in multiprocessor systems. The results extracted from the model are verified using FPGA implementation and experimental measurements. Systems consisting 16, 25, and 36 processors are implemented and simulated and test time is calculated. In addition, BIST and Ext are compared in terms of test time at different conditions such as at different number of test patterns and nodes. Using the model the maximum frequency of testing could be calculated and the test structure could be optimized for high speed testing.

Keywords: test, nano on-chip network, JTAG, modelling

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Authors: Masoumeh Shojaei, Natalie Gedayloo, Amir Sarshin

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The purpose of the present study was to investigate the effect of Isokinetic fatigue of muscles around the ankle, knee, and hip on the indicators of dynamic postural stability. Therefore, 15 female university students (age 19.7± 0.6 years old, weight 54.6± 9.4 kg, and height 163.9± 5.6 cm) participated in within-subjects design for 5 different days. In the first session, the postural stability indices (time to stabilization after jump-landing) without fatigue were assessed by force plate and in each next sessions, one of muscle groups of the lower limb including the muscles around ankles, knees, and hip was randomly exhausted by Biodex Isokinetic dynamometer and the indices were assessed immediately after the fatigue of each muscle group. The method involved landing on a force plate from a dynamic state, and transitioning balance into a static state. Results of ANOVA with repeated measures indicated that there was no significant difference between the time to stabilization (TTS) before and after Isokinetic fatigue of the muscles around the ankle, knee and hip in medial – lateral direction (p > 0.05), but in the anterior – posterior (AP) direction, the difference was statistically significant (p < 0.05). Least Significant Difference (LSD) post hoc test results also showed that there was significant difference between TTS in knee and hip muscles before and after isokinetic fatigue in AP direction. In the other hand knee and hip muscles group were affected by isokinetic fatigue only in AP surface (p < 0.05).

Keywords: dynamic balance, fatigue, lower limb muscles, postural control

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: Rana Gill, Chandandeep Kaur

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Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts.

Keywords: segmentation, edge detection, text, extracting characters

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Authors: Mourad Bédir, Sami Khomsi, Hakim Gabtni, Hajer Azaiez, Ramzi Gharsalli, Riadh Chebbi

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Eastern Tunisia is a semi-arid area; located in the northern Africa plate; southern Mediterranean side. It is facing water scarcity, overexploitation, and decreasing of water quality of phreatic water table. Water supply and storage will not respond to the demographic and economic growth and demand. In addition, only 5 109 m3 of rainwater from 35 109 m3 per year renewable rain water supply can be retained and remobilized. To remediate this water deficiency, researches had been focused to near new subsurface deep aquifers resources. Among them, Upper Miocene sandstone deposits of Béglia, Saouaf, and Somaa Formations. These sandstones are known for their proven Hydrogeologic and hydrocarbon reservoir characteristics in the Tunisian margin. They represent semi-confined to confined aquifers. This work is based on new integrated approaches of seismic stratigraphy, seismic tectonics, and hydrogeology, to highlight and characterize these reservoirs levels for aquifer exploitation in semi-arid area. As a result, five to six third order sequence deposits had been highlighted. They are composed of multi-layered extended sandstones reservoirs; separated by shales packages. These reservoir deposits represent lowstand and highstand system tracts of these sequences, which represent lowstand and highstand system tracts of these sequences. They constitute important strategic water resources volumes for the region.

Keywords: Tunisia, Hydrogeology, sandstones, basin, seismic, aquifers, modeling

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Authors: Z. Arabshahi, I. Afara, A. Oloyede, H. Moody, J. Kashani, T. Klein

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Articular cartilage is a fluid-swollen tissue of synovial joints that functions by providing a lubricated surface for articulation and to facilitate the load transmission. The biomechanical function of this tissue is highly dependent on the integrity of its ultrastructural matrix. Any alteration of articular cartilage matrix, either by injury or degenerative conditions such as osteoarthritis (OA), compromises its functional behaviour. Therefore, the assessment of articular cartilage is important in early stages of degenerative process to prevent or reduce further joint damage with associated socio-economic impact. Therefore, there has been increasing research interest into the functional assessment of articular cartilage. This study developed a characterization parameter for articular cartilage assessment based on the response of cartilage surface to loading/unloading. This is because the response of articular cartilage to compressive loading is significantly depth-dependent, where the superficial zone and underlying matrix respond differently to deformation. In addition, the alteration of cartilage matrix in the early stages of degeneration is often characterized by PG loss in the superficial layer. In this study, it is hypothesized that the response of superficial layer is different in normal and proteoglycan depleted tissue. To establish the viability of this hypothesis, samples of visually intact and artificially proteoglycan-depleted bovine cartilage were subjected to compression at a constant rate to 30 percent strain using a ring-shaped indenter with an integrated ultrasound probe and then unloaded. The response of articular surface which was indirectly loaded was monitored using ultrasound during the time of loading/unloading (deformation/recovery). It was observed that the rate of cartilage surface response to loading/unloading was different for normal and PG-depleted cartilage samples. Principal Component Analysis was performed to identify the capability of the cartilage surface response to loading/unloading, to distinguish between normal and artificially degenerated cartilage samples. The classification analysis of this parameter showed an overlap between normal and degenerated samples during loading. While there was a clear distinction between normal and degenerated samples during unloading. This study showed that the cartilage surface response to loading/unloading has the potential to be used as a parameter for cartilage assessment.

Keywords: cartilage integrity parameter, cartilage deformation/recovery, cartilage functional assessment, ultrasound

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Authors: Sankara Ganesh Dhoopam, Phaneendra Aduri

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Lattice towers are light weight structures which are primarily governed by the effects of wind loading. Ensuring a precise assessment of wind loads on the tower structure, antennas, and associated equipment is vital for the safety and efficiency of tower design. Earlier, the Indian standards are not available for design of telecom towers. Instead, the industry conventionally relied on the general building wind loading standard for calculating loads on tower components and the transmission line tower design standard for designing the angular members of the towers. Subsequently, the Bureau of Indian Standards (BIS) revised these standards and angular member design standard. While the transmission line towers are designed using the above standard, a full-scale model test will be done to prove the design. Telecom angular towers are also designed using the same with overload factor/factor of safety without full scale tower model testing. General construction in steel design code is available with limit state design approach and is applicable to the design of general structures involving angles and tubes but not used for angle member design of towers. Recently, in response to the evolving industry needs, the Bureau of Indian Standards (BIS) introduced a new standard titled “Isolated Towers, Masts, and Poles using structural steel -Code of practice” for the design of telecom towers. This study focuses on a 40m four legged angular tower to compare loading calculations and member designs between old and new standards. Additionally, a comparative analysis aligning with the new code provisions with international loading and design standards with a specific focus on American standards has been carried out. This paper elaborates code-based provisions used for load and member design calculations, including the influence of "ka" area averaging factor introduced in new wind load case.

Keywords: telecom, angular tower, PLS tower, GSM antenna, microwave antenna, IS 875(Part-3):2015, IS 802(Part-1/sec-2):2016, IS 800:2007, IS 17740:2022, ANSI/TIA-222G, ANSI/TIA-222H.

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Authors: Prathmesh Vikas Patil, Yashaswini Lomte Patil

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Laminated composite materials have considerable attention in various engineering applications due to their exceptional strength-to-weight ratio and mechanical properties. The analysis of laminated composite plates in three-dimensional (3D) elasticity is a complex problem, as it requires accounting for the orthotropic anisotropic nature of the material and the interactions between multiple layers. Conventional approaches, such as the classical plate theory, provide simplified solutions but are limited in performing exact analysis of the plate. To address such a challenge, the state space method emerges as a powerful numerical technique for modeling the behavior of laminated composites in 3D. The state-space method involves transforming the governing equations of elasticity into a state-space representation, enabling the analysis of complex structural systems in a systematic manner. Here, an effort is made to perform a 3D elasticity analysis of plates with cross-ply and angle-ply laminates using the state space approach. The state space approach is used in this study as it is a mixed formulation technique that gives the displacements and stresses simultaneously with the same level of accuracy.

Keywords: cross ply laminates, angle ply laminates, state space method, three-dimensional elasticity analysis

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Mohamed M. A. Abumandour

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The red swamp freshwater crayfish breathe through three types of feather-like trichobranchiate gills; podobranchiae, arthrobranchiae and pleurobranchiae. All gills have the same general structure and appearance; plume-like with single broad setiferous, and single axis. The gill consists of axis with numerous finger-like filaments, having three morphological types; round, pointed and somewhat hooked shaped. The direction of filaments vary according their position; in middle region were nearly perpendicular to gill axis while in the apex were nearly parallel to axis. There were characteristic system of gill spines on; central axis (two types were distinguishable by presence of socket), basal plate, setobranch (long non-branched and short multidenticulate) and on the bilobed epipodal plate. There are four shape of spinated-like distal region of setobranch seta; two pointed processes (longitudinal arrangement and irregular arranged) and two broad processes (transverse triangular and multidenticulate). The bilobed epipodal plate devoid from any filaments and extended from outer side of podobranchiae as triangular basal part then extended between the gills as cord-like middle part then pass under the gill to lies against the thoracic body wall. By SEM, the apical part of bilobed epipodal plate have serrated free border and corrugated surface while the middle part have none serrated free border. There are two methods of gill cleaning mechanism in crayfish; passive and active method. The passive method occurred by; setae of setobranch, branchiostegite, bilobed epipodal plate, setiferous arthrodial lamellae and reversing the respiratory water through a narrow spaced branchial chamber.

Keywords: crayfis, gill spines, setobranch, gill setae, cleaning mechanisms

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Niaz Gharavi, Hexin Zhang

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In the structural timber design, the shear modulus of the timber beam is an important factor that needs to be determined accurately. According to BS EN 408, shear modulus can be determined using torsion test or shear field test method. Although torsion test creates pure shear status in the beam, it does not represent the real-life situation when the beam is in the service. On the other hand, shear field test method creates similar loading situation as in reality. The latter method is based on shear distortion measurement of the beam at the zone with the constant transverse load in the standardized four-point bending test as indicated in BS EN 408. Current testing practice code advised using two metallic arms act as an instrument to measure the diagonal displacement of the constructing square. Timber is not a homogenous material, but a heterogeneous and this characteristic makes timber to undergo a non-uniform deformation. Therefore, the dimensions and the location of the constructing square in the area with the constant transverse force might alter the shear modulus determination. This study aimed to investigate the impact of the shape, size, and location of the square in the shear field test method. A binocular stereo vision system was developed to capture the 3D displacement of a grid of target points. This approach is an accurate and non-contact method to extract the 3D coordination of targeted object using two cameras. Two group of three glue laminated beams were produced and tested by the mean of four-point bending test according to BS EN 408. Group one constructed using two materials, laminated bamboo lumber and structurally graded C24 timber and group two consisted only structurally graded C24 timber. Analysis of Variance (ANOVA) was performed on the acquired data to evaluate the significance of size and location of the square in the determination of shear modulus of the beam. The results have shown that the size of the square is an affecting factor in shear modulus determination. However, the location of the square in the area with the constant shear force does not affect the shear modulus.

Keywords: shear field test method, BS EN 408, timber shear modulus, photogrammetry approach

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Authors: Merbouha Barka, K. H. Benrahou, A. Fakrar, A. Tounsi, E. A. Adda Bedia

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This paper presents an analytical investigation on the buckling and postbuckling behaviors of thick functionally graded plates subjected to thermal load .Material properties are assumed to be temperature dependent, and graded in the thickness direction according to a simple power law distribution in terms of the volume fractions of constituents. The formulations are based on first order shear deformation plate theory taking into account Von Karman nonlinearity and initial geometrical imperfection. By applying Galerkin method, closed-form relations of postbuckling equilibrium paths for simply supported plates are determined. Analysis is carried out to show the effects of material and geometrical properties, in-plane boundary restraint, and imperfection on the buckling and postbuckling loading capacity of the plates.

Keywords: functionally graded materials, postbuckling, first order shear deformation theory, imperfection

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Authors: Amal Khalifa, Nicolas Vana Santos

Abstract:

Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Md. Zahirul Islam, Chad A. Ulven

Abstract:

Fiber-reinforced polymer matrix composites have a wide range of applications in the sectors of automotive, aerospace, sports utilities, among others, due to their high specific strength, stiffness as well as reduced weight. In addition to those favorable properties, composites composed of natural fibers and bio-based resins (i.e., biocomposites) have eco-friendliness and biodegradability. However, the applications of biocomposites are limited due to the lack of knowledge about their long-term reliability under fluctuating loads. In order to explore the long-term reliability of flax fiber reinforced composites under fluctuating loads through high cycle fatigue strength (HCFS), fatigue test were conducted on unidirectional flax fiber reinforced thermoset composites at different percentage loads of ultimate tensile strength (UTS) with a loading frequency of 5 Hz. Change of temperature of the sample during cyclic loading was captured using an IR camera. Initially, the temperature increased rapidly, but after a certain time, it stabilized. A mathematical model was developed to predict the fatigue life from the data of stabilized temperature. Stabilized temperature and dissipated energy per cycle were compared with applied stress. Both showed bilinear behavior and the intersection of those curves were used to determine HCFS. HCFS for unidirectional flax fiber reinforced composites is around 45% of UTS for a loading frequency of 5Hz. Unlike fatigue life, stabilized temperature and dissipated energy-based models are convenient to define HCFS as they have little variation from sample to sample.

Keywords: energy method, fatigue, flax fiber reinforced composite, HCFS, thermographic approach

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

Abstract:

Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Azubuike Ekwere

Abstract:

The shallow and deep aquiferous horizons of the fractured and weathered crystalline basement Oban Massif of south-eastern Nigeria were studied during the dry and wet seasons. The criteria were ascertaining hydrochemistry relative to seasonal and spatial variations across the study area. Results indicate that concentrations of major cations and anions exhibit the order of abundance; Ca>Na>Mg>K and HCO3>SO4>Cl respectively, with minor variations across sampling seasons. Major elements Ca, Mg, Na and K were higher for the shallow aquifers than the deep aquifers across seasons. The major anions Cl, SO4, HCO3, and NO3 were higher for the deep aquifers compared to the shallow ones. Two water types were identified for both aquifer types: Ca-Mg-HCO3 and Ca-Na-Cl-SO4. Most of the parameters considered were within the international limits for drinking, domestic and irrigation purposes. Assessment by use of sodium absorption ratio (SAR), percent sodium (%Na) and the wilcox diagram reveals that the waters are suitable for irrigation purposes.

Keywords: shallow aquifer, deep aquifer, seasonal variation, hydrochemistry, Oban massif, Nigeria

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Authors: Alessandro Marano

Abstract:

The author describes the use of deep plane face lifting and platysmaplasty to treat sagging neck with minimal scars. Series of case study. The author uses a selective deep plane face lift with a minimal access scar that not extend behind the ear lobe, neck liposuction and platysmaplasty to restore the sagging neck; the scars are minimal and no require drainage post-op. The deep plane face lifting can achieve a good result restoring vertical vectors in aging and sagging face, neck district can be treated without cutting the skin behind the ear lobe combining the SMAS vertical suspension and platysmaplasty; surgery can be performed in local anesthesia with sedation in day surgery and fast recovery. Restoring neck sagging without extend scars behind ear lobe is possible in selected patients, procedure is fast, safe, no drainage required, patients are satisfied and healing time is fast and comfortable.

Keywords: face lifting, aesthetic, face, neck, platysmaplasty, deep plane

Procedia PDF Downloads 105