Search results for: automated teller machines

Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

Procedia PDF Downloads 225

Authors: A. Marolleau, F. Salaun, D. Dupont, H. Gidik, S. Ducept.

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The goal of this study is to analyze the hydric behaviour of textiles which can impact significantly the comfort of the wearer. Indeed, fabrics can be adapted for different climate if hydric and thermal behaviors are known. In this study, fabrics are only submitted to hydric variations. Sorption and desorption isotherms obtained from the dynamic vapour sorption apparatus (DVS) are fitted with the parallel exponential kinetics (PEK), the Hailwood-Horrobin (HH) and the Brunauer-Emmett-Teller (BET) models. One of the major finding is the relationship existing between PEK and HH models. During slow and fast processes, the sorption of water molecules on the polymer can be in monolayer and multilayer form. According to the BET model, moisture regain, a physical property of textiles, show a linear correlation with the total amount of water taken in monolayer. This study provides potential information of the end uses of these fabrics according to the selected activity level.

Keywords: comfort, hydric properties, modelling, underwears

Procedia PDF Downloads 127

Authors: Saad Roustom, Hajo Ribberink

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In most cities in the world, traffic has increased strongly over the last decades, causing high levels of congestion and deteriorating inner-city air quality. This study analyzes the impact of connected and automated vehicles (CAVs) on traffic performance and greenhouse gas (GHG) emissions under different CAV penetration rates in mixed fleet environments of CAVs and driver-operated vehicles (DOVs) and under three different traffic demand levels. Utilizing meso-scale traffic simulations of the City of Ottawa, Canada, the research evaluates the traffic performance of three distinct CAV driving behaviors—Cautious, Normal, and Aggressive—at penetration rates of 25%, 50%, 75%, and 100%, across three different traffic demand levels. The study employs advanced correlation models to estimate GHG emissions. The results reveal that Aggressive and Normal CAVs generally reduce traffic congestion and GHG emissions, with their benefits being more pronounced at higher penetration rates (50% to 100%) and elevated traffic demand levels. On the other hand, Cautious CAVs exhibit an increase in both traffic congestion and GHG emissions. However, results also show deteriorated traffic flow conditions when introducing 25% penetration rates of any type of CAVs. Aggressive CAVs outperform all other driving at improving traffic flow conditions and reducing GHG emissions. The findings of this study highlight the crucial role CAVs can play in enhancing urban traffic performance and mitigating the adverse impact of transportation on the environment. This research advocates for the adoption of effective CAV-related policies by regulatory bodies to optimize traffic flow and reduce GHG emissions. By providing insights into the impact of CAVs, this study aims to inform strategic decision-making and stimulate the development of sustainable urban mobility solutions.

Keywords: connected and automated vehicles, congestion, GHG emissions, mixed fleet environment, traffic performance, traffic simulations

Procedia PDF Downloads 51

Authors: Jalal Soleimannejad, Mohammad Asadizeidabadi, Mahmoud Koorki, Mojtaba Azarpira

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A significant percentage of production costs is the maintenance costs, and analysis of maintenance costs could to achieve greater productivity and competitiveness. With this is mind, the maintenance of machines and installations is considered as an essential part of organizational functions and applying effective strategies causes significant added value in manufacturing activities. Organizations are trying to achieve performance levels on a global scale with emphasis on creating competitive advantage by different methods consist of RCM (Reliability-Center-Maintenance), TPM (Total Productivity Maintenance) etc. In this study, increasing the capacity of Concentration Plant of Golgohar Iron Ore Mining & Industrial Company (GEG) was examined by using of reliability and maintainability analyses. The results of this research showed that instead of increasing the number of machines (in order to solve the bottleneck problems), the improving of reliability and maintainability would solve bottleneck problems in the best way. It should be mention that in the abovementioned study, the data set of Concentration Plant of GEG as a case study, was applied and analyzed.

Keywords: bottleneck, golgohar iron ore mining & industrial company, maintainability, maintenance costs, reliability

Procedia PDF Downloads 328

Authors: Shuen-Tai Wang, Ying-Chuan Chen, Yu-Ching Lin

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There has been renewal of interest in the relation between Green IT and cloud computing in recent years. Cloud computing has to be a highly elastic environment which provides stable services to users. The growing use of cloud computing facilities has caused marked energy consumption, putting negative pressure on electricity cost of computing center or data center. Each year more and more network devices, storages and computers are purchased and put to use, but it is not just the number of computers that is driving energy consumption upward. We could foresee that the power consumption of cloud computing facilities will double, triple, or even more in the next decade. This paper aims at resource allocation and scheduling technologies that are short of or have not well developed yet to reduce energy utilization in cloud computing platform. In particular, our approach relies on recalling services dynamically onto appropriate amount of the machines according to user’s requirement and temporarily shutting down the machines after finish in order to conserve energy. We present initial work on integration of resource and power management system that focuses on reducing power consumption such that they suffice for meeting the minimizing quality of service required by the cloud computing platform.

Keywords: cloud computing, energy utilization, power consumption, resource allocation

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss

Procedia PDF Downloads 447

Authors: Sajjad Hassanpour, Vicente A. González, Yang Zou, Jiamou Liu

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Earthquakes pose a significant threat to both structural and non-structural elements in buildings, putting human lives at risk. Effective post-earthquake evacuation is critical for ensuring the safety of building occupants. However, current design practices often neglect the integration of post-earthquake evacuation considerations into the early-stage architectural design process. To address this gap, this paper presents a novel automated internal architectural layout generation tool that optimizes post-earthquake evacuation performance. The tool takes an initial plain floor plan as input, along with specific requirements from the user/architect, such as minimum room dimensions, corridor width, and exit lengths. Based on these inputs, firstly, the tool randomly generates different architectural layouts. Secondly, the human post-earthquake evacuation behaviour will be thoroughly assessed for each generated layout using the advanced Agent-Based Building Earthquake Evacuation Simulation (AB2E2S) model. The AB2E2S prototype is a post-earthquake evacuation simulation tool that incorporates variables related to earthquake intensity, architectural layout, and human factors. It leverages a hierarchical agent-based simulation approach, incorporating reinforcement learning to mimic human behaviour during evacuation. The model evaluates different layout options and provides feedback on evacuation flow, time, and possible casualties due to earthquake non-structural damage. By integrating the AB2E2S model into the automated layout generation tool, architects and designers can obtain optimized architectural layouts that prioritize post-earthquake evacuation performance. Through the use of the tool, architects and designers can explore various design alternatives, considering different minimum room requirements, corridor widths, and exit lengths. This approach ensures that evacuation considerations are embedded in the early stages of the design process. In conclusion, this research presents an innovative automated internal architectural layout generation tool that integrates post-earthquake evacuation simulation. By incorporating evacuation considerations into the early-stage design process, architects and designers can optimize building layouts for improved post-earthquake evacuation performance. This tool empowers professionals to create resilient designs that prioritize the safety of building occupants in the face of seismic events.

Keywords: agent-based simulation, automation in design, architectural layout, post-earthquake evacuation behavior

Procedia PDF Downloads 67

Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Being able to identify biological particles such as spores, viruses, or pollens is important for health care professionals, as it allows for appropriate therapeutic management of patients. Optical microscopy is a technology widely used for the analysis of these types of microorganisms, because, compared to other types of microscopy, it is not expensive. The analysis of an optical microscope slide is a tedious and time-consuming task when done manually. However, using machine learning and computer vision, this process can be automated. The first step of an automated microscope slide image analysis process is segmentation. During this step, the biological particles are localized and extracted. Very often, the use of an automatic thresholding method is sufficient to locate and extract the particles. However, in some cases, the particles are not extracted individually because they are stuck to other biological elements. In this paper, we propose a stuck particles separation method based on the use of the Sobel operator and thresholding. We illustrate it by applying it to the separation of 813 images of adjacent pollen grains. The method correctly separated 95.4% of these images.

Keywords: image segmentation, stuck particles separation, Sobel operator, thresholding

Procedia PDF Downloads 108

Authors: Angad Arora

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In production operations/manufacturing, a cell or line is typically a bunch of similar machines (computer numerical control (CNCs), advanced cutting, 3D printing or special purpose machines. For qualifying a typical manufacturing line /cell / new process, Ideally, we need a sample of parts that can be flown through the process and then we make a judgment on the health of the line/cell. However, with huge volumes and mass production scope, such as in the mobile phone industry, for example, the actual cells or lines can go in thousands and to qualify each one of them with statistical confidence means utilizing samples that are very large and eventually add to product /manufacturing cost + huge waste if the parts are not intended to be customer shipped. To solve this, we come up with 2 steps statistical approach. We start with a small sample size and then objectively evaluate whether the process needs additional samples or not. For example, if a process is producing bad parts and we saw those samples early, then there is a high chance that the process will not meet the desired yield and there is no point in keeping adding more samples. We used this hypothesis and came up with 2 steps binomial testing approach. Further, we also prove through results that we can achieve an 18-25% reduction in samples while keeping the same statistical confidence.

Keywords: statistics, data science, manufacturing process qualification, production planning

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Authors: Flóra Pomázi, Sándor Baranya, Zoltán Szalai

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A harmonized monitoring of the suspended sediment transport along such a large river as the world’s most international river, the Danube River, is a rather challenging task. The traditional monitoring method in Hungary is obsolete but using indirect measurement devices and techniques like optical backscatter sensors (OBS), laser diffraction or acoustic backscatter sensors (ABS) could provide a fast and efficient alternative option of direct methods. However, these methods are strongly sensitive to the particle characteristics (i.e. particle shape, particle size and mineral composition). The current method does not provide sufficient information about particle size distribution, mineral analysis is rarely done, and the shape of the suspended sediment particles have not been examined yet. The aims of the study are (1) to determine the particle characterisation of suspended sediment in the Danube River using advanced particle characterisation methods as laser diffraction and automated imaging, and (2) to perform a sensitivity analysis of the indirect methods in order to determine the impact of suspended particle characteristics. The particle size distribution is determined by laser diffraction. The particle shape and mineral composition analysis is done by the Morphologi G3ID image analyser. The investigated indirect measurement devices are the LISST-Portable|XR, the LISST-ABS (Sequoia Inc.) and the Rio Grande 1200 kHz ADCP (Teledyne Marine). The major findings of this study are (1) the statistical shape of the suspended sediment particle - this is the first research in this context, (2) the actualised particle size distribution – that can be compared to historical information, so that the morphological changes can be tracked, (3) the actual mineral composition of the suspended sediment in the Danube River, and (4) the reliability of the tested indirect methods has been increased – based on the results of the sensitivity analysis and the previous findings.

Keywords: advanced particle characterisation, automated imaging, indirect methods, laser diffraction, mineral composition, suspended sediment

Procedia PDF Downloads 115

Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

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Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

Procedia PDF Downloads 217

Authors: Aysegul Sarac

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Washer-dryers are the machines which can either wash the laundries or can dry them. In other words, we can define a washer-dryer as a washing machine and a dryer in one machine. Washing machines are characterized by the loading capacity, cabinet depth and spin speed. Dryers are characterized by the drying technology. On the other hand, energy efficiency, water consumption, and noise levels are main characteristics that influence customer decisions to buy washers. Water condensation technology is the most common drying technology existing in the washer-dryer market. Water condensation technology uses water to dry the laundry inside the machine. Thus, in this type of the drying technology water consumption is at high levels comparing other technologies. Water condensation technology sprays cold water in the drum to condense the humidity of hot weather in order to dry the laundry inside. Thus, water consumption influences the drying performance. The scope of this study is to optimize water consumption during drying process under a constraint of energy consumption and drying performance. We are using 6-Sigma methodology to find the optimum water consumption by comparing drying performances of different drying algorithms.

Keywords: optimization, 6-Sigma methodology, washer-dryers, water condensation technology

Procedia PDF Downloads 337

Authors: Pham-Thi Huong, Byeong-Kyu Lee, Jitae Kim, Chi-Hyeon Lee

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Fe-nano zeolite adsorbent was used for removal of Ni (II) ions from aqueous solution. The adsorbent was characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and the surface area Brunauer–Emmett–Teller (BET) using for analysis of functional groups, morphology and surface area. Bath adsorption experiments were analyzed on the effect of pH, time, adsorbent doses and initial Ni (II) concentration. The optimum pH for Ni (II) removal using Fe-nano zeolite was found at 5.0 and 90 min of reaction time. The maximum adsorption capacity of Ni (II) was 231.68 mg/g based on the Langmuir isotherm. The kinetics data for the adsorption process was fitted with the pseudo-second-order model. The desorption of Ni (II) from Ni-loaded Fe-nano zeolite was analyzed and even after 10 cycles 72 % desorption was achieved. These finding supported that Fe-nano zeolite with high adsorption capacity, high reuse ability would be utilized for Ni (II) removal from water.

Keywords: Fe-nano zeolite, adsorption, Ni (II) removal, regeneration

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Authors: Sadia Basar, Naila Habib, Awais Adnan

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The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

Procedia PDF Downloads 353

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

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

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

Procedia PDF Downloads 233

Authors: Shu Yin, Zhiyang Ding, Jianzhong Huang, Xiaojun Ruan, Xiaomin Zhu, Xiao Qin

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Since large-scale and data-intensive applications have been widely deployed, there is a growing demand for high-performance storage systems to support data-intensive applications. Compared with traditional storage systems, next-generation systems will embrace dedicated processor to reduce computational load of host machines and will have hybrid combinations of different storage devices. The advent of flash- memory-based solid state disk has become a critical role in revolutionizing the storage world. However, instead of simply replacing the traditional magnetic hard disk with the solid state disk, it is believed that finding a complementary approach to corporate both of them is more challenging and attractive. This paper explores an idea of active storage, an emerging new storage configuration, in terms of the architecture and design, the parallel processing capability, the cooperation of other machines in cluster computing environment, and a disk configuration, the hybrid combination of different types of disk drives. Experimental results indicate that the proposed HcDD achieves better I/O performance and longer storage system lifespan.

Keywords: arallel storage system, hybrid storage system, data inten- sive, solid state disks, reliability

Procedia PDF Downloads 417

Authors: Samira Hamiditehrani

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Automated vehicles (AVs) are emerging technologies that could potentially offer a wide range of opportunities and challenges for the transportation sector. The advent of AV technology has also resulted in new business models in shared mobility services where many ride hailing and car sharing companies are developing on-demand AVs including shared automated vehicles (SAVs) and pooled automated vehicles (Pooled AVs). SAVs and Pooled AVs could provide alternative shared mobility services which encourage sustainable transport systems, mitigate traffic congestion, and reduce automobile dependency. However, the success of on-demand AVs in addressing major transportation policy issues depends on whether and how the public adopts them as regular travel modes. To identify conditions under which individuals may adopt on-demand AVs, previous studies have applied human behavior and technology acceptance theories, where Theory of Planned Behavior (TPB) has been validated and is among the most tested in on-demand AV research. In this respect, this study has three objectives: (a) to propose and validate a theoretical model for behavioral intention to use SAVs and Pooled AVs by extending the original TPB model; (b) to identify the characteristics of early adopters of SAVs, who prefer to have a shorter and private ride, versus prospective users of Pooled AVs, who choose more affordable but longer and shared trips; and (c) to investigate Canadians’ intentions to adopt on-demand AVs for regular trips. Toward this end, this study uses data from an online survey (n = 3,622) of workers or adult students (18 to 75 years old) conducted in October and November 2021 for six major Canadian metropolitan areas: Toronto, Vancouver, Ottawa, Montreal, Calgary, and Hamilton. To accomplish the goals of this study, a base bivariate ordered probit model, in which both SAV and Pooled AV adoptions are estimated as ordered dependent variables, alongside a full structural equation modeling (SEM) system are estimated. The findings of this study indicate that affective motivations such as attitude towards AV technology, perceived privacy, and subjective norms, matter more than sociodemographic and travel behavior characteristic in adopting on-demand AVs. Also, the results of second objective provide evidence that although there are a few affective motivations, such as subjective norms and having ample knowledge, that are common between early adopters of SAVs and PooledAVs, many examined motivations differ among SAV and Pooled AV adoption factors. In other words, motivations influencing intention to use on-demand AVs differ among the service types. Likewise, depending on the types of on-demand AVs, the sociodemographic characteristics of early adopters differ significantly. In general, findings paint a complex picture with respect to the application of constructs from common technology adoption models to the study of on-demand AVs. Findings from the final objective suggest that policymakers, planners, the vehicle and technology industries, and the public at large should moderate their expectations that on-demand AVs may suddenly transform the entire transportation sector. Instead, this study suggests that SAVs and Pooled AVs (when they entire the Canadian market) are likely to be adopted as supplementary mobility tools rather than substitutions for current travel modes

Keywords: automated vehicles, Canadian perception, theory of planned behavior, on-demand AVs

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Authors: Amir Rashid

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Cloud Computing frameworks empower organizations to cut expenses by outsourcing computation resources on-request. As of now, customers of Cloud service providers have no methods for confirming the privacy and ownership of their information and data. To address this issue we propose the platform of a trusted cloud computing program (TCCP). TCCP empowers Infrastructure as a Service (IaaS) suppliers, for example, Amazon EC2 to give a shout box execution condition that ensures secret execution of visitor virtual machines. Also, it permits clients to bear witness to the IaaS supplier and decide if the administration is secure before they dispatch their virtual machines. This paper proposes a Trusted Cloud Computing Platform (TCCP) for guaranteeing the privacy and trustworthiness of computed data that are outsourced to IaaS service providers. The TCCP gives the deliberation of a shut box execution condition for a client's VM, ensuring that no cloud supplier's authorized manager can examine or mess up with its data. Furthermore, before launching the VM, the TCCP permits a client to dependably and remotely acknowledge that the provider at backend is running a confided in TCCP. This capacity extends the verification of whole administration, and hence permits a client to confirm the data operation in secure mode.

Keywords: cloud security, IaaS, cloud data privacy and integrity, hybrid cloud

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Authors: Armel Mbon

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This paper discusses the first-person point of view in Maugham's 'Mr. Know-All.' It particularly analyses the narrator's position in relation to the story told in this short story, with the intention of disclosing the latter's prejudice against Mr. Kelada, the protagonist, and, consequently, its hindrance to globalisation. It thus underlines the fact that this protagonist and other travellers are different colours, but one person on this ship epitomises globalisation. The general attitude of readers is that they are inclined to easily believe the narrator while forgetting that fiction is the work of a taler, a teller, but, first and foremost, a liar. The audience, whether it is disconnected from the setting or not, also tends to forget that "travellers from afar can lie with impunity." In fact, the nameless narrator in Maugham's short story has a persona that leaves a lot to be desired. He is prejudiced against Mr. Kelada, known as Mr. Know-All, as will be evidenced by the scrutiny of his diction. This paper finally purports to show that those who proclaim globalisation loudly are not ready to live together.

Keywords: narrator, persona, point of view, diction, contrast, globalisation

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Authors: Ali Akbar Kazemi Asl, Mansour Rahsepar

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Mesoporous carbon nanospheres (MCNs) with uniform particle size distribution having an average of 290 nm and large specific surface area (274.4 m²/g) were synthesized by a one-step hydrothermal method followed by the calcination process and then utilized as an enzyme-free glucose biosensor. Morphology, crystal structure, and porous nature of the synthesized nanospheres were characterized by scanning electron microscopy (SEM), X-Ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) analysis, respectively. Also, the electrochemical performance of the MCNs@GCE electrode for the measurement of glucose concentration in alkaline media was investigated by electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and chronoamperometry (CA). MCNs@GCE electrode shows good sensing performance, including a rapid glucose oxidation response within 3.1 s, a wide linear range of 0.026-12 mM, a sensitivity of 212.34 μA.mM⁻¹.cm⁻², and a detection limit of 25.7 μM with excellent selectivity.

Keywords: biosensor, electrochemical, glucose, mesoporous carbon, non-enzymatic

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Authors: Amir Nazemi, Milad Ramezankhani, Marian Kӧrber, Abbas S. Milani

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The excellent strength to weight ratio of woven fabric composites, along with their high formability, is one of the primary design parameters defining their increased use in modern manufacturing processes, including those in aerospace and automotive. However, for emerging automated preform processes under the smart manufacturing paradigm, complex geometries of finished components continue to bring several challenges to the designers to cope with manufacturing defects on site. Wrinklinge. g. is a common defectoccurring during the forming process and handling of semi-finished textile composites. One of the main reasons for this defect is the weak bending stiffness of fibers in unconsolidated state, causing excessive relative motion between them. Further challenges are represented by the automated handling of large-area fiber blanks with specialized gripper systems. For fabric composites forming simulations, the finite element (FE)method is a longstanding tool usedfor prediction and mitigation of manufacturing defects. Such simulations are predominately meant, not only to predict the onset, growth, and shape of wrinkles but also to determine the best processing condition that can yield optimized positioning of the fibers upon forming (or robot handling in the automated processes case). However, the need for use of small-time steps via explicit FE codes, facing numerical instabilities, as well as large computational time, are among notable drawbacks of the current FEtools, hindering their extensive use as fast and yet efficient digital twins in industry. This paper presents a novel woven fabric simulation technique through the application of the material point method (MPM), which enables the use of much larger time steps, facing less numerical instabilities, hence the ability to run significantly faster and efficient simulationsfor fabric materials handling and forming processes. Therefore, this method has the ability to enhance the development of automated fiber handling and preform processes by calculating the physical interactions with the MPM fiber models and rigid tool components. This enables the designers to virtually develop, test, and optimize their processes based on either algorithmicor Machine Learning applications. As a preliminary case study, forming of a hemispherical plain weave is shown, and the results are compared to theFE simulations, as well as experiments.

Keywords: material point method, woven fabric composites, forming, material handling

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Authors: Shahin Shabani, Pouya Pourmadadi

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Tunnel boring machines (TBMs) have been used for the construction of various tunnels for mining projects for the purpose of access, conveyance of ore and waste, drainage, exploration, water supply and water diversion. Several mining projects have seen the successful and economic beneficial use of TBMs, and there is an increasing awareness of the benefits of TBMs for mining projects. Key technical considerations for the use of TBMs for the construction of tunnels for mining projects include geological issues (rock type, rock alteration, rock strength, rock abrasivity, durability, ground water inflows), depth of cover and the potential for overstressing/rockbursts, site access and terrain, portal locations, TBM constraints, minimum tunnel size, tunnel support requirements, contractor and labor experience, and project schedule demands. This study focuses on tunnelling mining, with the goal to develop methods and tools to be used to gain understanding of these processes, and to analyze metro of Tehran. The Metro Line 7 of Tehran is one of the Longest (26 Km) and deepest (27m) of projects that’s under implementation. Because of major differences like passing under all geotechnical layers of the town and encountering part of it with underground water table and also using mechanized excavation system, is one of special metro projects.

Keywords: TBM, tunnel boring machines economic, metro, line 7

Procedia PDF Downloads 357

Authors: Hamidullah Binol, Abdullah Bal

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Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: food (ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods

Procedia PDF Downloads 404

Authors: Ali Mamizadeh, Ires Iskender, Saeid Aghaei

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Energy is one of the important issues with high priority property in the world. Energy demand is rapidly increasing depending on the growing population and industry. The useable energy sources in the world will be insufficient to meet the need for energy. Therefore, the efficient and economical usage of energy sources is getting more importance. In a survey conducted among electric consuming machines, the electrical machines are consuming about 40% of the total electrical energy consumed by electrical devices and 96% of this consumption belongs to induction motors. Induction motors are the workhorses of industry and have very large application areas in industry and urban systems like water pumping and distribution systems, steel and paper industries and etc. Monitoring and the control of the motors have an important effect on the operating performance of the motor, driver selection and replacement strategy management of electrical machines. The sensorless monitoring system for monitoring and calculating efficiency of induction motors are studied in this study. The equivalent circuit of IEEE is used in the design of this study. The terminal current and voltage of induction motor are used in this motor to measure the efficiency of induction motor. The motor nameplate information and the measured current and voltage are used in this system to calculate accurately the losses of induction motor to calculate its input and output power. The efficiency of the induction motor is monitored online in the proposed method without disconnecting the motor from the driver and without adding any additional connection at the motor terminal box. The proposed monitoring system measure accurately the efficiency by including all losses without using torque meter and speed sensor. The monitoring system uses embedded architecture and does not need to connect to a computer to measure and log measured data. The conclusion regarding the efficiency, the accuracy and technical and economical benefits of the proposed method are presented. The experimental verification has been obtained on a 3 phase 1.1 kW, 2-pole induction motor. The proposed method can be used for optimal control of induction motors, efficiency monitoring and motor replacement strategy.

Keywords: induction motor, efficiency, power losses, monitoring, embedded design

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Authors: Aniket Alam, Sravya Gurram

Abstract:

This paper proposes a categorization of sport that is based on the system of rules that a sport must adhere to. We focus on these systems of rules to examine how a winner is produced in different sports. The rules of a sport dictate the game play and the direction it takes. We propose to break down the game play into events. At this junction, we observe two kinds of events that constitute the game play of a sport –ones that follow sequential logic and ones that do not. Our focus is pertained to sports that are comprised of sequential events. To examine these events further, to understand how a winner emerges, we take the help of finite-state automaton from the theory of computation (Automata theory). We showcase how sequential sports are eligible to be represented as finite state machines. We depict these finite state machines as state diagrams. We examine these state diagrams to observe how a team/player reaches the final states of the sport, with a special focus on one final state –the final state which determines the winner. This exercise has been carried out for the following sports: Hurdles, Track, Shot Put, Long Jump, Bowling, Badminton, Pacman and Weightlifting (Snatch). Based on our observations of how this final state of winning is achieved, we propose a categorization of sports.

Keywords: sport classification, sport modelling, ontology, automata theory

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Authors: Kritiyaporn Kunsook

Abstract:

Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

Procedia PDF Downloads 336

Authors: A. Sohouli, A. Suleman

Abstract:

This study focuses on reliability analysis of variable stiffness composite laminate structures to investigate the potential structural improvement compared to conventional (straight fibers) composite laminate structures. A computational framework was developed which it consists of a deterministic design step and reliability analysis. The optimization part is Discrete Material Optimization (DMO) and the reliability of the structure is computed by Monte Carlo Simulation (MCS) after using Stochastic Response Surface Method (SRSM). The design driver in deterministic optimization is the maximum stiffness, while optimization method concerns certain manufacturing constraints to attain industrial relevance. These manufacturing constraints are the change of orientation between adjacent patches cannot be too large and the maximum number of successive plies of a particular fiber orientation should not be too high. Variable stiffness composites may be manufactured by Automated Fiber Machines (AFP) which provides consistent quality with good production rates. However, laps and gaps are the most important challenges to steer fibers that effect on the performance of the structures. In this study, the optimal curved fiber paths at each layer of composites are designed in the first step by DMO, and then the reliability analysis is applied to investigate the sensitivity of the structure with different standard deviations compared to the straight fiber angle composites. The random variables are material properties and loads on the structures. The results show that the variable stiffness composite laminate structures are much more reliable, even for high standard deviation of material properties, than the conventional composite laminate structures. The reason is that the variable stiffness composite laminates allow tailoring stiffness and provide the possibility of adjusting stress and strain distribution favorably in the structures.

Keywords: material optimization, Monte Carlo simulation, reliability analysis, response surface method, variable stiffness composite structures

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Authors: Hatib Shabbir

Abstract:

Student support services play an important role in providing technical and motivational support to distance learner. ICT based systems have improved the efficiency and effectiveness of support services. In distance education, students being at distant require quick responses from their institution. In the manual system, it is practically hard to give prompt response to each and every student, so as a result student has to suffer a lot. The best way to minimize inefficiencies is to use automated systems. This project involves the development of centralized automated software that would not only replace the manual degree issuance system of 1.3 million students studying at AIOU but also provide online tracking to all the students applying for Degrees. DTS is also the first step towards the paperless culture which is adopted by the major organizations of the world. DTS would not only save university cost but also save students cost and time too by conveying all the information/objection through email and SMS. Moreover, DTS also monitors the performance of each and every individual working in the exam department AIOU and generates daily, monthly and yearly reports of every individual which helps a lot in continuous performance monitoring of the employees.

Keywords: aiou dts, dts aiou, dts, degree tracking aiou

Procedia PDF Downloads 202

Authors: Solanki Ravirajsinh, Kudo Kuniaki, Sharma Ankit, Devi Sherine, Kuboshima Misaki, Tachi Shuntaro

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In software development life cycles, the absence of scalable CI/CD significantly impacts organizations, leading to increased overall maintenance costs, prolonged release delivery times, heightened manual efforts, and difficulties in meeting tight deadlines. Implementing CI/CD with standard serverless technologies using cloud services overcomes all the above-mentioned issues and helps organizations improve efficiency and faster delivery without the need to manage server maintenance and capacity. By integrating scalable CI/CD with scalable automation testing, productivity, quality, and agility are enhanced while reducing the need for repetitive work and manual efforts. Implementing scalable CI/CD for development using cloud services like ECS (Container Management Service), AWS Fargate, ECR (to store Docker images with all dependencies), Serverless Computing (serverless virtual machines), Cloud Log (for monitoring errors and logs), Security Groups (for inside/outside access to the application), Docker Containerization (Docker-based images and container techniques), Jenkins (CI/CD build management tool), and code management tools (GitHub, Bitbucket, AWS CodeCommit) can efficiently handle the demands of diverse development environments and are capable of accommodating dynamic workloads, increasing efficiency for faster delivery with good quality. CI/CD pipelines encourage collaboration among development, operations, and quality assurance teams by providing a centralized platform for automated testing, deployment, and monitoring. Scalable CI/CD streamlines the development process by automatically fetching the latest code from the repository every time the process starts, building the application based on the branches, testing the application using a scalable automation testing framework, and deploying the builds. Developers can focus more on writing code and less on managing infrastructure as it scales based on the need. Serverless CI/CD eliminates the need to manage and maintain traditional CI/CD infrastructure, such as servers and build agents, reducing operational overhead and allowing teams to allocate resources more efficiently. Scalable CI/CD adjusts the application's scale according to usage, thereby alleviating concerns about scalability, maintenance costs, and resource needs. Creating scalable automation testing using cloud services (ECR, ECS Fargate, Docker, EFS, Serverless Computing) helps organizations run more than 500 test cases in parallel, aiding in the detection of race conditions, performance issues, and reducing execution time. Scalable CI/CD offers flexibility, dynamically adjusting to varying workloads and demands, allowing teams to scale resources up or down as needed. It optimizes costs by only paying for the resources as they are used and increases reliability. Scalable CI/CD pipelines employ automated testing and validation processes to detect and prevent errors early in the development cycle.

Keywords: achieve parallel execution, cloud services, scalable automation testing, scalable continuous integration and deployment

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Authors: Zimasa Siyasanga Gysman

Abstract:

This paper assesses the history of electronic music and its performance to illustrate that machines and technology have largely influenced how humans perform electronic music. The history of electronic music mainly focuses on the composition and production of electronic music with little to no attention paid to its performance by the majority of scholars in this field. Therefore, establishing a history of performance involves investigating what compositions of electronic music called for in the production of electronic music performance. This investigation into seminal works in the history of electronic music, therefore, illustrates the aesthetics of electronic music performance and the aesthetics established in the very beginnings of electronic music performance demonstrate the aesthetics of electronic music which are still prevalent today. The key aesthetics are the repurposing of technology and the hybridisation of technology. Performers take familiar technology (technology that society has become accustomed to using in daily life), not necessarily related to music or performance and use it as an instrument in their performances, such as a rotary dial telephone. Likewise, since the beginnings of electronic music, producers have always experimented with the latest technologies available to them in their compositions and performances. The spirit of performers of electronic music, therefore, revolves around repurposing familiar technologies and using them in new ways, whilst similarly experimenting with new technologies in their performances. This process of hybridisation plays a key role in the production and performance of electronic music in the twentieth century. Through various interviews with performers of electronic music, it is shown that these aesthetics are driving performance practices in the twenty-first century.

Keywords: body, hybridisation, performance, sound

Procedia PDF Downloads 136