Search results for: virtual Machine

Authors: Yegetaneh T. Dejenu, Delesa Kejela, Abdulak Alemu

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From the earliest time of humankind, the trenches were used for water to flow along and for soldiers to hide in during enemy attacks. Now a day due to civilization, the needs of the human being become endless, and the living condition becomes sophisticated. The unbalance between the needs and resource obligates them to find the way to manage this condition. The attempt to use the scares resource in very efficient and effective way makes the trench an endeavor practice in the world in all countries. A trencher is a construction equipment used to dig trenches, especially for laying pipes or cables, installing drainage, irrigation, installing fencing, and in preparation for trench warfare. It is a machine used to make a ditch by cutting the soil ground and effectively used in agricultural irrigation. The most common types of trencher are wheel trencher, chain trencher, micro trencher, portable trencher. In Ethiopia people have been trenching the ditch for many purposes and the tools they are using are Pickaxe, Shovel and some are using Micro Excavators. The adverse effect of using traditional equipment is, time and energy consuming, less productive, difficult and more man power is required. Hence it is necessary to design and produce low price, and simple machine to narrow this gap. Our objective is to design and model a light duty trencher that is used for trenching the ground or soil for making ditch and used for agricultural, ground cabling, ground piping, and drainage system. The designed machine trenches, maximum of 1-meter depth, 30 cm width, and the required length. The working mechanism is fully hydraulic, and the engine with 12.7 hp will provide suitable power for the pump that delivers 23 l/min at 1500 rpm to drive hydraulic motors and actuators.

Keywords: hydraulics, modelling, trenching, ditch

Procedia PDF Downloads 215

Authors: Chanida Tangjai Benchalak Muangmeesri, Dechrit Maneetham

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Collection of marine garbage in the modern world, where technology is vital to existence. Consequently, technology can assist in reducing the duplicate labor in the subject of collecting trash in the water that must be done the same way repeatedly owing to the consequence of suffering an emerging disease or COVID-19. This is due to the rapid advancement of technology. As a result, solid trash and plastic garbage are increasing. Agricultural gardens, canals, ponds, and water basins are all sources of water. Building boat-like instruments for rubbish collection in the water will be done this time. It has two control options, boat control via remote control and boat control via an Internet of Things system. A solar panel with a power output of 40 watts powers the system being able to store so accurate and precise waste collection, allowing for thorough water cleaning. The primary goals are to keep the water's surface clean and assess its quality to support the aquatic ecology.

Keywords: automatic boat, water treatment, cleaning machine, iot

Procedia PDF Downloads 91

Authors: Z. Xu

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Immersive virtual reality (VR) has gained momentum in humanitarian communication due to its utopian promises of co-presence, immediacy, and transcendence. These potential benefits have led the United Nations (UN) to tirelessly produce and distribute VR series to evoke global empathy and encourage policymakers, philanthropic business tycoons and citizens around the world to actually do something (i.e. give a donation). However, it is unclear whether or not VR can cultivate cosmopolitans with a sense of social responsibility towards the geographically, socially/culturally and morally mediated misfortune of faraway others. Drawing upon existing works on the mediation of distant suffering, this article constructs an analytical framework to articulate the issue. Applying this framework on a case study of five of the UN’s VR pieces, the article identifies three paradoxes that exist between cyber-utopian and cyber-dystopian narratives. In the “paradox of distance”, VR relies on the notions of “presence” and “storyliving” to implicitly link audiences spatially and temporally to distant suffering, creating global connectivity and reducing perceived distances between audiences and others; yet it also enables audiences to fully occupy the point of view of distant sufferers (creating too close/absolute proximity), which may cause them to feel naive self-righteousness or narcissism with their pleasures and desire, thereby destroying the “proper distance”. In the “paradox of agency”, VR simulates a superficially “real” encounter for visual intimacy, thereby establishing an “audiences–beneficiary” relationship in humanitarian communication; yet in this case the mediated hyperreality is not an authentic reality, and its simulation does not fill the gap between reality and the virtual world. In the “paradox of the hierarchies of human life”, VR enables an audience to experience virtually fundamental “freedom”, epitomizing an attitude of cultural relativism that informs a great deal of contemporary multiculturalism, providing vast possibilities for a more egalitarian representation of distant sufferers; yet it also takes the spectator’s personally empathic feelings as the focus of intervention, rather than structural inequality and political exclusion (an economic and political power relations of viewing). Thus, the audience can potentially remain trapped within the minefield of hegemonic humanitarianism. This study is significant in two respects. First, it advances the turn of digitalization in studies of media and morality in the polymedia milieu; it is motivated by the necessary call for a move beyond traditional technological environments to arrive at a more novel understanding of the asymmetry of power between the safety of spectators and the vulnerability of mediated sufferers. Second, it not only reminds humanitarian journalists and NGOs that they should not rely entirely on the richer news experience or powerful response-ability enabled by VR to gain a “moral bond” with distant sufferers, but also argues that when fully-fledged VR technology is developed, it can serve as a kind of alchemy and should not be underestimated merely as a “bugaboo” of an alarmist philosophical and fictional dystopia.

Keywords: audience, cosmopolitan, distant suffering, virtual reality, humanitarian communication

Procedia PDF Downloads 142

Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

Procedia PDF Downloads 116

Authors: Ehsan Atefi, Michael Grigware

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Respiratory failure secondary to surfactant deficiency resulting from respiratory distress syndrome is considered one major cause of morbidity in preterm infants. Surfactant replacement treatment (SRT) is considered an effective treatment for this disease. Here, we introduce an AI-mediated approach for estimating the distribution of surfactant in the lung airway of a newborn infant during SRT. Our approach implements machine learning to precisely estimate the splitting ratio of a liquid drop during bifurcation at different injection velocities and patient orientations. This technique can be used to calculate the surfactant residue remaining on the airway wall during the surfactant injection process. Our model works by minimizing the pressure drop difference between the two airway branches at each generation, subject to mass and momentum conservation. Our platform can be used to generate feedback for immediately adjusting the velocity of injection and patient orientation during SRT.

Keywords: respiratory failure, surfactant deficiency, surfactant replacement, machine learning

Procedia PDF Downloads 126

Authors: R. R. Govind

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In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

Procedia PDF Downloads 61

Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

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Authors: Subrato Saha, Yun-Hyun Cho

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In electric power steering (EPS), spoke type brushless ac (BLAC) motors offer distinct advantages over other electric motor types in terms torque smoothness, reliability and efficiency. This paper deals with the shape optimization of spoke type BLAC motor, in order to reduce cogging torque. This paper examines 3 steps skewing rotor angle, optimizing rotor core edge and rotor overlap length for reducing cogging torque in spoke type BLAC motor. The methods were applied to existing machine designs and their performance was calculated using finite- element analysis (FEA). Prototypes of the machine designs were constructed and experimental results obtained. It is shown that the FEA predicted the cogging torque to be nearly reduce using those methods.

Keywords: EPS, 3-Step skewing, spoke type BLAC, cogging torque, FEA, optimization

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Authors: Hai Zeng, Anqing Liu, Shuguang Dan, Ying Zhang, Yan Li, Zihang Zeng

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Children are strong; the country strong, the development of children Basketball is a strategic advantage. Common forms of basketball equipment has been difficult to meet the needs of young children teaching the game of basketball, basketball development for 3-6 years old children in the form of appropriate teaching aids is a breakthrough basketball game teaching children bottlenecks, improve teaching critical path pleasure, but also the development of early childhood basketball a necessary requirement. In this study, literature, questionnaires, focus group interviews, comparative analysis, for domestic and foreign use of 12 kinds of basketball teaching aids (cloud computing MINI basketball, adjustable basketball MINI, MINI basketball court, shooting assist paw print ball, dribble goggles, dribbling machine, machine cartoon shooting, rebounding machine, against the mat, elastic belt, ladder, fitness ball), from fun and improve early childhood shooting technique, dribbling technology, as well as offensive and defensive rebounding against technology conduct research on conversion technology. The results show that by using appropriate forms of teaching children basketball aids, can effectively improve children's fun basketball game, targeted to improve a technology, different types of aids from different perspectives enrich the connotation of children basketball game. Recommended for children of color psychology, cartoon and environmentally friendly material production aids, and increase research efforts basketball aids children, encourage children to sports teachers aids applications.

Keywords: appropriate forms of children basketball, auxiliary equipment, appli, MINI basketball, 3-6 years old children, teaching

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Authors: Min Kyu Kim, Eun Young Lee, Dong Woo Son, Yoon Seok Chang

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In this paper, we introduce a flexible box erecting machine (BEM) that swiftly and automatically transforms cardboard into a three dimensional box. Recently, the parcel service and home-shopping industries have grown rapidly, and there is an increasing need for various box types to ship various products. However, workers cannot fold thousands of boxes manually in a day. As such, automatic BEMs are garnering greater attention. This study takes equipment operation into consideration as well as mechanical improvements in order to design a BEM that is able to outperform its conventional counterparts. We analyzed six dispatching rules – First In First Out (FIFO), Shortest Processing Time (SPT), Earliest Due Date (EDD), Setup Avoidance, EDD + SPT, and EDD + Setup Avoidance – to determine which one was most suitable for BEM operation. Consequently, SPT and Setup Avoidance were found to be the most critical rules, followed by EDD + Setup Avoidance, EDD + SPT, EDD, and FIFO. This hierarchy was valid for both our conventional BEM and our new flexible BEM from the viewpoint of processing time. We believe that this research can contribute to flexible BEM management, which has the potential to increase productivity and convenience.

Keywords: automation, box erecting machine, dispatching rule, setup time

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Authors: Laleh Boroumand, Muhammad Shiraz, Abdullah Gani, Rashid Hafeez Khokhar

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Cloud computing being the use of hardware and software that are delivered as a service over a network has its application in the area of health care. Due to the emergency cases reported in most of the medical centers, prompt for an efficient scheme to make health data available with less response time. To this end, we propose a mobile global healthcare village (MGHV) model that combines the components of three deployment model which include country, continent and global health cloud to help in solving the problem mentioned above. In the creation of continent model, two (2) data centers are created of which one is local and the other is global. The local replay the request of residence within the continent, whereas the global replay the requirements of others. With the methods adopted, there is an assurance of the availability of relevant medical data to patients, specialists, and emergency staffs regardless of locations and time. From our intensive experiment using the simulation approach, it was observed that, broker policy scheme with respect to optimized response time, yields a very good performance in terms of reduction in response time. Though, our results are comparable to others when there is an increase in the number of virtual machines (80-640 virtual machines). The proportionality in increase of response time is within 9%. The results gotten from our simulation experiments shows that utilizing MGHV leads to the reduction of health care expenditures and helps in solving the problems of unqualified medical staffs faced by both developed and developing countries.

Keywords: cloud computing (MCC), e-healthcare, availability, response time, service broker policy

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Authors: Danilo Pesce, Emilio Paolucci, Mariolina Affatato

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Looking at the future and at the post-digital era, innovations commonly tend to dismiss the old and replace it with the new. The aim of this research is to study the role that digital innovation can play alongside the information chain within the traditional sectors and the subsequent value creation opportunities that actors and stakeholders can exploit. By drawing on a wide body of literature on innovation and strategic management and by conducting a case study on the cultural heritage industry, namely Google Arts & Culture, this study shows that technology augments complements, and amplifies the way people experience their cultural interests and experience. Furthermore, the study shows a process of democratization of art since museums can exploit new digital and virtual ways to distribute art globally. Moreover, new needs arose from the 2020 pandemic that hit and forced the world to a state of cultural fasting and caused a radical transformation of the paradigm online vs. onsite. Finally, the study highlights the capabilities that are emerging at different stages of the value chain, owing to the technological innovation available in the market. In essence, this research underlines the role of Google in allowing museums to reach users worldwide, thus unlocking new mechanisms of value creation in the cultural heritage industry. Likewise, this study points out how Google provides value to users by means of increasing the provision of artworks, improving the audience engagement and virtual experience, and providing new ways to access the online contents. The paper ends with a discussion of managerial and policy-making implications.

Keywords: big data, digital platforms, digital transformation, digitization, Google Arts and Culture, stakeholders’ interests

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Jae-Yong Lee, Gyu-Hyun Bae, Yun-Soo Chung, Dae-Sub Kim, Jae-Sung Bae

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In general, fabric is heat-treated using a stenter machine in order to dry and fix its shape. It is important to shape before the heat treatment because it is difficult to revert back once the fabric is formed. To produce the product of right shape, camera type weft straightener has been applied recently to capture and process fabric images quickly. It is more powerful in determining the final textile quality rather than photo-sensor. Positioning in front of a stenter machine, weft straightener helps to spread fabric evenly and control the angle between warp and weft constantly as right angle by handling skew and bow rollers. To process this tricky procedure, the structural analysis should be carried out in advance, based on which, its control technology can be drawn. A structural analysis is to figure out the specific contact/slippage characteristics between fabric and roller. We already examined the applicability of camera type weft straightener to plain weave fabric and found its possibility and the specific working condition of machine and rollers. In this research, we aimed to explore another applicability of camera type weft straightener. Namely, we tried to figure out camera type weft straightener can be used for fabrics. To find out the optimum condition, we increased the number of rollers. The analysis is done by ANSYS software using Finite Element Analysis method. The control function is demonstrated by experiment. In conclusion, the structural analysis of weft straightener is done to identify a specific characteristic between roller and fabrics. The control of skew and bow roller is done to decrease the error of the angle between warp and weft. Finally, it is proved that camera type straightener can also be used for the special fabrics.

Keywords: camera type weft straightener, structure analysis, control, skew and bow roller

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Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: J. S. M. Yang, G. E. T. Chua

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Diploma in Optometry (OPT) course is a three-year program offered by Ngee Ann Polytechnic (NP) to train students to provide primary eye care. Students are equipped with foundational conceptual knowledge and practical skills in the first three semesters before clinical modules in fourth to six semesters. In the clinical modules, students typically have difficulties in integrating the acquired knowledge and skills from the past semesters to perform general eye examinations on public patients at NP Optometry Centre (NPOC). To help the students overcome the challenge, a web-based game Virtual Optometrist (VO) was developed to help students apply their skills and knowledge through scenario-based learning. It consisted of two interfaces, Optical Practice Counter (OPC) and Optometric Consultation Room (OCR), to provide two simulated settings for authentic learning experiences. In OPC, students would recommend and provide appropriate frame and lens selection based on virtual patient’s case history. In OCR, students would diagnose and manage virtual patients with common ocular conditions. Simulated scenarios provided real-world clinical situations that required contextual application of integrated knowledge from relevant modules. The stages in OPC and OCR are of increasing complexity to align to expected students’ clinical competency as they progress to more senior semesters. This prevented gameplay fatigue as VO was used over the semesters to achieve different learning outcomes. Numerous feedback opportunities were provided to students based on their decisions to allow individualized learning to take place. The game-based learning element in VO was achieved through the scoreboard and leader board to enhance students' motivation to perform. Scores were based on the speed and accuracy of students’ responses to the questions posed in the simulated scenarios, preparing the students to perform accurately and effectively under time pressure in a realistic optometric environment. Learning analytics was generated in VO’s backend office based on students’ responses, offering real-time data on distinctive and observable learners’ behavior to monitor students’ engagement and learning progress. The backend office allowed versatility to add, edit, and delete scenarios for different intended learning outcomes. Likert Scale was used to measure students’ learning experience with VO for OPT Year 2 and 3 students. The survey results highlighted the learning benefits of implementing VO in the different modules, such as enhancing recall and reinforcement of clinical knowledge for contextual application to develop higher-order thinking skills, increasing efficiency in clinical decision-making, facilitating learning through immediate feedback and second attempts, providing exposure to common and significant ocular conditions, and training effective communication skills. The results showed that VO has been useful in reinforcing optometry students’ learning and supporting the development of higher-order thinking, increasing efficiency in clinical decision-making, and allowing students to learn from their mistakes with immediate feedback and second attempts. VO also exposed the students to diverse ocular conditions through simulated real-world clinical scenarios, which may otherwise not be encountered in NPOC, and promoted effective communication skills.

Keywords: authentic learning, game-based learning, scenario-based learning, simulated clinical scenarios

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Authors: Fredrick Forster, Gareth Ward, Matthew Tubby, Pamela Lithgow, Anne Nortcliffe

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This paper presents the results from a research study of random adult participants accessing one of four different commercially available Virtual Reality (VR) Head Mounted Displays (HMDs) and completing a post user experience reflection questionnaire. The research sort to understand how inclusive commercially available VR HMDs are and identify any associated barriers that could impact the widespread adoption of the devices, specifically in Higher Education (HE). In the UK, education providers are legally required under the Equality Act 2010 to ensure all education facilities are inclusive and reasonable adjustments can be applied appropriately. The research specifically aimed to identify the considerations that academics and learning technologists need to make when adopting the use of commercial VR HMDs in HE classrooms, namely cybersickness, user comfort, Interpupillary Distance, inclusiveness, and user perceptions of VR. The research approach was designed to build upon previously published research on user reflections on presence, usability, and overall HMD comfort, using quantitative and qualitative research methods by way of a questionnaire. The quantitative data included the recording of physical characteristics such as the distance between eye pupils, known as Interpupillary Distance (IPD). VR HMDs require each user’s IPD measurement to enable the focusing of the VR HMDs virtual camera output to the right position in front of the eyes of the user. In addition, the questionnaire captured users’ qualitative reflections and evaluations of the broader accessibility characteristics of the VR HMDs. The initial research activity was accomplished by enabling a random sample of visitors, staff, and students at Canterbury Christ Church University, Kent to use a VR HMD for a set period of time and asking them to complete the post user experience questionnaire. The study identified that there is little correlation between users who experience cyber sickness and car sickness. Also, users with a smaller IPD than average (typically associated with females) were able to use the VR HMDs successfully; however, users with a larger than average IPD reported an impeded experience. This indicates that there is reduced inclusiveness for the tested VR HMDs for users with a higher-than-average IPD which is typically associated with males of certain ethnicities. As action education research, these initial findings will be used to refine the research method and conduct further investigations with the aim to provide verification and validation of the accessibility of current commercial VR HMDs. The conference presentation will report on the research results of the initial study and subsequent follow up studies with a larger variety of adult volunteers.

Keywords: virtual reality, education technology, inclusive technology, higher education

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Zhang Songning, Guan Zheng, Ci Yan, Ding Gangyi

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The application of physical simulation engines in collaborative editing systems has an important background and role. Firstly, physical simulation engines can provide real-world physical simulations, enabling users to interact and collaborate in real time in virtual environments. This provides a more intuitive and immersive experience for collaborative editing systems, allowing users to more accurately perceive and understand various elements and operations in collaborative editing. Secondly, through physical simulation engines, different users can share virtual space and perform real-time collaborative editing within it. This real-time sharing and collaborative editing method helps to synchronize information among team members and improve the efficiency of collaborative work. Through experiments, the average model transmission speed of a single person in the collaborative editing system has increased by 141.91%; the average model processing speed of a single person has increased by 134.2%; the average processing flow rate of a single person has increased by 175.19%; the overall efficiency improvement rate of a single person has increased by 150.43%. With the increase in the number of users, the overall efficiency remains stable, and the physical simulation engine running status collaborative editing system also has horizontal scalability. It is not difficult to see that the design and implementation of a collaborative editing system based on physical simulation engines not only enriches the user experience but also optimizes the effectiveness of team collaboration, providing new possibilities for collaborative work.

Keywords: physics engine, simulation technology, collaborative editing, system design, data transmission

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Authors: M. Duran Toksari, Berrin Ucarkus

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In this paper, we propose two algorithms to optimally solve makespan and total completion time scheduling problems with learning effect and job dependent delivery times in a single machine environment. The delivery time is the extra time to eliminate adverse effect between the main processing and delivery to the customer. In this paper, we introduce the job dependent delivery times for some single machine scheduling problems with position dependent learning effect, which are makespan are total completion. The results with respect to two algorithms proposed for solving of the each problem are compared with LINGO solutions for 50-jobs, 100-jobs and 150-jobs problems. The proposed algorithms can find the same results in shorter time.

Keywords: delivery Times, learning effect, makespan, scheduling, total completion time

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Authors: Birmohan Singh, V.K.Jain

Abstract:

Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

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Authors: Faiza Babur Khan, Sohail Asghar

Abstract:

With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

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Authors: Ozcan B., Koca B., Tuzcuoglu E., Cavusoglu S., Efe A., Bayraktar S.

Abstract:

A washing machine is mainly used for removing any types of dirt and stains and also eliminating malodorous substances from textile surfaces. Stains originate from various sources from the human body to environmental contamination. Therefore, there are various methods for removing them. They are roughly classified into four different groups: oily (greasy) stains, particulate stains, enzymatic stains and bleachable (oxidizable) stains. Oily stains on clothes surfaces are a common result of being in contact with organic substances of the human body (e.g. perspiration, skin shedding and sebum) or by being exposed to an oily environmental pollutant (e.g. oily foods). Studies showed that human sebum is major component of oily soil found on the garments, and if it is aged under the several environmental conditions, it can generate obstacle yellow stains on the textile surface. In this study, a parametric study was carried out to investigate the key factors affecting the cleaning performance (specifically sebum removal performance) of a washing machine. These parameters are mechanical agitation percentage of tumble, consumed water and total washing period. A full factorial design of the experiment is used to capture all the possible parametric interactions using Minitab 2021 statistical program. Tests are carried out with commercial liquid detergent and 2 different types of sebum-soiled cotton and cotton + polyester fabrics. Parametric results revealed that for both test samples, increasing the washing time and the mechanical agitation could lead to a much better removal result of sebum. However, for each sample, the water amount had different outcomes. Increasing the water amount decreases the performance of cotton + polyester fabrics, while it is favorable for cotton fabric. Besides this, it was also discovered that the type of textile can greatly affect the sebum removal performance. Results showed that cotton + polyester fabrics are much easier to clean compared to cotton fabric

Keywords: laundry, washing machine, low-temperature washing, cold wash, washing efficiency index, sustainability, cleaning performance, stain removal, oily soil, sebum, yellowing

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

Abstract:

This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Faishal Muhammad, Erlinda Muslim, Nadia Faradilla, Sayidul Fikri

Abstract:

To deal with the increase of mass transportation needs problem, PT. Kereta Commuter Jabodetabek (KCJ) implements Commuter Vending Machine (C-VIM) as the solution. For that background, C-VIM is implemented as a substitute to the conventional ticket windows with the purposes to make transaction process more efficient and to introduce self-service technology to the commuter line user. However, this implementation causing problems and long queues when the user is not accustomed to using the machine. The objective of this research is to evaluate user experience after using the commuter vending machine. The goal is to analyze the existing user experience problem and to achieve a better user experience design. The evaluation method is done by giving task scenario according to the features offered by the machine. The features are daily insured ticket sales, ticket refund, and multi-trip card top up. There 20 peoples that separated into two groups of respondents involved in this research, which consist of 5 males and 5 females each group. The experienced and inexperienced user to prove that there is a significant difference between both groups in the measurement. The user experience is measured by both quantitative and qualitative measurement. The quantitative measurement includes the user performance metrics such as task success, time on task, error, efficiency, and learnability. The qualitative measurement includes system usability scale questionnaire (SUS), questionnaire for user interface satisfaction (QUIS), and retrospective think aloud (RTA). Usability performance metrics shows that 4 out of 5 indicators are significantly different in both group. This shows that the inexperienced group is having a problem when using the C-VIM. Conventional ticket windows also show a better usability performance metrics compared to the C-VIM. From the data processing, the experienced group give the SUS score of 62 with the acceptability scale of 'marginal low', grade scale of “D”, and the adjective ratings of 'good' while the inexperienced group gives the SUS score of 51 with the acceptability scale of 'marginal low', grade scale of 'F', and the adjective ratings of 'ok'. This shows that both groups give a low score on the system usability scale. The QUIS score of the experienced group is 69,18 and the inexperienced group is 64,20. This shows the average QUIS score below 70 which indicate a problem with the user interface. RTA was done to obtain user experience issue when using C-VIM through interview protocols. The issue obtained then sorted using pareto concept and diagram. The solution of this research is interface redesign using activity relationship chart. This method resulted in a better interface with an average SUS score of 72,25, with the acceptable scale of 'acceptable', grade scale of 'B', and the adjective ratings of 'excellent'. From the time on task indicator of performance metrics also shows a significant better time by using the new interface design. Result in this study shows that C-VIM not yet have a good performance and user experience.

Keywords: activity relationship chart, commuter line vending machine, system usability scale, usability performance metrics, user experience evaluation

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Authors: Yuxi Zhu, Zhenqian Chen

Abstract:

It is of great interest to investigate the thermal properties of non-precious metal catalysts for Proton exchange membrane fuel cell (PEMFC) based on the thermal management requirements. Due to the low symmetry of materials, to accurately obtain the thermal conductivity of materials, it is necessary to obtain the second and third order force constants by combining density functional theory and machine learning interatomic potential. To be specific, the interatomic force constants are obtained by moment tensor potential (MTP), which is trained by the computational trajectory of Ab initio molecular dynamics (AIMD) at 50, 300, 600, and 900 K for 1 ps each, with a time step of 1 fs in the AIMD computation. And then the thermal conductivity can be obtained by solving the Boltzmann transport equation. In this paper, the thermal transport properties of single metal atom catalysts are studied for the first time to our best knowledge by machine-learning interatomic potential (MLIP). Results show that the single metal atom catalysts exhibit anisotropic thermal conductivities and partially exhibit good thermal conductivity. The average lattice thermal conductivities of G-FeN₄, G-CoN₄ and G-NiN₄ at 300 K are 88.61 W/mK, 205.32 W/mK and 210.57 W/mK, respectively. While other single metal atom catalysts show low thermal conductivity due to their low phonon lifetime. The results also show that low-frequency phonons (0-10 THz) dominate thermal transport properties. The results provide theoretical insights into the application of single metal atom catalysts in thermal management.

Keywords: proton exchange membrane fuel cell, single metal atom catalysts, density functional theory, thermal conductivity, machine-learning interatomic potential

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Authors: M. Govindarajan

Abstract:

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

Procedia PDF Downloads 248