World Academy of Science, Engineering and Technology

Authors: Shuangxin Wu

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Superalloy ultra-thin-walled components occupy a considerable proportion of aero engines and play an increasingly important role in structural weight reduction and performance improvement. To solve problems such as high deformation resistance and poor formability at room temperature, the introduction of pulse current in the processing process can improve the plasticity of metal materials, but the influence mechanism of pulse current on the forming limit of superalloy ultra-thin sheet is not clear, which is of great significance for determining the material processing window and improving the micro-forming process. The effect of pulse current on the microstructure evolution of superalloy thin plates was observed by optical microscopy (OM) and X-ray diffraction topography (XRT) by applying pulse current to GH3039 with a thickness of 0.2mm under plane strain and uniaxial tensile states. Compared with the specimen without pulse current applied at the same temperature, the internal void volume fraction is significantly reduced, reflecting the non-thermal effect of pulse current on the growth of micro-pores. ED (electrically deforming) specimens have larger and deeper dimples, but the elongation is not significantly improved because the pulse current promotes the void coalescence process, resulting in material fracture. The electro-plastic phenomenon is more obvious in the plane strain state, which is closely related to the effect of stress triaxial degree on the void evolution under pulsed current.

Keywords: pulse current, superalloy, ductile fracture, void damage

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Authors: Rui Zhao, Dongxu Zhang, Min Wan

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Composite materials are widely used in the fields of aviation, aerospace, and transportation due to their lightweight and high strength. Functionalization of composite structures is a hot topic in the future development of composite materials. This article proposed a polyimide-resin based composite material with a sensing function. This material can serve as a sensor to achieve deformation monitoring of metal sheets in room temperature environments. In the deformation process of metal sheets, the slope of the linear fitting line for the corresponding material resistance change rate is different in the elastic stage and the plastic strengthening stage. Therefore, the slope of the material resistance change rate can be used to characterize the deformation stage of the metal sheet. In addition, the resistance change rate of the material exhibited a good negative linear relationship with temperature in a high-temperature environment, and the determination coefficient of the linear fitting line for the change rate of material resistance in the range of 520-650℃ was 0.99. These results indicate that the material has the potential to be applied in the monitoring of mechanical properties of structural materials and temperature monitoring of high-temperature environments.

Keywords: polyimide, composite, sensing, resistance change rate

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Authors: Aguib Salah, Chikh Noureddine, Djedid Toufik, Nour Abdelkader, Kobzili Lallia

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This work presents an experimental analysis of the dynamic properties of microcomposite magnetorheological elastomer (MMRE) by a dynamic mechanical analyzer (DMA). The charge of magnetized iron particles is 30% of the total volume. A dynamic mechanical analysis DMA was carried out in the scanning mode of the amplitude of shear strain and for magnetic field densities varying from 0mT to 325mT. The storage modulus G’ and the loss modulus G’’ of the elastomer decrease when the amplitude of the strain increases. This trend is more pronounced under a higher magnetic flux density (250mT and 325mT). In the presence of the magnetic field, the level of these two dynamic moduli and the damping increases considerably, passing from one value to another of the applied external magnetic field. As a result, the MR effect of MRE elastomers has increased significantly with increasing magnetic flux density.

Keywords: microcomposite, magnetorheological elastomer, magnetic flux density, dynamic mechanical properties, iron particles

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Authors: Lukas Wimmer, Christian Bienert, Robert Schiftner, Christoph Eisenmenger-Sittner

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The mechanism of K-doping, with its resulting bubbles, can affect recrystallization and thus the mechanical properties of tungsten significantly. Within this study the possibility to modify the microstructure of thin sheets via different annealing procedures is investigated. The effects of non-isothermal annealing procedures, especially with varying temperature increase rates, are compared for rolled pure and potassium doped tungsten sheets. The annealing processes were carried out under ultra high vacuum conditions with direct sample heating. The analysis of the microstructure was conducted via metallographic methods and electron backscatter diffraction. To assess any effect of the potassium, e.g. the bubble size or distribution, scanning electron microscopy studies were carried out. The resulting microstructure of the doped tungsten sheets presented elongated, layered grains that could be affected significantly by the temperature increase rate. By an increase of the heating rate from 20 K/min to 14400 K/min a reduction of the average grain size (in rolling direction) from 476 to 68 μm could be achieved. In contrast to that, the temperature increase rate during annealing was not significantly relevant for the recrystallized microstructure of pure tungsten sheets. However, additional isothermal annealing resulted in a significant grain growth for pure tungsten. These further isothermal procedures did in general not affect the grain structure of doped tungsten sheets. The microstructural differences were attributed to the respective grain growth mechanisms, abnormal for doped and normal for pure tungsten. Even though the microstructure was affected considerably by the heating rate in the K-doped case, the corresponding K-bubbles, in turn, were only affected by the maximum temperature during annealing. Different temperature change rates showed no significant effect on the bubble size distribution. Moreover, additional annealing at 2400°C led to comparably minor changes in the average bubble size.

Keywords: K-bubble distribution, microstructure, non-isothermal annealing, potassium doping

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Authors: Donatien Callon, Ian Lalonde, Mathieu Nadeau, Alexandre Girard

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Patient transfer is a challenging, critical task because it exposes caregivers to injury risks. Available transfer devices, like floor lifts, lead to improvements but are far from perfect. They do not eliminate the caregivers’ risk of musculoskeletal disorders, and they can be burdensome to use due to their poor maneuverability. This paper presents a new motorized floor lift with a single central motorized wheel connected to an instrumented handle. Admittance controllers are designed to 1) improve the device maneuverability, 2) reduce the required caregiver effort, and 3) ensure the security and comfort of patients. Two controller designs, one with a linear admittance law and a non-linear admittance law with variable damping, were developed and implemented on a prototype. Tests were performed on seven participants to evaluate the performance of the assistance system and the controllers. The experimental results show that 1) the motorized assistance with the variable damping controller improves maneuverability by 28%, 2) reduces the amount of effort required to push the lift by 66%, and 3) provides the same level of patient comfort compared to a standard unassisted floor lift.

Keywords: floor lift, human robot interaction, admittance controller, variable admittance

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Authors: Martin Arguelles Perez, Woosoon Yim, Alexander Barzilov

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High-fidelity radiation monitoring is an essential component in the enhancement of the situational awareness capabilities of the Department of Energy’s Office of Environmental Management (DOE-EM) personnel. In this paper, multiple units of unmanned aerial vehicles (UAVs) each equipped with a cadmium zinc telluride (CZT) gamma-ray sensor are used for radiation source localization, which can provide vital real-time data for the EM tasks. To achieve this goal, a fully autonomous system of multicopter-based UAV swarm in 3D tetrahedron formation is used for surveying the area of interest and performing radiation source localization. The CZT sensor used in this study is suitable for small-size multicopter UAVs due to its small size and ease of interfacing with the UAV’s onboard electronics for high-resolution gamma spectroscopy enabling the characterization of radiation hazards. The multicopter platform with a fully autonomous flight feature is suitable for low-altitude applications such as radiation contamination sites. The conventional approach uses a single UAV mapping in a predefined waypoint path to predict the relative location and strength of the source, which can be time-consuming for radiation localization tasks. The proposed UAV swarm-based approach can significantly improve its ability to search for and track radiation sources. In this paper, two approaches are developed using (a) 2D planar circular (3 UAVs) and (b) 3D tetrahedron formation (4 UAVs). In both approaches, accurate estimation of the gradient vector is crucial for heading angle calculation. Each UAV carries the CZT sensor; the real-time radiation data are used for the calculation of a bulk heading vector for the swarm to achieve a UAV swarm’s source-seeking behavior. Also, a spinning formation is studied for both cases to improve gradient estimation near a radiation source. In the 3D tetrahedron formation, a UAV located closest to the source is designated as a lead unit to maintain the tetrahedron formation in space. Such a formation demonstrated a collective and coordinated movement for estimating a gradient vector for the radiation source and determining an optimal heading direction of the swarm. The proposed radiation localization technique is studied by computer simulation and validated experimentally in the indoor flight testbed using gamma sources. The technology presented in this paper provides the capability to readily add/replace radiation sensors to the UAV platforms in the field conditions enabling extensive condition measurement and greatly improving situational awareness and event management. Furthermore, the proposed radiation localization approach allows long-term measurements to be efficiently performed at wide areas of interest to prevent disasters and reduce dose risks to people and infrastructure.

Keywords: radiation, unmanned aerial system(UAV), source localization, UAV swarm, tetrahedron formation

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Authors: Lutfi Abosrra, Alseddeq Alabaoub, Nuri Elhaloudi

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Corrosion processes in the Oil & Gas industry can lead to failures that are usually costly to repair, costly in terms of loss of contaminated product, in terms of environmental damage and possibly costly in terms of human safety. This article describes the results of the corrosion review and criticality assessment done at Mellitah Gas (SRU unit) for pressure equipment and piping system. The information gathered through the review was intended for developing a qualitative RBI study. The corrosion criticality assessment has been carried out by applying company procedures and industrial recommended practices such as API 571, API 580/581, ASME PCC 3, which provides a guideline for establishing corrosion integrity assessment. The corrosion review is intimately related to the probability of failure (POF). During the corrosion study, the process units are reviewed by following the applicable process flow diagrams (PFDs) in the presence of Mellitah’s personnel from process engineering, inspection, and corrosion/materials and reliability engineers. The expected corrosion damage mechanism (internal and external) was identified, and the corrosion rate was estimated for every piece of equipment and corrosion loop in the process units. A combination of both Consequence and Likelihood of failure was used for determining the corrosion risk. A qualitative consequence of failure (COF) for each individual item was assigned based on the characteristics of the fluid as per its flammability, toxicity, and pollution into three levels (High, Medium, and Low). A qualitative probability of failure (POF)was applied to evaluate the internal and external degradation mechanism, a high-level point-based (0 to 10) for the purpose of risk prioritizing in the range of Low, Medium, and High.

Keywords: corrosion, criticality assessment, RBI, POF, COF

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Authors: Jan Luxa, Vlastimil Mazanek, Petr Malinsky, Alexander Romanenko, Mariapompea Cutroneo, Vladimir Havranek, Josef Novak, Eva Stepanovska, Anna Mackova, Zdenek Sofer

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Using accelerated energetic ions is an interesting method for the introduction of structural changes in various carbon-based materials. This way, the properties can be altered in two ways: a) the ions lead to the formation of conductive pathways in graphene oxide structures due to the elimination of oxygen functionalities and b) doping with selected ions to form metal nanoclusters, thus increasing the conductivity. In this work, energetic beams were employed in two ways to prepare capacitor structures in graphene oxide (GO), reduced graphene oxide (rGO) and polyimide (PI) on a micro-scale. The first method revolved around using ion beam writing with a focused ion beam, and the method involved ion implantation via a polymeric mask. To prepare the polymeric mask, a direct spin-coating of PMMA on top of the foils was used. Subsequently, proton beam writing and development in isopropyl alcohol were employed. Finally, the mask was removed using acetone solvent. All three materials were exposed to ion beams with an energy of 2.5-5 MeV and an ion fluence of 3.75x10¹⁴ cm-² (1800 nC.mm-²). Thus, prepared microstructures were thoroughly characterized by various analytical methods, including Scanning electron microscopy (SEM) with Energy-Dispersive X-ray spectroscopy (EDS), X-ray Photoelectron spectroscopy (XPS), micro-Raman spectroscopy, Rutherford Back-scattering Spectroscopy (RBS) and Elastic Recoil Detection Analysis (ERDA) spectroscopy. Finally, these materials were employed and tested as sensors for humidity using electrical conductivity measurements. The results clearly demonstrate that the type of ions, their energy and fluence all have a significant influence on the sensory properties of thus prepared sensors.

Keywords: graphene, graphene oxide, polyimide, ion implantation, sensors

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Authors: Temitope Olumide Olugbade

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Low resistance and delayed fracture to corrosion, especially in harsh environmental conditions, often limit the wide application of high-manganese (high-Mn) steels. To address this issue, the present work investigates the influence of microalloying on the corrosion properties of high-Mn steels. Microalloyed and base high-Mn steels were synthesized through an arc melting process under an argon atmosphere. To generate different microstructures, the temperature and duration were varied via thermal homogenization treatments. The electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization techniques were used to determine the corrosion properties in 0.6 M NaCl aqueous solution at room temperature. The relationship between the microstructures and corrosion properties was investigated via Scanning Kelvin Probe Microscopy (SKPFM), energy dispersive X-ray spectroscopy (EDX), and Scanning electron microscopy (SEM) techniques. The local corrosion properties were investigated via in situ atomic force spectroscopy (AFM), considering the homogenization treatments. The results indicate that microalloying is a successful technique for enhancing the corrosion behavior of high-Mn steels. Compared to other alloying elements, Vanadium has shown improvement in corrosion properties for both general and local corrosion in chloride environments.

Keywords: corrosion, high-manganese steel, homogenization, microalloying, vanadium

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Authors: Ahmet Mustafa Kangal, Efe Oguzhan Karci, Sinan Oncu

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This research focuses on enhancing cooperative localization for heterogeneous mobile robots composed of a quadcopter and an unmanned ground vehicle. The study employs sensor fusion techniques, particularly the Extended Kalman Filter, to fuse data from various sensors, including GPS, IMU, and camera. By integrating these sensors and optimizing fusion strategies, the research aims to improve the precision and reliability of cooperative localization in complex and dynamic environments. The primary objective is to develop a practical framework for cooperative localization that addresses the challenges posed by the differences in mobility and sensing capabilities among heterogeneous robots. Sensor fusion is used to compensate for the limitations of individual sensors, providing more accurate and robust localization results. Moreover, a comparative analysis of different sensor combinations and fusion strategies helps to identify the optimal configuration for each robot. This research focuses on the improvement of cooperative localization, path planning, and collaborative tasks for heterogeneous robots. The findings have broad applications in fields such as autonomous transportation, agricultural operation and disaster response, where the cooperation of diverse robotic platforms is crucial for mission success.

Keywords: cooperative localization, heterogeneous mobile robots, extended kalman filter, sensor fusion

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Hossein Keshavarz, Alejandro Ramirez-Serrano

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Energy efficiency and locomotion speed are two key parameters for legged robots; thus, finding ways to improve them are important. This paper proposes a locomotion framework to analyze the energy usage and speed of quadruped robots via a Genetic Algorithm (GA) optimization process. For this, a quadruped robot platform with joint redundancy in its hind legs that we believe will help multi-legged robots improve their speed and energy consumption is used. ContinuO, the quadruped robot of interest, has 14 active degrees of freedom (DoFs), including three DoFs for each front leg, and unlike previously developed quadruped robots, four DoFs for each hind leg. ContinuO aims to realize a cost-effective quadruped robot for real-world scenarios with high speeds and the ability to overcome large obstructions. The proposed framework is used to locomote the robot and analyze its energy consumed at diverse stride lengths and locomotion speeds. The analysis is performed by comparing the obtained results in two modes, with and without the joint redundancy on the robot’s hind legs.

Keywords: genetic algorithm optimization, locomotion path planning, quadruped robots, redundant legs

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Authors: Romisaa Ali

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This paper presents a comparison between twin-delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environmental complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios.

Keywords: Jackal robot environments, reinforcement learning, TD3, SAC, robust navigation, transferability, custom environment

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Authors: Kaiyuan Chena, Senentxu Lanceros-Méndeza, Laijun Liub, Qi Zhanga

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0.6Bi(Mg1/2Ti1/2)O3-0.4Ba0.8Ca0.2(Nb0.125Ti0.875)O3 (0.6BMT-0.4BCNT) ceramics with a pseudo-cubic structure and re-entrant dipole glass behavior have been investigated via X-ray diffraction and dielectric permittivity-temperature spectra. It shows an excellent dielectric-temperature stability with small variations of dielectric permittivity (± 5%, 420 - 802 K) and dielectric loss tangent (tanδ < 2.5%, 441 - 647 K) in a wide temperature range. Three dielectric anomalies are observed from 290 K to 1050 K. The low-temperature weakly coupled re-entrant relaxor behavior was described using Vogel-Fulcher law and the new glass model. The mid- and high-temperature dielectric anomalies are characterized by isothermal impedance and electrical modulus. The activation energy of both dielectric relaxation and conductivity follows the Arrhenius law in the temperature ranges of 633 - 753 K and 833 - 973 K, respectively. The ultrahigh thermal stability of the dielectric permittivity is attributed to the weakly coupling of polar clusters, the formation of diffuse phase transition (DPT) and the local phase transition of calcium-containing perovskite.

Keywords: permittivity, relaxor, electronic ceramics, activation energy

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Authors: Bekhedda Kheira

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Rare earth-doped luminescent materials (Ce, Eu, Yb, Tb, etc.) are now widely used in flat-screen displays, fluorescent lamps, and photovoltaic solar cells. They exhibit several fine emission bands in a spectral range from near UV to infrared when added to inorganic materials. This study chose cerium oxide (CeO2) because of its exceptional intrinsic properties, energy levels, and ease of implementation of doped layer synthesis. In this study, thin films were obtained by the evaporation deposition technique of cerium oxide (CeO2) on silicon Nitride (SiNx) layers and then annealing under nitrogen N2. The characterization of these films was carried out by different techniques, scanning electron microscopy (SEM) to visualize morphological properties and (EDS) was used to determine the elemental composition of individual dots, optical analysis characterization of thin films was studied by a spectrophotometer in reflectance mode to determine different energies gap of the nanostructured layers and to adjust these values for the photovoltaic application.

Keywords: thin films, photovoltaic, rare earth, evaporation

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Authors: Innocent O. Arukalam, Emeka E. Oguzie

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Interest in the development of eco-friendly anti-corrosion coatings using bio-based renewable materials is gaining momentum recently. To this effect, chitin biopolymer, which is non-toxic, biodegradable, and inherently possesses anti-microbial property, was successfully synthesized from snail shells and used as a filler in the preparation of epoxy coating. The chitin particles were characterized with contact angle goniometer, scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectrophotometer, and X-ray diffractometer (XRD). The performance of the coatings was evaluated by immersion and electrochemical impedance spectroscopy (EIS) tests. Electronic structure properties of the coating ingredients and molecular level interaction of the corrodent and coated Q235 steel were appraised by quantum chemical computations (QCC) and molecular dynamics (MD) simulation techniques, respectively. The water contact angle (WCA) measurement of chitin particles was found to be 129.3o while that of chitin particles modified with amino trimethoxy silane (ATMS) was 149.6o, suggesting it is highly hydrophobic. Immersion and EIS analyses revealed that epoxy coating containing silane-modified chitin exhibited lowest water absorption and highest barrier as well as anti-corrosion performances. The QCC showed that quantum parameters for the coating containing silane-modified chitin are optimum and therefore corresponds to high corrosion protection. The high negative value of adsorption energies (Eads) for the coating containing silane-modified chitin indicates the coating molecules interacted and adsorbed strongly on the steel surface. The observed results have shown that silane-modified epoxy-chitin coating would perform satisfactorily for surface protection of metal structures in saline environment.

Keywords: chitin, EIS, epoxy coating, hydrophobic, molecular dynamics simulation, quantum chemical computation

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Authors: Suleyman Karabal, Timothy L. Burnett, Egemen Avcu, Andrew H. Sherry, Philip J. Withers

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SA508 Grade 3 steel is widely used in the construction of nuclear pressure vessels, where its fracture toughness plays a critical role in ensuring operational safety and reliability. Understanding the ductile failure mechanisms in this steel grade is crucial for designing robust pressure vessels that can withstand severe nuclear environment conditions. In the present study, round bar specimens of SA508 Grade 3 steel with four distinct notch geometries were subjected to tensile loading while capturing continuous 2D images at 5-second intervals in order to monitor any alterations in their geometries to construct true stress-strain curves of the specimens. 3D reconstructions of X-ray computed tomography (CT) images at high-resolution (a spatial resolution of 0.82 μm) allowed for a comprehensive assessment of the influences of second-phase particles (i.e., manganese sulfide inclusions and cementite particles) on ductile failure initiation as a function of applied plastic strain. Additionally, based on 2D and 3D images, plasticity modeling was executed, and the results were compared to experimental data. A specific ‘two-parameter criterion’ was established and calibrated based on the correlation between stress triaxiality and equivalent plastic strain at failure initiation. The proposed criterion demonstrated substantial agreement with the experimental results, thus enhancing our knowledge of ductile fracture behavior in this steel grade. The implementation of X-ray CT and fractography analysis provided new insights into the diverse roles played by different populations of second-phase particles in fracture initiation under varying stress triaxiality conditions.

Keywords: ductile fracture, two-parameter criterion, x-ray computed tomography, stress triaxiality

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Authors: Narges Shayesteh Moghaddam

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Tungsten (W), a refractory metal known for its remarkably high melting temperature, offers tremendous potential for use in challenging environments prevalent in sectors such as space exploration, defense, and nuclear industries. Additive manufacturing, especially the Laser Powder-Bed Fusion (LPBF) technique, emerges as a beneficial method for fabricating tungsten parts. This technique enables the production of intricate components while simultaneously reducing production lead times and associated costs. However, the inherent brittleness of tungsten and its tendency to crack under high-temperature conditions pose significant challenges to the manufacturing process. Our research primarily focuses on the process of rolling tungsten parts in a layer-by-layer manner in LPBF and the subsequent changes in microstructure. Our objective is not only to identify the alterations in the microstructure but also to assess their implications on the physical properties and performance of the fabricated tungsten parts. To examine these aspects, we conducted an extensive series of experiments that included the fabrication of tungsten samples through LPBF and subsequent characterization using advanced materials analysis techniques. These investigations allowed us to scrutinize shifts in various microstructural features, including, but not limited to, grain size and grain boundaries occurring during the rolling process. The results of our study provide crucial insights into how specific factors, such as plastic deformation occurring during the rolling process, influence the microstructural characteristics of the fabricated parts. This information is vital as it provides a foundation for understanding how the parameters of the layer-by-layer rolling process affect the final tungsten parts. Our research significantly broadens the current understanding of microstructural evolution in tungsten parts produced via the layer-by-layer rolling process in LPBF. The insights obtained will play a pivotal role in refining and optimizing manufacturing parameters, thus improving the mechanical properties of tungsten parts and, therefore, enhancing their performance. Furthermore, these findings will contribute to the advancement of manufacturing techniques, facilitating the wider application of tungsten parts in various high-demand sectors. Through these advancements, this research represents a significant step towards harnessing the full potential of tungsten in high-temperature and high-stress applications.

Keywords: additive manufacturing, rolling, tungsten, refractory materials

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Umair Rashid

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Normally softmax loss is used as the supervision signal in face recognition (FR) system, and it boosts the separability of features. In the last two years, a number of techniques have been proposed by reformulating the original softmax loss to enhance the discriminating power of Deep Convolutional Neural Networks (DCNNs) for FR system. To learn angularly discriminative features Cosine-Margin based softmax has been adjusted as monotonically decreasing angular function, that is the main challenge for angular based softmax. On that issue, we propose monotonically decreasing element for Cosine-Margin based softmax and also, we discussed the effect of different monotonically decreasing parameters on angular Margin softmax for FR system. We train the model on publicly available dataset CASIA- WebFace via our proposed monotonically decreasing parameters for cosine function and the tests on YouTube Faces (YTF, Labeled Face in the Wild (LFW), VGGFace1 and VGGFace2 attain the state-of-the-art performance.

Keywords: deep convolutional neural networks, cosine margin face recognition, softmax loss, monotonically decreasing parameter

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Authors: Anuoluwapo S. Taiwo, David S. Ayre, Morteza Khorami, Sameer S. Rahatekar

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This investigation aims to characterize the effect of Corn Cob (CC), an agricultural waste, for potential use as a filler material, reducing cement in natural fibre-reinforced cement composite boards used for building applications in low-cost housing estates in developing countries. The corn cob, an agro-waste, is readily and abundantly available in many West African States. However, this agricultural waste product has not been put to any effective use. Hence, the objective of the current research is to convert this massive agro-waste resource into a potential material for use as partial cement replacement in fibre-cement board production. Kraft pulp fibre-reinforced cement composite boards were developed with the incorporation of the corn cob at a varying percentage of 1 - 4 wt.% as partial cement replacement using a laboratory-simulated Hatschek process. The mechanical properties of the developed cement boards were characterized through a three-point bending test, while the fractured morphology of the cement boards was examined through a scanning electron microscope (SEM). Results revealed that the flexural strength of the composite board improved significantly with an optimum enhancement of 40% when compared to the reference sample without corn cob replacement; however, the flexural behaviour (toughness) of the composite board was slightly affected by the addition of the corn cob. SEM observation of the fractured surfaces revealed good bonding at the fibre-matrix interface as well as a ductile-to-brittle fracture mechanism. Overall, the composite board incorporated with 2 wt.% corn cob replacement for cement had the optimum properties, which satisfied the minimum requirements of relevant standards for fibre cement flat sheets.

Keywords: agricultural waste, building applications, fibre-cement board, kraft pulp fibre, sustainability

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

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Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

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Authors: Muneeb Ahmad, Ali Raza

Abstract:

A computational method inspired by the immune system (IS) is presented, leveraging its shared characteristics of robustness, fault tolerance, scalability, and adaptability with swarm intelligence. This method aims to showcase flocking behaviors in a swarm of robots (SR). The innate part of the IS offers a variety of reactive and probabilistic cell functions alongside its self-regulation mechanism which have been translated to enable swarming behaviors. Although, the research is specially focused on flocking behaviors in a variety of simulated environments using e-puck robots in a physics-based simulator (CoppeliaSim); the artificial innate immune system (AIIS) can exhibit other swarm behaviors as well. The effectiveness of the immuno-inspired approach has been established with extensive experimentations, for scalability and adaptability, using standard swarm benchmarks as well as the immunological regulatory functions (i.e., Dendritic Cells’ Maturity and Inflammation). The AIIS-based approach has proved to be a scalable and adaptive solution for emulating the flocking behavior of SR.

Keywords: artificial innate immune system, flocking swarm, immune system, swarm intelligence

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Authors: Behnaz Sohani, Ifeoluwa Joshua Adigun, Amir Rahmani, Khaled Goher

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This paper provides initial results on the efficacy of the designed ergonomic-oriented neck support to mitigate and alleviate tension neck syndrome musculoskeletal disorder. This is done using both simulations and measurements. Tension Neck Syndrome Musculoskeletal Disorder (TNS MSD) causes discomfort in the muscles around the neck and shoulder. TNS MSD is one of the leading causes of early retirement. This research focuses on the design of an adaptive neck supporter by integrating a soft actuator massager to help deliver a soothing massage. The massager and adaptive neck supporter prototype were validated by finite element analysis prior to fabrication to envisage the feasibility of the design concept. Then a prototype for the massager was fabricated and tested for concept validation. Future work will be focused on fabricating the full-scale prototype and upgrading and optimizing the design concept for the adaptive neck supporter.

Keywords: adaptive neck supporter, tension neck syndrome, musculoskeletal disorder, soft actuator massager, soft robotics

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Authors: Mostafa Vahdani, Mohsen Asadnia, Shuying Wu

Abstract:

Huge amounts of e-waste are being produced by the rapidly expanding use of electronics; the majority of this material is either burned or dumped directly in landfills since recycling would either be impracticable or too expensive. Degradable and environmentally friendly materials are therefore seen as the answer to this urgent problem. Here, we create strain sensors that are biodegradable, robust, and incredibly flexible using thin films of sodium carboxymethyl cellulose (NaCMC), glycerol, and polyvinyl alcohol (PVA). Due to the creation of many inter- or intramolecular hydrogen bonds, the polymer blends (NaCMC/PVA/glycerol) exhibit a failure strain of up to 330% and negligible hysteresis when exposed to cyclic stretching-releasing. What's more intriguing is that the sensors can degrade completely in deionized water at a temperature of 95 °C in about 25 minutes. This project illustrates a novel method for developing wearable electronics that are environmentally beneficial.

Keywords: degradable, stretchable, strain sensors, wearable electronics.

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Authors: Berkas Khaoula

Abstract:

Based on selection characteristics, high-density polyethylene (HDPE) extruded pipes are among the most economical and durable materials as well-designed solutions for water and gas transmission systems. The main reasons for such a choice are the high quality-performance ratio and the long-term service durability under aggressive conditions. Due to inevitable interactions with soils of different chemical compositions and transported fluids, aggressiveness becomes a key factor in studying resilient strength and life prediction limits. This phenomenon is known as environmental stress cracking resistance (ESCR). In this work, the effect of 3 acidic environments (5% acetic, 20% hydrochloric and 20% sulfuric) on HDPE-100 samples (~10x11x24 mm3). The results presented in the form (Δm/m0, %) as a function of √t indicate that the absorption, in the case of strong acids (HCl and H2SO4), evolves towards negative values involving material losses such as antioxidants and some additives. On the other hand, acetic acid and deionized water (DW) give a form of linear Fickean (LF) and B types, respectively. In general, the acids cause a slow but irreversible alteration of the chemical structure, composition and physical integrity of the polymer. The DW absorption is not significant (~0.02%) for an immersion duration of 69 days. Such results are well accepted in actual applications, while changes caused by acidic environments are serious and must be subjected to particular monitoring of the OIT factor (Oxidation Induction Time). After 55 days of aging, the H2SO4 and HCl media showed particular values with a loss of % mass in the interval [0.025-0.038] associated with irreversible chemical reactions as well as physical degradations. This state is usually explained by hydrolysis of the polymer, causing the loss of functions and causing chain scissions. These results are useful for designing and estimating the lifetime of the tube in service and in contact with adverse environments.

Keywords: HDPE, environmental stress cracking, absorption, acid media, chemical aging

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Authors: Giuseppe Spampinato, Arcangelo Bruna, Antonin Martin-Schouler, Cedric Colombo

Abstract:

This paper describes a solution in the field of navigation systems, in particular for obstacle avoidance with Time-of-Flight (TOF) sensor(s). It is inspired by a “divide and conquer” like idea. Obstacle avoidance consists in choosing the correct robot rotation angle to avoid impact with (moving) objects. The solution used is based on a simple state machine based only on “turn” and “go straight” commands. The system resets the chosen direction each time the robot moves forward to avoid potential rotation loops and unpleasant rotations. Moreover, the process consists in dividing into N vertical parts the TOF(s) we are considering and calculating separately a measure of each part. The combination of these values (the measure calculated on each part and chosen direction) will determine the output rotation to avoid the obstacle. Moreover, a peculiar pre-processing is done at the beginning to increase performance. The system works in real-time also in a dynamic environment and can be easily adapted to different TOF-like sensors.

Keywords: robot navigation, obstacle detection, obstacle avoidance, time of flight, divide and conquer

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Authors: Nilki Upathissa, Torin Wirasinghe

Abstract:

This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: N. Naz, A. D. Domenico, M. N. Huda

Abstract:

Soft Robotics is a rapidly growing multidisciplinary field where robots are fabricated using highly deformable materials motivated by bioinspired designs. The high dexterity and adaptability to the external environments during contact make soft robots ideal for applications such as gripping delicate objects, locomotion, and biomedical devices. The actuation system of soft robots mainly includes fluidic, tendon-driven, and smart material actuation. Among them, Soft Pneumatic Actuator, also known as SPA, remains the most popular choice due to its flexibility, safety, easy implementation, and cost-effectiveness. However, at present, most of the fabrication of SPA is still based on traditional molding and casting techniques where the mold is 3d printed into which silicone rubber is cast and consolidated. This conventional method is time-consuming and involves intensive manual labour with the limitation of repeatability and accuracy in design. Recent advancements in direct 3d printing of different soft materials can significantly reduce the repetitive manual task with an ability to fabricate complex geometries and multicomponent designs in a single manufacturing step. The aim of this research work is to design and analyse the Soft Pneumatic Actuator (SPA) utilizing both conventional casting and modern direct 3d printing technologies. The mold of the SPA for traditional casting is 3d printed using fused deposition modeling (FDM) with the polylactic acid (PLA) thermoplastic wire. Hyperelastic soft materials such as Ecoflex-0030/0050 are cast into the mold and consolidated using a lab oven. The bending behaviour is observed experimentally with different pressures of air compressor to ensure uniform bending without any failure. For direct 3D-printing of SPA fused deposition modeling (FDM) with thermoplastic polyurethane (TPU) and stereolithography (SLA) with an elastic resin are used. The actuator is modeled using the finite element method (FEM) to analyse the nonlinear bending behaviour, stress concentration and strain distribution of different hyperelastic materials after pressurization. FEM analysis is carried out using Ansys Workbench software with a Yeon-2nd order hyperelastic material model. FEM includes long-shape deformation, contact between surfaces, and gravity influences. For mesh generation, quadratic tetrahedron, hybrid, and constant pressure mesh are used. SPA is connected to a baseplate that is in connection with the air compressor. A fixed boundary is applied on the baseplate, and static pressure is applied orthogonally to all surfaces of the internal chambers and channels with a closed continuum model. The simulated results from FEM are compared with the experimental results. The experiments are performed in a laboratory set-up where the developed SPA is connected to a compressed air source with a pressure gauge. A comparison study based on performance analysis is done between FDM and SLA printed SPA with the molded counterparts. Furthermore, the molded and 3d printed SPA has been used to develop a three-finger soft pneumatic gripper and has been tested for handling delicate objects.

Keywords: finite element method, fused deposition modeling, hyperelastic, soft pneumatic actuator

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