Search results for: cone of vision

Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

Procedia PDF Downloads 222

Authors: Cormac Reale, Luke J. Prendergast, Kenneth Gavin

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While significant improvements have been made in axial pile design methods over recent years, the influence of soils natural variability has not been adequately accounted for within them. Soil variability is a crucial parameter to consider as it can account for large variations in pile capacity across the same site. This paper seeks to address this knowledge deficit, by demonstrating how soil spatial variability can be accommodated into existing cone penetration test (CPT) based pile design methods, in the form of layered non-homogeneous random fields. These random fields model the scope of a given property’s variance and define how it varies spatially. A Monte Carlo analysis of the pile will be performed taking into account parameter uncertainty and spatial variability, described using the measured scales of fluctuation. The results will be discussed in light of Eurocode 7 and the effect of spatial averaging on design capacities will be analysed.

Keywords: pile axial design, reliability, spatial variability, CPT

Procedia PDF Downloads 231

Authors: Awni H. Alkhazaleh, Baljinder K. Kandola

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In this study, a form-stable composite Paraffin/Nanoclay (PA-NC) has been prepared by absorbing PA into porous particles of NC to be used for low-temperature latent heat thermal energy storage. The leakage test shows that the maximum mass fraction of PA that can be incorporated in NC without leakage is 60 wt.%. Differential scanning calorimetry (DSC) has been used to measure the thermal properties of the PA and PA-NC both before and after incorporation in plasterboard (PL). The mechanical performance of the samples has been evaluated in flexural mode. The thermal energy storage performance has been studied using a small test chamber (100 mm × 100 mm × 100 mm) made from 10 mm thick PL and measuring the temperatures using thermocouples. The flammability of the PL+PL-NC has been discussed using a cone calorimeter. The results indicate that the form composite PA has good potential for use as thermal energy storage materials in building applications.

Keywords: building materials, flammability, phase change materials, thermal energy storage

Procedia PDF Downloads 316

Authors: Molakala Mourya Vardhan Reddy, Kandimala Revanth, Koduru Sumanth, Beena B. M.

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Emergency response and recovery operations are severely hampered by natural catastrophes, especially floods. Understanding post-flood scenarios is essential to disaster management because it facilitates quick evaluation and decision-making. To this end, we introduce FloodNet, a brand-new high-resolution aerial picture collection created especially for comprehending post-flood scenes. A varied collection of excellent aerial photos taken during and after flood occurrences make up FloodNet, which offers comprehensive representations of flooded landscapes, damaged infrastructure, and changed topographies. The dataset provides a thorough resource for training and assessing computer vision models designed to handle the complexity of post-flood scenarios, including a variety of environmental conditions and geographic regions. Pixel-level semantic segmentation masks are used to label the pictures in FloodNet, allowing for a more detailed examination of flood-related characteristics, including debris, water bodies, and damaged structures. Furthermore, temporal and positional metadata improve the dataset's usefulness for longitudinal research and spatiotemporal analysis. For activities like flood extent mapping, damage assessment, and infrastructure recovery projection, we provide baseline standards and evaluation metrics to promote research and development in the field of post-flood scene comprehension. By integrating FloodNet into machine learning pipelines, it will be easier to create reliable algorithms that will help politicians, urban planners, and first responders make choices both before and after floods. The goal of the FloodNet dataset is to support advances in computer vision, remote sensing, and disaster response technologies by providing a useful resource for researchers. FloodNet helps to create creative solutions for boosting communities' resilience in the face of natural catastrophes by tackling the particular problems presented by post-flood situations.

Keywords: image classification, segmentation, computer vision, nature disaster, unmanned arial vehicle(UAV), machine learning.

Procedia PDF Downloads 55

Authors: Hebah M. Musalem, Jeylan El-Mansoury, Lin M. Tuleimat, Selwa Alhazza, Abdul-Aziz A. Al Zoba

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Background: Amblyopia is a condition that affects the visual system triggering a decrease in visual acuity without a known underlying pathology. It is due to abnormal vision development in childhood or infancy. Most importantly, vision loss is preventable or reversible with the right kind of intervention in most of the cases. Strabismus, sensory defects, and anisometropia are all well-known causes of amblyopia. However, ocular misalignment in Strabismus is considered the most common form of amblyopia worldwide. The risk of developing amblyopia increases in premature children, developmentally delayed or children who had brain lesions affecting the visual pathway. The prevalence of amblyopia varies between 2 to 5 % in the world according to the literature. Objective: To determine the different causes of Amblyopia in pediatric patients seen in ophthalmology clinic of a tertiary care center, i.e. King Faisal Specialist Hospital and Research Center (KFSH&RC). Methods: This is a hospital based, random retrospective, based on reviewing patient’s files in the Ophthalmology Department of KFSH&RC in Riyadh city, Kingdom of Saudi Arabia. Inclusion criteria: amblyopic pediatric patients who attended the clinic from 2015 to 2016, who are between 6 months and 18 years old. Exclusion Criteria: patients above 18 years of age and any patient who is uncooperative to obtain an accurate vision or a proper refraction. Detailed ocular and medical history are recorded. The examination protocol includes a full ocular exam, full cycloplegic refraction, visual acuity measurement, ocular motility and strabismus evaluation. All data were organized in tables and graphs and analyzed by statistician. Results: Our preliminary results will be discussed on spot by our corresponding author. Conclusions: We focused on this study on utilizing various examination techniques which enhanced our results and highlighted a distinguished correlation between amblyopia and its’ causes. This paper recommendation emphasizes on critical testing protocols to be followed among amblyopic patient, especially in tertiary care centers.

Keywords: amblyopia, amblyopia causes, amblyopia diagnostic criterion, amblyopia prevalence, Saudi Arabia

Procedia PDF Downloads 141

Authors: A. Degale Desta, Cheng Jian

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

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

Procedia PDF Downloads 143

Authors: Sopio Gogilava, Goga Chakhaia, Levan Tsulukidze, Zurab Laoshvili, Irina Khubulava, Shalva Bosikashvili, Teimuraz Gugushvili

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Geoinformatics systems (GIS) integrated computer program RAMMS is widely used for forecasting debris flows and accordingly for the determination of anticipating risks with 85% accuracy. In view of the above, the work introduces new capabilities of the computer program RAMMS, which evaluates the effectiveness of anti-debris flow engineering construction, namely: the possibility of decreasing the expected velocity, kinetic energy, and output cone volume in the Mletiskhevi River. As a result of research has been determined that the anti-debris flow engineering construction designed to reduce the expected debris flow risk in the Mletiskhevi River is an effective environmental protection technology, that's why its introduction is promising.

Keywords: construction, debris flow, geoinformatics systems, program RAMMS

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Authors: Abbas Moradi, Mohsen Safajoy, Reza Yazdanparast

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Analysis of blade-disc dovetail joints is one of the biggest challenges facing designers of aero-engines. To avoid comparatively expensive experimental full-scale tests, numerical methods can be used to simulate loaded disc-blades assembly. Mortar method provides a powerful and flexible tool for solving frictional contact problems. In this study, 2D frictional contact in dovetail has been analysed based on the mortar algorithm. In order to model the friction, the classical law of coulomb and moving friction cone algorithm is applied. The solution is then obtained by solving the resulting set of non-linear equations using an efficient numerical algorithm based on Newton–Raphson Method. The numerical results show that this approach has better convergence rate and accuracy than other proposed numerical methods.

Keywords: computational contact mechanics, dovetail joints, nonlinear FEM, mortar approach

Procedia PDF Downloads 332

Authors: Amanda R. Teixeira, João H. S. Rego, Gabriel F. S. Brito, Fabricio M. Silva

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The use of superplasticizer additives has provided several advances for the civil construction industry, enabling gains in the rheological behavior and mechanical properties of cementitious matrices. These compounds act at the solid-liquid interface of colloidal suspensions of cement pastes, preventing agglomeration of the particles. Although the use in the concrete industry is wide, the mechanisms of dispersion of concrete admixtures composed of polycarboxylate in cement with supplementary cementitious materials have ample opportunity to be investigated, providing the attainment of increasingly compatible and efficient cement-addition-additive systems. The cements used in the research are Portland Cement CPV and two cements Portland Cement Composite (CPIV) with calcined clay contents of 20% and 28% and three commercial additives based on polycarboxylate. The performance of the additives and obtaining the optimal content was determined by the Marsh Cone test and spread by Mini-Slump.

Keywords: calcined clay, composite cements, superplasticizer additives, polycarboxylate

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Authors: Haibo Sheng, Yuan Hu

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Spinel structure Zinc stannate (Zn2SnO4, ZS)/Graphene was successfully synthesized by a simple in situ hydrothermal route. Morphological study and structure analysis confirmed the homogenously loading of ZS on the graphene sheets. Then, the resulted ZS/graphene hybrids were incorporated into epoxy resin to form EP/ZS/graphene composites by a solvent dispersion method. Improved thermal stability was investigated by Thermogravimetric Analysis (TGA). Cone calorimeter result showed low peak heat release rate (PHRR). Toxical gases release during combustion was evaluated by a facile device organized in our lab. The results showed that the release of NOx, HCN decrease of about 55%. Also, TG-IR technology was used to investigate the gas release during the EP decomposition process. The CO release had decreased about 80%.The EP/G/ZS showed lowest hazards during combustion (including flame retardancy, thermal stability, lower toxical gases release and so on) than pure EP.

Keywords: fire hazards, zinc stannate, epoxy resin, toxical gas hazards

Procedia PDF Downloads 170

Authors: Kittiphat Rattanachan

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The sheet metal forming process, the raw material mechanical properties are important parameters. This paper is to compare the wall’s incline angle or formability of SS 400 steel and SUS 304 stainless steel in single point incremental forming. The two materials are ferrous base alloy, which have the different cell unit, mechanical property and chemical composition. They were forming into cone shape specimens 100 mm diameter with different wall’s incline angle: 90o, 75o, and 60o. The investigation, the specimens were forming until the surface fracture was occurred. The experimental result showed that both materials with the smaller wall’s incline angle, the higher formability. The formability limited of the ferrous base alloy was approx. 60o wall’s incline angle. By nature, SS 400 was higher formability than SUS 304. This result could be used as the initial utilized data in designing the single point incremental forming parts.

Keywords: NC incremental forming, single point incremental forming, wall incline angle, formability

Procedia PDF Downloads 329

Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

Procedia PDF Downloads 88

Authors: Alexandre Faure, Yoko Mizokami, éRic Dinet

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These recent years, the potential of light in assisting visually impaired people in their indoor mobility has been demonstrated by different studies. Implementing smart lighting systems for selective visual enhancement, especially designed for low-vision people, is an approach that breaks with the existing visual aids. The appearance of the surface of an object is significantly influenced by the lighting conditions and the constituent materials of the objects. Appearance of objects may appear to be different from expectation. Therefore, lighting conditions lead to an important part of accurate material recognition. The main objective of this work was to investigate the effect of the spatial distribution of light on object identification in the context of low vision. The purpose was to determine whether and what specific lighting approaches should be preferred for visually impaired people. A psychophysical experiment was designed to study the ability of individuals to identify the smallest cube of a pair under different lighting diffusion conditions. Participants were divided into two distinct groups: a reference group of observers with normal or corrected-to-normal visual acuity and a test group, in which observers were required to wear visual impairment simulation glasses. All participants were presented with pairs of cubes in a "miniature room" and were instructed to estimate the relative size of the two cubes. The miniature room replicates real-life settings, adorned with decorations and separated from external light sources by black curtains. The correlated color temperature was set to 6000 K, and the horizontal illuminance at the object level at approximately 240 lux. The objects presented for comparison consisted of 11 white cubes and 11 black cubes of different sizes manufactured with a 3D printer. Participants were seated 60 cm away from the objects. Two different levels of light diffuseness were implemented. After receiving instructions, participants were asked to judge whether the two presented cubes were the same size or if one was smaller. They provided one of five possible answers: "Left one is smaller," "Left one is smaller but unsure," "Same size," "Right one is smaller," or "Right one is smaller but unsure.". The method of constant stimuli was used, presenting stimulus pairs in a random order to prevent learning and expectation biases. Each pair consisted of a comparison stimulus and a reference cube. A psychometric function was constructed to link stimulus value with the frequency of correct detection, aiming to determine the 50% correct detection threshold. Collected data were analyzed through graphs illustrating participants' responses to stimuli, with accuracy increasing as the size difference between cubes grew. Statistical analyses, including 2-way ANOVA tests, showed that light diffuseness had no significant impact on the difference threshold, whereas object color had a significant influence in low vision scenarios. The first results and trends derived from this pilot experiment clearly and strongly suggest that future investigations could explore extreme diffusion conditions to comprehensively assess the impact of diffusion on object identification. For example, the first findings related to light diffuseness may be attributed to the range of manipulation, emphasizing the need to explore how other lighting-related factors interact with diffuseness.

Keywords: Lighting, Low Vision, Visual Aid, Object Identification, Psychophysical Experiment

Procedia PDF Downloads 48

Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

Procedia PDF Downloads 94

Authors: Sana Abusin

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Food security in Qatar is a research priority of Qatar University (2021-2025) and all national strategies, including the Qatar National Vision 2030 and food security strategy (2018-2023). Achieving food security requires three actions: 1) transforming surplus food to those who are insecure; 2) reducing food loss and waste by recycling food into valuable resources such as compost (“green fertilizer”) that can be used in growing food; and, finally, 3) establishing strong enforcement agencies to protect consumers from outdated food and promote healthy food. Currently, these objectives are approached separately and not in a sustainable fashion. Food security in Qatar is a research priority of Qatar University (2021-2025) and all national strategies, including the Qatar National Vision 2030 and food security strategy (2018-2023). The study aims to develop an innovative mobile application that supports a sustainable solution to food insecurity and food waste in Qatar. The application will provide a common solution for many different users. For producers, it will facilitate easy disposal of excess food. For charities, it will notify them about surplus food ready for redistribution. The application will also benefit the second layer of end-users in the form of food recycling companies, who will receive information about available food waste that is unable to be consumed. We will use self-exoplanetary diagrams and digital pictures to show all the steps to the final stage. The aim is to motivate the young generation toward innovation and creation, and to encourage public-private collaboration in this sector.

Keywords: food security, innovative technology, sustainability, food waste, Qatar

Procedia PDF Downloads 102

Authors: Diane Ghanem, Arjun Gupta, Rohan Vijayan, Ali Uneri, Babar Shafiq

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Introduction: Ankle sprains and fractures often result in syndesmosis injuries. Unstable syndesmotic injuries result from relative motion between the distal ends of the tibia and fibula, anatomic juncture which should otherwise be rigid, and warrant operative management. Clinical and radiological evaluations of intraoperative syndesmosis stability remain a challenging task as traditional 2D fluoroscopy is limited to a uniplanar translational displacement. The purpose of this pilot cadaveric study is to compare the 2D fluoroscopy and 3D cone beam computed tomography (CBCT) stress-induced syndesmosis displacements. Methods: Three fresh-frozen lower legs underwent 2D fluoroscopy and 3D CIOS CBCT to measure syndesmosis position before dissection. Syndesmotic injury was simulated by resecting the (1) anterior inferior tibiofibular ligament (AITFL), the (2) posterior inferior tibiofibular ligament (PITFL) and the inferior transverse ligament (ITL) simultaneously, followed by the (3) interosseous membrane (IOM). Manual external rotation and Cotton stress test were performed after each of the three resections and 2D and 3D images were acquired. Relevant 2D and 3D parameters included the tibiofibular overlap (TFO), tibiofibular clear space (TCS), relative rotation of the fibula, and anterior-posterior (AP) and medial-lateral (ML) translations of the fibula relative to the tibia. Parameters were measured by two independent observers. Inter-rater reliability was assessed by intraclass correlation coefficient (ICC) to determine measurement precision. Results: Significant mismatches were found in the trends between the 2D and 3D measurements when assessing for TFO, TCS and AP translation across the different resection states. Using 3D CBCT, TFO was inversely proportional to the number of resected ligaments while TCS was directly proportional to the latter across all cadavers and ‘resection + stress’ states. Using 2D fluoroscopy, this trend was not respected under the Cotton stress test. 3D AP translation did not show a reliable trend whereas 2D AP translation of the fibula was positive under the Cotton stress test and negative under the external rotation. 3D relative rotation of the fibula, assessed using the Tang et al. ratio method and Beisemann et al. angular method, suggested slight overall internal rotation with complete resection of the ligaments, with a change < 2mm - threshold which corresponds to the commonly used buffer to account for physiologic laxity as per clinical judgment of the surgeon. Excellent agreement (>0.90) was found between the two independent observers for each of the parameters in both 2D and 3D (overall ICC 0.9968, 95% CI 0.995 - 0.999). Conclusions: The 3D CIOS CBCT appears to reliably depict the trend in TFO and TCS. This might be due to the additional detection of relevant rotational malpositions of the fibula in comparison to the standard 2D fluoroscopy which is limited to a single plane translation. A better understanding of 3D imaging may help surgeons identify the precise measurements planes needed to achieve better syndesmosis repair.

Keywords: 2D fluoroscopy, 3D computed tomography, image processing, syndesmosis injury

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Authors: Raz Flieshman, Adam Michael Altenbuchner, Jörg Krüger

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This paper presents an approach to optimizing the Fused Deposition Modeling (FDM) 3D printing process through a preprocess calibration routine of printing parameters. The core of this method involves the use of a low-cost thermal sensor capable of measuring tempera-tures within the range of -20 to 500 degrees Celsius for detailed process observation. The calibration process is conducted by printing a predetermined path while varying the process parameters through machine instructions (g-code). This enables the extraction of critical thermal, dimensional, and surface properties along the printed path. The calibration routine utilizes computer vision models to extract features and metrics from the thermal images, in-cluding temperature distribution, layer adhesion quality, surface roughness, and dimension-al accuracy and consistency. These extracted properties are then analyzed to optimize the process parameters to achieve the desired qualities of the printed material. A significant benefit of this calibration method is its potential to create printing parameter profiles for new polymer and composite materials, thereby enhancing the versatility and application range of FDM 3D printing. The proposed method demonstrates significant potential in enhancing the precision and reliability of FDM 3D printing, making it a valuable contribution to the field of additive manufacturing.

Keywords: FDM 3D printing, preprocess calibration, thermal sensor, process optimization, additive manufacturing, computer vision, material profiles

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Authors: Shuruq Ghazi Nahhas

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Islamic art is a decorative art that depends on repeating motifs in various shapes to cover different surfaces. Each motif has its own characteristics and style that may reflect different Islamic periods, such as Umayyad, Abbasid, Fatimid, Seljuk, Nasrid, Ottoman, and Safavid. These periods were the most powerful periods which played an important role in developing the Islamic motifs. Most of these motifs of the Islamic heritage were not used in new applications. This research focused on reviving the vegetal Islamic motifs found on Islamic heritage and redesign them in a new format to decorate various products, including scarfs, cushions, coasters, wallpaper, wall art, and boxes. The scarf is chosen as one element of these decorative products because it is used as accessories to add aesthetic value to fashion. A descriptive-analytical method is used for this research. The process started with extracting and analyzing the original motifs. Then, creating the new motifs by simplifying, deleting, or adding elements based on the original structure. Then, creating repeated patterns and applying them to decorative products. The findings of this research indicated: repeating patterns based on different structures creates unlimited patterns. Also, changing the elements of the motifs of a pattern adds new characteristics to the pattern. Also, creating frames using elements from the repeated motifs adds aesthetic and contemporary value to decorative products. Finally, using various methods of combining colors creates unlimited variations of each pattern. At the end, reviving the Islamic motifs in contemporary vision enriches decorative products with aesthetic, artistic, and historical values of different Islamic periods. This makes the decorative products valuable that adds uniqueness to their surroundings.

Keywords: Islamic motifs, contemporary patterns, scarfs, decorative products

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Authors: Siti Nur Aida Mario, Farah Hafifee Ahmad, Rudy Tawie

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Soil Stabilization has been widely implemented in the construction industry nowadays. Peat soil is well known as one of the most problematic soil among the engineers. The procedures need to take into account both physical and engineering properties of the stabilized peat soil. This paper presents a result of plasticity index and hardening of treated peat soil with various dosage of additives. In order to determine plasticity of the treated peat soil, atterberg limit test which comprises plastic limit and liquid limit test has been conducted. Determination of liquid limit in this experimental study is by using cone penetrometer. Vicat testing apparatus has been used in the hardening test which the penetration of the plunger is recorded every one hour for 24 hours. The results show that the plasticity index of peat soil stabilized with 80% FAAC and 20% OPC has the lowest plasticity index and recorded the fastest initial setting time. The significant of this study is to promote greener solution for future soil stabilization industry.

Keywords: additives, hardening, peat soil, plasticity index, soil stabilization

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Authors: Iqraq Kamal, Akmal Razif, Sivadas Chandra Sekaran, Ahmad Syazwan Hisaburi

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Painting plays a crucial role in the aerospace manufacturing industry, serving both protective and cosmetic purposes for components. However, the traditional manual painting method is time-consuming and labor-intensive, posing challenges for the sector in achieving higher efficiency. Additionally, the current automated robot path planning has been a bottleneck for spray painting processes, as typical manual teaching methods are time-consuming, error-prone, and skill-dependent. Therefore, it is essential to develop automated tool path planning methods to replace manual ones, reducing costs and improving product quality. Focusing on flat panel painting in aerospace manufacturing, this study aims to address issues related to unreliable part identification techniques caused by the high-mixture, low-volume nature of the industry. The proposed solution involves using a spray gun and a UR10 robotic arm with a vision system that utilizes one-shot object detection (OS2D) to identify parts accurately. Additionally, the research optimizes path planning by concentrating on the region of interest—specifically, the identified part, rather than uniformly covering the entire painting tray.

Keywords: aerospace manufacturing, one-shot object detection, automated spray painting, vision-based path optimization, deep learning, automation, robotic arm

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Authors: Sayed A. Elsayed, Emad Z. Ibrahim, Osama M. Mesalhy, Mohamed A. Abdelatief

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An experimental and numerical study has been conducted to clarify fluid flow characteristics and pressure drop distributions of a cross-flow heat exchanger employing staggered wing-shaped tubes at different angels of attack. The water-side Rew and the air-side Rea were at 5 x 102 and at from 1.8 x 103 to 9.7 x 103, respectively. Three cases of the tubes arrangements with various angles of attack, row angles of attack and 90° cone angles were employed at the considered Rea range. Correlation of pressure drop coefficient Pdc in terms of Rea, design parameters for the studied cases were presented. The flow pattern around the staggered wing-shaped tubes bundle were predicted by using commercial CFD FLUENT 6.3.26 software package. Results indicated that the values of Pdc were increased by increasing the angle of attack from 0° to 45°, while the opposite was true for angles of attack from 135° to 180°. Comparisons between the experimental and numerical results of the present study and those, previously, obtained for similar available studies showed good agreements.

Keywords: wing-shaped tubes, cross-flow cooling, staggered arrangement, CFD

Procedia PDF Downloads 359

Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

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Authors: Bao Thach Nguyen, Abbas Mohajerani

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The California bearing ratio (CBR) has been acknowledged as an important parameter to characterize the bearing capacity of earth structures, such as earth dams, road embankments, airport runways, bridge abutments, and pavements. Technically, the CBR test can be carried out in the laboratory or in the field. The CBR test is time-consuming and is infrequently performed due to the equipment needed and the fact that the field moisture content keeps changing over time. Over the years, many correlations have been developed for the prediction of CBR by various researchers, including the dynamic cone penetrometer, undrained shear strength, and Clegg impact hammer. This paper reports and discusses some of the results from a study on the prediction of CBR. In the current study, the CBR test was performed in the laboratory on some fine-grained subgrade soils collected from various locations in Victoria. Based on the test results, a satisfactory empirical correlation was found between the CBR and the physical properties of the experimental soils.

Keywords: California bearing ratio, fine-grained soils, soil physical properties, pavement, soil test

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: M. A. Zaki Ewiss, S. Afifi

Abstract:

Nahda University (NUB) believes that internationalisation of higher educational is able to provide global society with an education that meets current needs and that can respond efficiently to contemporary demands and challenges, which are characterized by globalisation, interdependence, and multiculturalism. In this paper, we will discuss the the challenges of the Egyptian Higher Education system and the future vision to improve this system> In this report, the following issues will be considered: Increasing knowledge on the development of specialized programs of study at the university. Developing international cooperation programs, which focus on the development of the students and staff skills, and providing academic culture and learning opportunities. Increasing the opportunities for student mobility, and research projects for faculty members. Increased opportunities for staff, faculty and students to continue to learn foreign universities, and to benefit from scholarships in various disciplines. Taking the advantage of the educational experience and modern teaching methods; Providing the opportunities to study abroad without increasing the period of time required for graduation, and through greater integration in the curricula and programs; More cultural interaction through student exchanges.Improving and providing job opportunities for graduates through participation in the global labor market. This document sets out NUB strategy to move towards that vision. We are confident that greater explicit differentiation, greater freedom and greater collaboration are the keys to delivering the further improvement in quality we shall need to retain and strengthen our position as one of the world’s leading higher education systems.

Keywords: technology transfer higher education, knowledge transfer, internationalisation, mobility

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Authors: Xi Liu, Fan Yuan

Abstract:

In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: M. Kagan Keler, Sibel Daglilar, Isil Kerti, Oguzhan Gunduz

Abstract:

Electrospinning is a versatile way to occur fibers at nano-scale and polycaprolactone is a biomedical material which has a wide usage in cartilage defects and tissue regeneration. PCL is biocompatible and durable material which can be used in bio-implants. Therefore, electrospinning process was chosen as a fabrication method to get PCL fibers in an effective way because of its significant adjustments. In this research study, electrospinning parameters was evaluated during the producing of polymer tissue scaffolds. Polycaprolactone’s molecular weight was 80.000 Da and was employed as a tissue material in the electrospinning process. PCL was decomposed in dimethylformamid(DMF) and chloroform(CF) with the weight ratio of 1:1. Different compositions (1%, 3%, 5%, 10% and 20 %) of PCL was prepared in the laboratory conditions. All solvents with different percentages of PCL have been taken into the syringe and loaded into the electrospinning system. In electrospinning dozens of trial were applied to get homogeneously uniform scaffold samples. Taylor cone which is crucial point for electrospinning characteristic was occurred and changed in different voltages up to the material compositions’ conductivity. While the PCL percentages were increasing in the electrospinning, structure started to arise with droplets, which was an expressive problem for tissue scaffold. The vertical and horizontal layouts were applied to produce non-woven structures at all.

Keywords: tissue engineering, artificial scaffold, electrospinning, biocomposites

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Authors: Kunal Krishna Das, Eddie S. S. Lam

Abstract:

Preplaced Aggregate Concrete (PAC) is produced by first placing the coarse aggregate into the formwork, followed by injection of grout to fill in the voids in between the coarse aggregates. In this study, tests were carried out to determine the effects of supplementary cementitious materials on the properties of PAC. Cement was partially replaced by ground granulated blast furnace slag (GGBS) and silica fume (SF) at different proportions. Grout properties were determined by the flow cone test and compressive strength test. Grout proportion was optimized statistically. It was applied to form PAC. Hardened properties of PAC, comprising compressive strength, splitting tensile strength, chloride-ion penetration and drying shrinkage, were evaluated. GGBS enhanced the flowability of the grout, whereas SF enhanced the strength of PAC. Both GGBS and SF improved the resistance to chloride-ion penetration with the drawback of increased drying shrinkage. Nevertheless, drying shrinkage was within the range to be classified as low shrinkage concrete.

Keywords: factorial design, ground granulated blast furnace slag, preplaced aggregate concrete, silica fume

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

Abstract:

In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Amrita Mishra, Tushar Kanti Das

Abstract:

Agriculture is the backbone of the economy of Odisha. Sustainability of agriculture holds the key for the development of Odisha. The Sustainable Development Goals are a framework of 17 goals and 169 targets across social, economical and environmental areas of sustainable development. Among all the seventeen goals the second goal is focusing on the promotion of Sustainable Agriculture. In this research our main aim is also to contribute an understanding of effectiveness of sustainable agriculture as a tool for rural development in the selected tribal district (i.e. Koraput and Malkangiri) of Odisha. These two districts are comes under KBK districts of Odisha which are identified as most backward districts of Odisha. The objectives of our study are to investigate the effect of sustainable agriculture on the lives of tribal farmers, to study whether the farmers are empowered by their participation in sustainable agriculture initiatives to move towards their own vision of development and to study the investment and profit ratio in sustainable agriculture. This research will help in filling the major gaps in sociological studies of sustainable agriculture. This information will helpful for farmers, development organisations, donors and policy makers in formulating the development of effective initiatives and policies to support the development of sustainable agriculture. In this study, we have taken 210 respondents and used various statistical techniques like chi-square test, one-way ANOVA and percentage analysis. This research shows that sustainable agriculture is an effective development strategy that benefits the tribal farmers to move towards their own vision of Good Fortune. The poor farmers who struggle to feed their families and maintain viable livelihoods on shrinking land for them sustainable agriculture are really benefited. The farmers are using homemade pesticides, manure and also getting the seeds from different development organisations and Government. So the investment in Sustainable Agriculture is very less. All farmers said their lives are now better than before. The creation of farmers groups for training and marketing for the produces was shown to be very important for empowerment.

Keywords: sustainable, agriculture, tribal farmers, development, empowerment

Procedia PDF Downloads 156