Search results for: train the trainer

Authors: T. Pakulski, L. Kryza, A. Linossier

Abstract:

The BEar Articulated TeleRobotic Inspection and Clasping Extremity is a lightweight, 5 DoF robotic manipulator for the Berlin Educational Assistant Rover (BEAR). BEAR is one of the educational planetary rovers developed under the Space Rover projects at the Chair of Space Technology of the Technische Universität Berlin. The projects serve to conduct research and train engineers by developing rovers for competitions like the European Rover Challenge and the DLR SpaceBot Cup. BEATRICE is the result of a cost-driven design process to deliver a simple but capable platform for a variety of competition tasks: object grasping and manipulation, inspection, instrument wielding and more. The manipulator’s simple mechatronic design, based on a combination of servomotors and stepper motors with planetary gearboxes, also makes it a practical tool for developing embedded control systems. The platform’s initial implementation relies on tele-operated control but is fully instrumented for future autonomous functionality. This paper describes BEATRICE’s development from its preliminary link model to its structural and mechatronic design, embedded control and AI and T. In parallel, it examines the influence of budget constraints and high personnel turnover commonly associated with student teams on the manipulator’s design. Finally, it comments on the utility of robot design projects for educating future engineers.

Keywords: education, low-cost, manipulator, robotics, rover

Procedia PDF Downloads 228

Authors: Muhammad Ali

Abstract:

Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

Procedia PDF Downloads 96

Authors: B. Moaveni, S. Morovati

Abstract:

In recent years, increasing the usage of railway transportations especially in developing countries caused more attention to control systems railway vehicles. Consequently, designing and implementing the modern control systems to improve the operating performance of trains and locomotives become one of the main concerns of researches. Dynamic braking systems is an important safety system which controls the amount of braking torque generated by traction motors, to keep the adhesion coefficient between the wheel-sets and rail road in optimum bound. Adhesion force has an important role to control the braking distance and prevent the wheels from slipping during the braking process. Cascade control structure is one of the best control methods for the wide range of industrial plants in the presence of disturbances and errors. This paper presents cascade control structure based on two forward simple controllers with two feedback loops to control the slip ratio and braking torque. In this structure, the inner loop controls the angular velocity and the outer loop control the longitudinal velocity of the locomotive that its dynamic is slower than the dynamic of angular velocity. This control structure by controlling the torque of DC traction motors, tries to track the desired velocity profile to access the predefined braking distance and to control the slip ratio. Simulation results are employed to show the effectiveness of the introduced methodology in dynamic braking system.

Keywords: cascade control, dynamic braking system, DC traction motors, slip control

Procedia PDF Downloads 339

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

Abstract:

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 162

Authors: Reza Mohammadzadeh

Abstract:

The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

Procedia PDF Downloads 247

Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

Abstract:

This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

Procedia PDF Downloads 106

Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

Abstract:

In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

Procedia PDF Downloads 325

Authors: Yilin Liao, Hewen Li, Paula McConvey

Abstract:

Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

Procedia PDF Downloads 70

Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

Abstract:

Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

Procedia PDF Downloads 66

Authors: Jingting Luo, Yong Wang, Ying Wang

Abstract:

Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

Procedia PDF Downloads 116

Authors: Varda Gil, Ella Shoval, Tussia Mira

Abstract:

Teacher mentoring is a complex practical profession whose unique characteristic is the teacher-mentors' commitment to helping teachers link theory with teaching practice in the process of decision-making and in their reflections on teaching. The aim of this research is to examine the way practicing teacher-mentors participating in a teacher mentoring course made the connection between theory and practice. The researchers analyzed 20 final papers submitted by participants in a course to train teacher mentors. The participants were all veteran high-school teachers. The course comprised 112 in-class hours in addition to mentoring novices in the field. The course covered the following topics: The teacher-mentors' perception of their role; formative and summative evaluation of the novices; tutoring strategies and tools; types of learners; and ways of communicating and dealing with novice teachers' resistance to counseling. The course participants were required to write a 4-5 page reflective summary of their field mentoring practice. In addition, they were required to link theories explicitly learned in the course to their practice in the field. A qualitative analysis of the papers led to the creation of the taxonomy of the link between theory and practice relating to four topics: The kinds of links made between theory and practice, the quality of these links, the links made between private teaching theories and official teaching theory, and the qualities of these links. This taxonomy may prove to be a useful tool in the teacher-mentor training processes.

Keywords: taxonomy, teacher-mentors, theory, practice, teacher-mentor training

Procedia PDF Downloads 328

Authors: U. Borasi, Amit Kr. Jain, Rakesh, Piyush Jain

Abstract:

Data collection, data preparation, model training, model evaluation, and deployment are all processes in a typical machine learning workflow. Training data needs to be gathered and organised. This often entails collecting a sizable dataset and cleaning it to remove or correct any inaccurate or missing information. Preparing the data for use in the machine learning model requires pre-processing it after it has been acquired. This often entails actions like scaling or normalising the data, handling outliers, selecting appropriate features, reducing dimensionality, etc. This pre-processed data is then used to train a model on some machine learning algorithm. After the model has been trained, it needs to be assessed by determining metrics like accuracy, precision, and recall, utilising a test dataset. Every time a new model is built, both data pre-processing and model training—two crucial processes in the Machine learning (ML) workflow—must be carried out. Thus, there are various Machine Learning algorithms that can be employed for every single approach to data pre-processing, generating a large set of combinations to choose from. Example: for every method to handle missing values (dropping records, replacing with mean, etc.), for every scaling technique, and for every combination of features selected, a different algorithm can be used. As a result, in order to get the optimum outcomes, these tasks are frequently repeated in different combinations. This paper suggests a simple architecture for organizing this largely produced “combination set of pre-processing steps and algorithms” into an automated workflow which simplifies the task of carrying out all possibilities.

Keywords: machine learning, automation, AUTOML, architecture, operator pool, configuration, scheduler

Procedia PDF Downloads 33

Authors: Mfaniseni Wiseman Mbatha

Abstract:

The extension officers are regarded as the key role players in the provision of agricultural facilities to farmers across the world. The government of South Africa has shown a commitment to provide extensive support to farmers by the means of disseminating information and other agricultural facilities. This qualitative study on the experiences of extension officers on the provision of agricultural facilities to rural farmers towards improving agricultural practice was conducted in Msinga Local Municipality. The data was collected through the use of semi-structured interviews with extension officers who were sampled using the purposive sampling method. The qualitative data was analysed through the use of content analysis. The critical part of the findings reveals that the availability of arable land for agricultural practice, availability of agricultural schemes and availability of proper functioning community gardens were indicators of the high level of agricultural practice in the Msinga area. Therefore, the extension officers from the municipality department have shown to provide the agricultural budget to support rural farmers. Whereas, the department of agriculture provides well knowledgeable staff to train farmers about the process of farming and how they can address issues of livestock and crop diseases and also adapting to issues of climate change. The rural farmers, however, find it very difficult to learn and put into practice things that were thought by extension officers during training. There is, therefore, a need for recruitment of more extension staff and the involvement of Non-Government Organizations to increase access to extension facilities to the farmers.

Keywords: agricultural facilities, agricultural practice, extension officers, rural farmers

Procedia PDF Downloads 115

Authors: Shinji Sato, M. M. A. Alam, Manabu Takao

Abstract:

In recent, investigation of an unsteady flow inside the resonance tube have become a strongly motivated research field for their potential application as high-frequency actuators. By generating a shock wave inside the resonance tube, a high temperature and pressure can be achieved inside the tube, and this high temperature can also be used to ignite a jet engine. In the present research, a computational fluid dynamics (CFD) analysis was carried out to investigate the flow inside the resonance tube. The density-based solver of rhoCentralFoam in OpenFOAM was used to numerically simulate the flow. The supersonic jet that was driven by a cylindrical nozzle with a nominal exit diameter of φd = 20.3 mm impinged onto the resonance tube. The jet pressure ratio was varied between 2.6 and 7.8. The gap s between the nozzle exit and tube entrance was changed between 1.5d and 3.0d. The diameter and length of the tube were taken as D = 1.25d and L=3.0D, respectively. As a result, when a supersonic jet has impinged onto the resonance tube, a compression wave was found generating inside the tube and propagating towards the tube end wall. This wave train resulted in a rise in the end wall gas temperature and pressure. While, in an outflow phase, the gas near tube enwall was found cooling back isentropically to its initial temperature. Thus, the compression waves repeated a reciprocating motion in the tube like a piston, and a fluctuation in the end wall pressures and temperatures were observed. A significant change was found in the end wall pressures and temperatures with a change of jet flow conditions. In this study, the highest temperature was confirmed at a jet pressure ratio of 4.2 and a gap of s=2.0d

Keywords: compressible flow, OpenFOAM, oscillations, a resonance tube, shockwave

Procedia PDF Downloads 128

Authors: Sinsze Koo, Benjin Luo, Matthew Henry

Abstract:

In order to increase the performance of an automatic transmission, the automatic transmission fluid is required to be warm up to an optimal operating temperature. In a conventional vehicle, cold starts result in friction loss occurring in the gear box and engine. The stop and go nature of city driving dramatically affect the warm-up of engine oil and automatic transmission fluid and delay the time frame needed to reach an optimal operating temperature. This temperature phenomenon impacts both engine and transmission performance but also increases fuel consumption and CO2 emission. The aim of this study is to develop know-how of the thermal behavior in order to identify thermal impacts and functional principles in automatic transmissions. Thermal behavior was studied using models and simulations, developed using GT-Suit, on a one-dimensional thermal and flow transport. A power train of a conventional vehicle was modeled in order to emphasis the thermal phenomena occurring in the various components and how they impact the automatic transmission performance. The simulation demonstrates the thermal model of a transmission fluid cooling system and its component parts in warm-up after a cold start. The result of these analyses will support the future designs of transmission systems and components in an attempt to obtain better fuel efficiency and transmission performance. Therefore, these thermal analyses could possibly identify ways that improve existing thermal management techniques with prioritization on fuel efficiency.

Keywords: thermal management, automatic transmission, hybrid, and systematic approach

Procedia PDF Downloads 354

Authors: Aungkana Kmonpetch

Abstract:

Protection of trafficked victims and partnership among stakeholders are established as core principles in 5P’ strategies in international and national anti-human trafficking policies. In this article, it is of interest to discuss how the role of public-private partnerships in promoting the occupation development for employment in wage will enhance the better protection for victims of trafficking who affirmatively decide they want a criminal justice intervention, using Thailand as a case. Most of the victims who have accepted to be witness in the criminal justice system have lost income during their absence from work. The analysis of Thailand case is based on two methodological approaches: 1) interview with victims of trafficking, protection authorities, service providers, trainers and teachers, social workers, NGOs, police, prosecutors, business owners and enterprises, ILO, UNDP etc.; 2) create collaborative effort through workshops/consultation meetings in participation of all stakeholders – governmental agencies, private organizations, UN and international agencies. The linking of protection and partnership is anchored in international conventions and human trafficking directives. While this is actually framed as a responsive advantage for 5P strategies of anti-human trafficking – prevention, protection, persecution, punishment, and partnership, in reality, there might have more practical requirements of care and support. The article addresses how the partnership between governmental agencies and private organizations provide opportunities for trafficked victims to engage in high-skilled occupational development such as Silk-Sewing and Tailoring. The discussion is also focused how this approach of capacity building of the trainer for trainee, be enable the trafficked victims to cultivate the practices of high-skilled training to engage them into the business of social enterprise with employment in wage. The partnership coordination draws specifically to two aspects: firstly, to formulate appropriate assistance for promotion and protection of human rights of the trafficked victims in response to the 5P’ strategies of anti-human trafficking policy; secondly, to empower them to settle some economic stability for livelihood opportunity in the country of origin on their return and reintegration. Therefore, they can define how they want to move forward to prevent them at risk of vulnerable situations where they might being trafficked again or going on to work in exploitative conditions. It strengthens proper access to protection and assistance, depending on how the incentive of protection for cooperation is perceived to be and how useful the capacity building in occupation development for employment in wage will be implemented practically both in the host country and in the country of origin. This also brings into question how the victim of trafficking are able to access to the trade of market and are supported the employment opportunity according to the concept of decent work as they are constituted as witnesses. We discuss these issues in the area of a broader literature on social protection, economic security, gender, law, and victimhood.

Keywords: employment opportunity, occupation development, protection for victim of trafficking, public-private partnership

Procedia PDF Downloads 202

Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

Abstract:

In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

Procedia PDF Downloads 395

Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

Abstract:

Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

Procedia PDF Downloads 179

Authors: Alina P. Colling, Robert G. Hekkenberg

Abstract:

Recent years have seen extensive technological advancement in truck platooning, as reflected in the literature. Its main benefits are the improvement of traffic stability and the reduction of air drag, resulting in less fuel consumption, in comparison to using individual trucks. Platooning is now being adapted to the waterborne transport sector in the NOVIMAR project through the development of a Vessel Train (VT) concept. The main focus of VT’s, as opposed to the truck platoons, is the decrease in manning on board, ultimately working towards autonomous vessel operations. This crew reduction can prove to be an important selling point in achieving economic competitiveness of the waterborne approach when compared to alternative modes of transport. This paper discusses the expected benefits and drawbacks of the VT concept, in terms of the technical logistic performance and generalized costs. More specifically, VT’s can provide flexibility in destination choices for shippers but also add complexity when performing special manoeuvres in VT formation. In order to quantify the cost and performances, a model is developed and simulations are carried out for various case studies. These compare the application of VT’s in the short sea and inland water transport, with specific sailing regimes and technologies installed on board to allow different levels of autonomy. The results enable the identification of the most important boundary conditions for the successful operation of the waterborne platooning concept. These findings serve as a framework for future business applications of the VT.

Keywords: autonomous vessels, NOVIMAR, vessel trains, waterborne platooning

Procedia PDF Downloads 194

Authors: Claudio Díaz, Mabel Ortiz

Abstract:

An EFL teacher education programme in Chile takes five years to train a future teacher of English. Preservice teachers are prepared to learn an advanced level of English and teach the language from 5th to 12th grade in the Chilean educational system. In the context of their first EFL Methodology course in year four, preservice teachers have to create a five-minute digital story that starts from a critical incident they have experienced as teachers-to-be during their observations or interventions in the schools. A critical incident can be defined as a happening, a specific incident or event either observed by them or involving them. The happening sparks their thinking and may make them subsequently think differently about the particular event. When they create their digital stories, preservice teachers put technology, teaching practice and theory together to narrate a story that is complemented by still images, moving images, text, sound effects and music. The story should be told as a personal narrative, which explains the critical incident. This presentation will focus on the creation process of 50 Chilean preservice teachers’ digital stories highlighting the critical incidents they started their stories. It will also unpack preservice teachers’ beliefs and reflections when approaching their teaching practices in schools. These beliefs will be coded and categorized through content analysis to evidence preservice teachers’ most rooted conceptions about English teaching and learning in Chilean schools. The findings seem to indicate that preservice teachers’ beliefs are strongly mediated by contextual and affective factors.

Keywords: beliefs, digital stories, preservice teachers, practicum

Procedia PDF Downloads 414

Authors: Satish Kumar Peddapelli

Abstract:

This paper presents advances in pulse width modulation techniques which refers to a method of carrying information on train of pulses and the information be encoded in the width of pulses. Pulse Width Modulation is used to control the inverter output voltage. This is done by exercising the control within the inverter itself by adjusting the ON and OFF periods of inverter. By fixing the DC input voltage we get AC output voltage. In variable speed AC motors the AC output voltage from a constant DC voltage is obtained by using inverter. Recent developments in power electronics and semiconductor technology have lead improvements in power electronic systems. Hence, different circuit configurations namely multilevel inverters have become popular and considerable interest by researcher are given on them. A fast Space-Vector Pulse Width Modulation (SVPWM) method for five-level inverter is also discussed. In this method, the space vector diagram of the five-level inverter is decomposed into six space vector diagrams of three-level inverters. In turn, each of these six space vector diagrams of three-level inverter is decomposed into six space vector diagrams of two-level inverters. After decomposition, all the remaining necessary procedures for the three-level SVPWM are done like conventional two-level inverter. The proposed method reduces the algorithm complexity and the execution time. It can be applied to the multilevel inverters above the five-level also. The experimental setup for three-level diode-clamped inverter is developed using TMS320LF2407 DSP controller and the experimental results are analysed.

Keywords: five-level inverter, space vector pulse wide modulation, diode clamped inverter, electrical engineering

Procedia PDF Downloads 365

Authors: Qin Long, Li Xiaoge

Abstract:

The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

Procedia PDF Downloads 71

Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

Abstract:

New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Three research questions and hypotheses were formulated, and tested at a 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following recommendations was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and trainings should be organized to train teachers on the use of this technique and that schools and government should formulate policies and procedures towards responsible use of MCLT.

Keywords: education, communication, learning, mobile collaboration, technology

Procedia PDF Downloads 196

Authors: Chandra Gupt Porwal, Preeti C. Porwal

Abstract:

Transient data reveals much about the machine's condition that steady-state data cannot. New technologies make this information much more available for evaluating the mechanical integrity of a machine train. Recent surveys at various stations indicate that simplicity is preferred over completeness in machine audits throughout the power generation industry. This is most clearly shown by the number of rotating machinery predictive maintenance programs in which only steady-state vibration amplitude is trended while important transient vibration data is not even acquired. Efforts have been made to explain what transient data is, its importance, the types of plots used for its display, and its effective utilization for analysis. In order to demonstrate the value of measuring transient data and its practical application in rotating machinery for resolving complex and persistent issues with turbine generators, the author presents a few case studies that highlight the presence of rotor instabilities due to the shaft moving towards the bearing centre in a 100 MM LMZ unit located in the Northern Capital Region (NCR), heavy misalignment noticed—especially after 2993 rpm—caused by loose coupling bolts, which prevented the machine from being synchronized for more than four months in a 250 MW KWU unit in the Western Region (WR), and heavy preload noticed at Intermediate pressure turbine (IPT) bearing near HP- IP coupling, caused by high points on coupling faces at a 500 MW KWU unit in the Northern region (NR), experienced at Indian power plants.

Keywords: transient data, steady-state-data, intermediate -pressure-turbine, high-points

Procedia PDF Downloads 35

Authors: James Jr. Mashiyane, Risuna Nkolele, Stephanie J. Müller, Gciniwe S. Dlamini, Rebone L. Meraba, Darlington S. Mapiye

Abstract:

When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed.

Keywords: word embeddings, k-mer embedding, dimensionality reduction

Procedia PDF Downloads 109

Authors: Nader Khalafian, Mohsen Ghaderi

Abstract:

Field observations have revealed many examples of structures which were damaged due to ground surface deformation caused by the faulting phenomena. In this paper some efforts were made in order to estimate the length and location of the ground surface where large displacements were created due to the reverse faulting. This research has conducted in two steps; (1) in the first step, a 2D explicit finite element model were developed using ABAQUS software. A subroutine for Mohr-Coulomb failure criterion with strain softening model was developed by the authors in order to properly model the stress strain behavior of the soil in the fault rapture zone. The results of the numerical analysis were verified with the results of available centrifuge experiments. Reasonable coincidence was found between the numerical and experimental data. (2) In the second step, the effects of the fault dip angle (δ), depth of soil layer (H), dilation and friction angle of sand (ψ and φ) and the amount of fault offset (d) on the soil surface displacement and fault rupture path were investigated. An artificial neural network-based model (ANN), as a powerful prediction tool, was developed to generate a general model for predicting faulting characteristics. A properly sized database was created to train and test network. It was found that the length and location of the zone of displaced ground surface can be accurately estimated using the proposed model.

Keywords: reverse faulting, surface deformation, numerical, neural network

Procedia PDF Downloads 405

Authors: Rattana Sangchan, Pattaraporn Thampradit

Abstract:

Teaching students to use a variety of reading methods in developing reading is essential for Thai university students. However, there haven’t been a lot of studies concerned about developing reading methods that are used by Thai students in the industrial education field. Therefore, this study was carried out not only to investigate the developing reading methods of Industrial Education students at King Mongkut’s Institute of Technology Ladkrabang, but also to determine if the developing reading strategies differ among the students’ reading abilities and differ gender: male and female. The research instrument used in collecting the data consisted of fourteen statements which include either metacognitive strategies, cognitive strategies or social / affective strategies. Results of this study revealed that students could develop their reading methods in moderate level (mean=3.13). Furthermore, high reading ability students had different levels of using reading methods to develop their reading from those of mid reading ability students. In addition, high reading ability students could use either metacognitive reading methods or cognitive reading methods to develop their reading much better than mid reading ability students. Interestingly, male students could develop their reading methods in great levels while female students could develop their reading methods only in moderate level. Last but not least, male students could use either metacognitive reading methods or cognitive reading methods to develop their reading much better than female students. Thus, the results of this study could indicate that most students need to apply much more reading strategies to develop their reading. At the same time, suggestions on how to motivate and train their students to apply much more appropriate effective reading strategies to better comprehend their reading were also provided.

Keywords: developing reading methods, industrial education, reading abilities, reading method classification

Procedia PDF Downloads 262

Authors: Masica Jordan Alston, Angela N. Bullock, Angela S. Henderson, Stephanie Strianse, Sade Dunn, Joseph Hackett, Alaysia Black Hackett, Marcus Mason

Abstract:

The Energy Model of Cumulative Grief was created in 2018. The Energy Model of Cumulative Grief utilizes historic models of grief stage theories. The innovative model is additionally unique due to its focus on cultural responsiveness. The Energy Model of Cumulative Grief helps to train practitioners who work with clients dealing with grief and loss. This paper assists in introducing the world to this innovative model and exploring how this model positively impacted a convenience sample of 140 practitioners and individuals experiencing grief and loss. Respondents participated in Webinars provided by the National Grief and Loss Center of America (NGLCA). Participants in this cross-sectional research design study completed one of three Grief and Loss Surveys created by the Grief and Loss Centers of America. Data analysis for this study was conducted via SPSS and Survey Hero to examine survey results for respondents. Results indicate that the Energy Model of Cumulative Grief was an effective resource for participants in addressing grief and loss. The majority of participants found the Webinars to be helpful and a conduit to providing them with higher levels of hope. The findings suggest that using The Energy Model of Cumulative Grief is effective in providing culturally responsive grief and loss resources to practitioners and clients. There are far reaching implications with the use of technology to provide hope to those suffering from grief and loss worldwide through The Energy Model of Cumulative Grief.

Keywords: grief, loss, grief energy, grieving brain

Procedia PDF Downloads 59

Authors: Zasha Romero, Joe Paschall

Abstract:

This poster will detail the importance of physical activity for the Visually Impaired students and how to promote inclusion in fitness through way of social gatherings and jogging. Furthermore, it will demonstrate how a Health & Kinesiology University Club cooperated in the journey of visually impaired students from participating in physical activity to completing their first 10K fun run. Purpose: The poster will detail how a university’s Health & Kinesiology Club developed a program to promote participation in fitness activities for visually impaired individuals. Also, it will detail their journey from participation in physical activity to completing a 10K fun run. Methods: In an effort to promote inclusion of all into physical activity, a university’s Health & Kinesiology Club developed a non-profit program to challenge visually impaired students to train and complete a 10 kilometer fun run in a South Texas town. The idea was to promote physical fitness through way of social interaction. In order to maintain runners interested, Club students developed training plans and strategies to be able to navigate in a race that was attended by over 18,000 runners. The idea was to promote interaction and life-long fitness amongst participants. Implications: This strategy was done in collaboration with different non-profit institutions to create awareness and provide opportunities for physical fitness, social interaction and life-long fitness skills associated with the jogging. The workshop provided collaboration amongst different entities and novel ideas to create opportunities for a typically underserved population.

Keywords: inclusion, participation, management, disability, fitness

Procedia PDF Downloads 369

Authors: Hyang Lim, Sungwon Choi

Abstract:

Many previous studies in area of adolescents' depression have used a longitudinal design. The previous studies have found that the developmental trajectory of them is only one. But it needs to be examined whether the trajectory is applied to all adolescents. Some factors in their home and/or school have an effect on adolescents' depression and more likely to be specific groups. The present study was a longitudinal study aimed to identify the trajectories and to explore the predictors of adolescents' depression. The study used Korean Children and Youth Panel Survey (KCYPS) data. In this study, 2,351 second and third-year of middle school and first of high school students' data was analyzed by using semi-parametric group modeling (SGM). There were 5 trajectory groups for adolescents; low depressed stables, low depressed risers, moderately depressed decreases, moderately depressed stables, severe depressed decreases. The predictors of adolescents' depression were parental abuse, parental neglect, annual family income, parental academic background, friendship at school, and teacher-student relationship at school. All predictors had the significant difference across trajectory group profile for adolescents. The findings of the present study recommend to promote the socioeconomic status and to train social skill for the interpersonal relationship at the home and school. And the results suggest that the proper prevention programs for each group in the middle adolescents that target selected factors may be helpful in reducing the level of depression.

Keywords: adolescent, depression, KCYPS, school life, semi-parametric group-based modeling

Procedia PDF Downloads 425