Search results for: virtual Machine

Authors: Yujeong Shin, Seong Jin Cho, Jin Ho Kim

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In the textile-weaving machine, the reed is the core component to separate thousands of strands of yarn and to produce the fabric in a continuous high-speed movement. In addition, the reed affects the quality of the fiber. Therefore, the wire forming analysis of the main raw materials of the reed needs to be considered. Roll-forming is a key technology among the manufacturing process of reed wire using textile machine. A simulation of roll-forming line in accordance with the reduction rate is performed using LS-DYNA. The upper roller, fixed roller and reed wire are modeled by finite element. The roller is set to be rigid body and the wire of SUS430 is set to be flexible body. We predict the variation of the cross-sectional shape of the wire depending on the reduction ratio.

Keywords: textile machine, reed, rolling, reduction ratio, wire

Procedia PDF Downloads 374

Authors: Pawamana Ramachandra, Jitesh Gupta, Saranga P. Pogula

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In verification of high volume and complex packet processing IPs, finer control of flow management aspects (for example, rate, bits/sec etc.) per flow class (or a virtual channel or a software thread) is needed. When any Software/Universal Verification Methodology (UVM) thread arbitration is left to the simulator (e.g., Verilog Compiler Simulator (VCS) or Incisive Enterprise Simulator core simulation engine (NCSIM)), it is hard to predict its pattern of resulting distribution of bandwidth by the simulator thread arbitration. In many cases, the patterns desired in a test scenario may not be accomplished as the simulator might give a different distribution than what was required. This can lead to missing multiple traffic scenarios, specifically deadlock and starvation related. We invented a component (namely Flow Manager Verification IP) to be intervening between the application (test case) and the protocol VIP (with UVM sequencer) to control the bandwidth per thread/virtual channel/flow. The Flow Manager has knobs visible to the UVM sequence/test to configure the required distribution of rate per thread/virtual channel/flow. This works seamlessly and produces rate stimuli to further harness the Design Under Test (DUT) with asymmetric inputs compared to the programmed bandwidth/Quality of Service (QoS) distributions in the Design Under Test.

Keywords: flow manager, UVM sequencer, rated traffic generation, quality of service

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Authors: Ali Allahverdi, Harun Aydilek, Asiye Aydilek

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Minimizing the number of tardy jobs is an important factor to consider while making scheduling decisions. This is because on-time shipments are vital for lowering cost and increasing customers’ satisfaction. This paper addresses the single machine scheduling problem with the objective of minimizing the number of tardy jobs. The only known information is the lower and upper bounds for processing times, and deterministic job due dates. A dominance relation is established, and an algorithm is proposed. Several heuristics are generated from the proposed algorithm. Computational analysis indicates that the performance of one of the heuristics is very close to the optimal solution, i.e., on average, less than 1.5 % from the optimal solution.

Keywords: single machine scheduling, number of tardy jobs, heuristi, lower and upper bounds

Procedia PDF Downloads 555

Authors: N. Sateesh, C. S. P. Rao, K. Satyanarayana, C. Rajashekar

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This paper proposes a development of a CAD/CAM system for complex profiles of various machine members using a synthetic curve i.e. B-spline. Conventional methods in designing and manufacturing of complex profiles are tedious and time consuming. Even programming those on a computer numerical control (CNC) machine can be a difficult job because of the complexity of the profiles. The system developed provides graphical and numerical representation B-spline profile for any given input. In this paper, the system is applicable to represent a cam profile with B-spline and attempt is made to improve the follower motion.

Keywords: plate-cams, cam profile, b-spline, computer numerical control (CNC), computer aided design and computer aided manufacturing (CAD/CAM), R-D-R-D (rise-dwell-return-dwell)

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Authors: Sanjal Gavande, Thomas Mazzuchi, Shahram Sarkani

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In a complex aerospace operational environment, identifying failures in a procedure involving multiple human-machine interactions are difficult. These failures could lead to accidents causing loss of hardware or human life. The likelihood of failure further increases if operational procedures are tested for a novel system with multiple human-machine interfaces and with no prior performance data. The existing approach in the literature of reviewing complex operational tasks in a flowchart or tabular form doesn’t provide any insight into potential system failures due to human decision-making ability. To address these challenges, this research explores an agent-based simulation approach for reliability assessment and fault detection in complex human-machine systems while utilizing a human decision-making model. The simulation will predict the emergent behavior of the system due to the interaction between humans and their decision-making capability with the varying states of the machine and vice-versa. Overall system reliability will be evaluated based on a defined set of success-criteria conditions and the number of recorded failures over an assigned limit of Monte Carlo runs. The study also aims at identifying high-likelihood failure locations for the system. The research concludes that system reliability and failures can be effectively calculated when individual human and machine agent states are clearly defined. This research is limited to the operations phase of a system lifecycle process in an aerospace environment only. Further exploration of the proposed agent-based and human decision-making model will be required to allow for a greater understanding of this topic for application outside of the operations domain.

Keywords: agent-based model, complex human-machine system, human decision-making model, system reliability assessment

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Authors: Vani Singhal, Jitendra Parmar, Satyendra Singh Chouhan

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Unseen class discovery has now become an important part of a machine-learning algorithm to judge new classes. Unseen classes are the classes on which the machine learning model is not trained on. With the advancement in technology and AI replacing humans, the amount of data has increased to the next level. So while implementing a model on real-world examples, we come across unseen new classes. Our aim is to find the number of unseen classes by using a hierarchical-based active learning algorithm. The algorithm is based on hierarchical clustering as well as active sampling. The number of clusters that we will get in the end will give the number of unseen classes. The total clusters will also contain some clusters that have unseen classes. Instead of first discovering unseen classes and then finding their number, we directly calculated the number by applying the algorithm. The dataset used is for intent classification. The target data is the intent of the corresponding query. We conclude that when the machine learning model will encounter real-world data, it will automatically find the number of unseen classes. In the future, our next work would be to label these unseen classes correctly.

Keywords: active sampling, hierarchical clustering, open world learning, unseen class discovery

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Mohammed Arif, Abdessalam Kifouche

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Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based.

Keywords: speaker verification, text-independent, support vector machine, Gaussian mixture model, cepstral analysis

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Authors: Sakib Hasn, Shahid Anwar

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This study highlights the vital importance of assessing the Simulator Sickness Questionnaire and presence measures as virtual reality (VR) incorporation into language teaching gains popularity. To address user discomfort, which prevents efficient learning in VR environments, the measurement of SSQ becomes crucial. Additionally, evaluating presence metrics is essential to determine the level of engagement and immersion, both crucial for rich language learning experiences. This paper designs a VR-based Chinese language application and proposes a thorough test technique aimed at systematically analyzing SSQ and presence measures. Subjective tests and data analysis were carried out to highlight the significance of addressing user discomfort in VR language education. The results of this study shed light on the difficulties posed by user discomfort in VR language learning and offer insightful advice on how to improve VR language learning applications. Furthermore, the outcome of the research explores ‘VR-based language education,’ ‘inclusive language learning platforms," and "cross-cultural communication,’ highlighting the potential for VR to facilitate language learning across diverse cultural backgrounds. Overall, the analysis results contribute to the enrichment of language learning experiences in the virtual realm and underscore the need for continued exploration and improvement in this field.

Keywords: virtual reality (VR), language education, simulator sickness questionnaire, presence metrics, VR-based Chinese language education

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Bonnie Clough, Tamara Ownsworth, Vladimir Estivill-Castro, Matt Stainer, Rene Hexel, Andrew Bulmer, Wendy Moyle, Allison Waters, David Neumann, Jayke Bennett

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The success of global vaccination programs is reliant on the uptake of vaccines to achieve herd immunity. Yet, many individuals do not obtain vaccines or venipuncture procedures when needed. Whilst health education may be effective for those individuals who are hesitant due to safety or efficacy concerns, for many of these individuals, the primary concern relates to blood or injection fear or phobia (BII). BII is highly prevalent and associated with a range of negative health impacts, both at individual and population levels. Exposure therapy is an efficacious treatment for specific phobias, including BII, but has high patient dropout and low implementation by therapists. Whilst virtual reality approaches exposure therapy may be more acceptable, they have similarly low rates of implementation by therapists and are often difficult to tailor to an individual client’s needs. It was proposed that a piloted robot may be able to adequately facilitate fear induction and be an acceptable approach to exposure therapy. The current study examined fear induction responses, acceptability, and feasibility of a piloted robot for BII exposure. A Nao humanoid robot was programmed to connect with a virtual reality head-mounted display, enabling live streaming and exploration of real environments from a distance. Thirty adult participants with BII fear were randomly assigned to robot-pilot or virtual reality exposure conditions in a laboratory-based fear exposure task. All participants also completed a computer-based two-dimensional exposure task, with an order of conditions counterbalanced across participants. Measures included fear (heart rate variability, galvanic skin response, stress indices, and subjective units of distress), engagement with a feared stimulus (eye gaze: time to first fixation and a total number of fixations), acceptability, and perceived treatment credibility. Preliminary results indicate that fear responses can be adequately induced via a robot-piloted platform. Further results will be discussed, as will implications for the treatment of BII phobia and other fears. It is anticipated that piloted robots may provide a useful platform for facilitating exposure therapy, being more acceptable than in-vivo exposure and more flexible than virtual reality exposure.

Keywords: anxiety, digital mental health, exposure therapy, phobia, robot, virtual reality

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Authors: Eyhab El-Kharashi, Maher El-Dessouki

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The paper is dedicated to precise assessment of the induction motor output energy during the unbalanced operation. Since many years ago and until now the voltage complex unbalance factor (CVUF) is used only to assess the output energy of the induction motor while this output energy for asymmetry supply voltage does not depend on the value of unbalanced voltage only but also on the machine parameters. The paper illustrates the variation of the two unbalance factors, complex voltage unbalance factor (CVUF) and impedance unbalance factor (IUF), with positive sequence voltage component, reveals that degree and manner of unbalance in supply voltage. From this point of view the paper delineates the current unbalance factor (CUF) to exactly reflect the output energy during unbalanced operation. The paper proceeds to illustrate the importance of using this factor in the multi-machine system to precise prediction of the output energy during the unbalanced operation. The use of the proposed unbalance factor (CUF) avoids the accumulation of the error due to more than one machine in the system which is expected if only the complex voltage unbalance factor (CVUF) is used.

Keywords: induction motor, electromagnetic torque, voltage unbalance, energy conversion

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Authors: Maryam Sheikhzadeh Noshabadi

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There is no doubt that the Internet has connected the entire world. As a result of this flexible environment, virtual businesses have grown in importance and become one of the most important types of businesses. Although many governments use the internet and have free access to it, some are not happy with the free space and wide accessibility. As a result of recent events and civil protests in Iran, the country's government leaders have decided to once again cut off and filter its free and global Internet. Several issues were impacted by this decision; this includes the lives of businesses that were formed in virtual spaces platform. In this study, we explored the definitive impact of the Internet in Iran in September 1401, using semi-structured interviews with 20 digital marketing activists. This group was discussed in detail in terms of their financial and psychological damages. As a result of these conditions, this group has experienced a crisis of livelihood.

Keywords: internet, Iran, filtering, digital marketing.

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Authors: Amani Alsaqqaf, Frederick W. B. Li

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Field trips are essential methods of learning in different subjects, and in recent times, there has been a reduction in the number of field trips (FTs) across all learning levels around the world. Virtual field trips (VFTs) in game environments provide FT experience based on the experiential learning theory (ELT). A conceptual framework for designing virtual field trip games (VFTGs) is developed with an aim to support game designers and educators to produce an effective FT experience where technology would enhance education. The conceptual framework quantifies ELT as an internal economy to link learning elements to game mechanics such as feedback loops which leads to facilitating VFTGs design and implementation. This study assesses the conceptual framework for designing VFTGs by investigating the possibility of applying immersive VFTGs in a secondary classroom and compare them with traditional learning that uses video clips and PowerPoint slides from the viewpoint of students’ perceived motivation, presence, and learning. The assessment is achieved by evaluating the learning performance and learner experience of a prototype VFT game, Island of Volcanoes. A quasi-experiment was conducted with 60 secondary school students. The findings of this study are that the VFTG enhanced learning performance to a better level than did the traditional way of learning, and in addition, it provided motivation and a general feeling of presence in the VFTG environment.

Keywords: conceptual framework, game-based learning, game design, virtual field trip game

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Authors: P. Saee, Baiju Gopal

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YouTube has seen a surge in the recent years in the number of creators opening up about their mental illness on the video-sharing platform. In documenting their mental health, YouTubers perform an identity of their mental illness in the online world. Identity performance is a theory under identity research that has been readily applied to illness narratives and internet studies. Furthermore, in India, suffering from mental illnesses is regarded with stigma, making the act of taking mental health from a personal to a public space on YouTube a phenomenon worth exploring. Thus, the aim of this paper is to analyse the mental illness narratives of Indian YouTubers for understanding its performance in the virtual world. For this purpose, thematic narrative analysis on the interviews of four Indian YouTubers was conducted. This data was synthesized with analysis of the videos the YouTubers had uploaded on their channel sharing about their mental illness. The narratives of the participants shed light on two significant presentations that they engage in: (a) the identity of a survivor/fighter and (b) the identity of a silent sufferer. Further, the participants used metaphors to describe their illness, thereby co-constructing a corresponding identity based on their particular metaphors. Lastly, the process of bringing mental illness from back stage to front stage on YouTube involves a shift in the audience, from being rejecting and invalidating in real life to being supportive and encouraging in the virtual space. Limitations and implications for future research were outlined.

Keywords: cyber-psychology, internet, media, mental health, mental illness, technology

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Authors: Pin Xiu Chen, Shang Liang Chen

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A virtualized software controller is developed in this research to replace traditional hardware control units. This virtualized software controller transfers motion interpolation calculations from the motion control units of end devices to edge computing platforms, thereby reducing the end devices' computational load and hardware requirements and making maintenance and updates easier. The study also applies the concept of microservices, dividing the control system into several small functional modules and then deploy into a cloud data server. This reduces the interdependency among modules and enhances the overall system's flexibility and scalability. Finally, with containerization technology, the system can be deployed and started in a matter of seconds, which is more efficient than traditional virtual machine deployment methods. Furthermore, this virtualized software controller communicates with end control devices via wireless networks, making the placement of production equipment or the redesign of processes more flexible and no longer limited by physical wiring. To handle the large data flow and maintain low-latency transmission, this study integrates 5G technology, fully utilizing its high speed, wide bandwidth, and low latency features to achieve rapid and stable remote machine control. An experimental setup is designed to verify the feasibility and test the performance of this framework. This study designs a smart manufacturing site with a 5G communication architecture, serving as a field for experimental data collection and performance testing. The smart manufacturing site includes one robotic arm, three Computer Numerical Control machine tools, several Input/Output ports, and an edge computing architecture. All machinery information is uploaded to edge computing servers and cloud servers via 5G communication and the Internet of Things framework. After analysis and computation, this information is converted into motion control commands, which are transmitted back to the relevant machinery for motion control through 5G communication. The communication time intervals at each stage are calculated using the C++ chrono library to measure the time difference for each command transmission. The relevant test results will be organized and displayed in the full-text.

Keywords: 5G, MEC, microservices, virtualized software controller, smart manufacturing

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Authors: Hadi Aghazadeh, Mohammadreza Naeimi, Seyed Ebrahim Afjei, Alireza Siadatan

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Torque ripple, maximum torque and high efficiency are important issues in synchronous reluctance machine (SynRM). This paper presents a view on design of a high efficiency, low torque ripple and high torque density SynRM. To achieve this goal SynRM parameters is calculated (such as insulation ratios in the d-and q-axes and the rotor slot pitch), while the torque ripple can be minimized by determining the best rotor slot pitch in the d-axis. The presented analytical-finite element method (FEM) approach gives the optimum distribution of air gap and iron portion for the maximizing torque density with minimum torque ripple.

Keywords: torque ripple, efficiency, insulation ratio, FEM, synchronous reluctance machine (SynRM), induction motor (IM)

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Nuran Öze

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Communities and societies have been changing towards computer mediated communication. This paper explores online and offline identities and how relationships are formed and negotiated within internet environments which offer opportunities for people who know each other offline and move into relationships online. The expectations and norms of behavior within everyday life cause people to be embodied self. According to the age categories of Turkish Cypriots, their measurements of attitudes in Facebook will be investigated. Face-to-face field research and semi-structured interview methods are used in the study. Face-to-face interview has been done with Turkish Cypriots who are using Facebook already. According to the study, in constructing a linkage between real and virtual identities mostly affected from societal relations serves as a societal grooming tool for Turkish Cypriots.

Keywords: facebook, identity, social media, virtual reality

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Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: Paulomi Polly Burey, Mark Lynch

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It has been shown that laboratory activities can help cement understanding of theoretical concepts, but it is difficult to deliver such an activity to an online cohort and issues such as occupational health and safety in the students’ learning environment need to be considered. Chemistry, in particular, is one of the sciences where practical experience is beneficial for learning, however typical university experiments may not be suitable for the learning environment of a distance learner. Food provides an ideal medium for demonstrating chemical concepts, and along with a few simple physical and virtual tools provided by educators, analytical chemistry can be experienced by distance learners. Food chemistry experiments were designed to be carried out in a home-based environment that 1) Had sufficient scientific rigour and skill-building to reinforce theoretical concepts; 2) Were safe for use at home by university students and 3) Had the potential to enhance student learning by linking simple hands-on laboratory activities with high-level virtual science. Two main components of the resources were developed, a home laboratory experiment component, and a virtual laboratory component. For the home laboratory component, students were provided with laboratory kits, as well as a list of supplementary inexpensive chemical items that they could purchase from hardware stores and supermarkets. The experiments used were typical proximate analyses of food, as well as experiments focused on techniques such as spectrophotometry and chromatography. Written instructions for each experiment coupled with video laboratory demonstrations were used to train students on appropriate laboratory technique. Data that students collected in their home laboratory environment was collated across the class through shared documents, so that the group could carry out statistical analysis and experience a full laboratory experience from their own home. For the virtual laboratory component, students were able to view a laboratory safety induction and advised on good characteristics of a home laboratory space prior to carrying out their experiments. Following on from this activity, students observed laboratory demonstrations of the experimental series they would carry out in their learning environment. Finally, students were embedded in a virtual laboratory environment to experience complex chemical analyses with equipment that would be too costly and sensitive to be housed in their learning environment. To investigate the impact of the intervention, students were surveyed before and after the laboratory series to evaluate engagement and satisfaction with the course. Students were also assessed on their understanding of theoretical chemical concepts before and after the laboratory series to determine the impact on their learning. At the end of the intervention, focus groups were run to determine which aspects helped and hindered learning. It was found that the physical experiments helped students to understand laboratory technique, as well as methodology interpretation, particularly if they had not been in such a laboratory environment before. The virtual learning environment aided learning as it could be utilized for longer than a typical physical laboratory class, thus allowing further time on understanding techniques.

Keywords: chemistry, food science, future pedagogy, STEM education

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Shah Krupa Rajendra, Amit Kumar

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Increasing use of traditional vehicles driven by internal combustion engine is responsible for the environmental pollution. Further, it leads to depletion of limited energy resources. Therefore, it is required to explore alternative energy sources for the transportation. The promising solution is to use electric vehicle. However, it suffers from limited driving range. Regenerative braking increases the range of the electric vehicle to a certain extent. In this paper, a novel methodology utilizing regenerative braking is described. The model comprising of DC machine, feedback based boost converter and micro-controller is proposed. The suggested method is very simple and reliable. The proposed model successfully shows the energy being saved into during regenerative braking process.

Keywords: boost converter, DC machine, electric vehicle, micro-controller, regenerative braking

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Authors: Dawoon Choi, Jian Li, Yunhyun Cho

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Axial Flux Permanent Magnet (AFPM) machines have been widely used in various applications due to their important merits, such as compact structure, high efficiency and high torque density. This paper presents one of the most important characteristics in the design process of the AFPM device, which is a recent issue. To design AFPM machine, the predicting electromagnetic forces between the permanent magnets and stator is important. Because of the magnitude of electromagnetic force affects many characteristics such as machine size, noise, vibration, and quality of output power. Theoretically, this force is canceled by the equilibrium of force when it is in the middle of the gap, but it is inevitable to deviate due to manufacturing problems in actual machine. Such as large scale wind generator, because of the huge attractive force between rotor and stator disks, this is more serious in getting large power applications such as large. This paper represents the characteristics of Double-Stator Inner –Rotor AFPM machines when it has rotor eccentricity. And, unbalanced air-gap and inclined air-gap condition which is caused by rotor offset and tilt in a double-stator single inner-rotor AFPM machine are each studied in electromagnetic and mechanical aspects. The output voltage and cogging torque under un-normal air-gap condition of AF machines are firstly calculated using a combined analytical and numerical methods, followed by a structure analysis to study the effect to mechanical stress, deformation and bending forces on bearings. Results and conclusions given in this paper are instructive for the successful development of AFPM machines.

Keywords: axial flux permanent magnet machine, inclined air gap, unbalanced air gap, rotor eccentricity

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Authors: Sanskar, Abhinav Pal, Aryush Gupta, Sushil Kumar Mishra

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One of the critical and tedious assignments in agricultural practices is the detection of diseases on vegetation. Agricultural production is very important in today’s economy because plant diseases are common, and early detection of plant diseases is important in agriculture. Automatic detection of such early diseases is useful because it reduces control efforts in large productive farms. Using digital image processing and machine learning algorithms, this paper presents a method for plant disease detection. Detection of the disease occurs on different leaves of the plant. The proposed system for plant disease detection is simple and computationally efficient, requiring less time than learning-based approaches. The accuracy of various plant and foliar diseases is calculated and presented in this paper.

Keywords: plant diseases, machine learning, image processing, deep learning

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Authors: Neerav Kumar, Naumaan Nayyar, Sharath Kashyap

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Most companies see an increase in the adoption of machine learning (ML) applications across internal and external-facing use cases. ML applications vend output either in batch or real-time patterns. A complete batch ML pipeline architecture comprises data sourcing, feature engineering, model training, model deployment, model output vending into a data store for downstream application. Due to unclear role expectations, we have observed that scientists specializing in building and optimizing models are investing significant efforts into building the other components of the architecture, which we do not believe is the best use of scientists’ bandwidth. We propose a system architecture created using AWS services that bring industry best practices to managing the workflow and simplifies the process of model deployment and end-to-end data integration for an ML application. This narrows down the scope of scientists’ work to model building and refinement while specialized data engineers take over the deployment, pipeline orchestration, data quality, data permission system, etc. The pipeline infrastructure is built and deployed as code (using terraform, cdk, cloudformation, etc.) which makes it easy to replicate and/or extend the architecture to other models that are used in an organization.

Keywords: data pipeline, machine learning, AWS, architecture, batch machine learning

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Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

Abstract:

Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

Procedia PDF Downloads 109

Authors: Sonja Gögele, Petra Kletzenbauer

Abstract:

International strategies are ranked as one of the core activities in the development plans of Austrian universities. This has led to numerous promising activities in terms of internationalization (i.e. development of international degree programmes, increased staff and student mobility, and blended international projects). The latest innovative approach in terms of Erasmus+ are so called Blended Intensive Programmes (BIP) which combine jointly delivered teaching and learning elements of at least three participating ERASMUS universities in a virtual and short-term mobility setup. Students who participate in BIP can maintain their study plans at their home institution and include BIP as a parallel activity. This paper presents the experiences of this programme on the topic of sustainable computing hosted by the University of Applied Sciences FH JOANNEUM. By means of an online survey and face-to-face interviews with all stakeholders (20 students, 8 professors), the empirical study addresses the challenges of hosting an international blended learning programme (i.e. virtual phase and on-site intensive phase) and discusses the impact of such activities in terms of internationalization and Englishization. In this context, key roles are assigned to the development of future transnational and transdisciplinary curricula by considering innovative aspects for learning and teaching (i.e. virtual collaboration, research-based learning).

Keywords: internationalization, englishization, short-term mobility, international teaching and learning

Procedia PDF Downloads 120