Search results for: virtual machine
399 Gene Expressions in Left Ventricle Heart Tissue of Rat after 150 Mev Proton Irradiation
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Introduction: In mediastinal radiotherapy and to a lesser extend also in total-body irradiation (TBI) radiation exposure may lead to development of cardiac diseases. Radiation-induced heart disease is dose-dependent and it is characterized by a loss of cardiac function, associated with progressive heart cells degeneration. We aimed to determine the in-vivo radiation effects on fibronectin, ColaA1, ColaA2, galectin and TGFb1 gene expression levels in left ventricle heart tissues of rats after irradiation. Material and method: Four non-treatment adult Wistar rats as control group (group A) were selected. In group B, 4 adult Wistar rats irradiated to 20 Gy single dose of 150 Mev proton beam locally in heart only. In heart plus lung irradiate group (group C) 4 adult rats was irradiated by 50% of lung laterally plus heart radiation that mentioned in before group. At 8 weeks after radiation animals sacrificed and left ventricle heart dropped in liquid nitrogen for RNA extraction by Absolutely RNA® Miniprep Kit (Stratagen, Cat no. 400800). cDNA was synthesized using M-MLV reverse transcriptase (Life Technologies, Cat no. 28025-013). We used Bio-Rad machine (Bio Rad iQ5 Real Time PCR) for QPCR testing by relative standard curve method. Results: We found that gene expression of fibronectin in group C significantly increased compared to control group, but it was not showed significant change in group B compared to group A. The levels of gene expressions of Cola1 and Cola2 in mRNA did not show any significant changes between normal and radiation groups. Changes of expression of galectin target significantly increased only in group C compared to group A. TGFb1 expressions in group C more than group B showed significant enhancement compared to group A. Conclusion: In summary we can say that 20 Gy of proton exposure of heart tissue may lead to detectable damages in heart cells and may distribute function of them as a component of heart tissue structure in molecular level.Keywords: gene expression, heart damage, proton irradiation, radiotherapy
Procedia PDF Downloads 484398 Exploring the Applications of Neural Networks in the Adaptive Learning Environment
Authors: Baladitya Swaika, Rahul Khatry
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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.Keywords: computer adaptive tests, item response theory, machine learning, neural networks
Procedia PDF Downloads 167397 Energy Efficiency Approach to Reduce Costs of Ownership of Air Jet Weaving
Authors: Corrado Grassi, Achim Schröter, Yves Gloy, Thomas Gries
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Air jet weaving is the most productive, but also the most energy consuming weaving method. Increasing energy costs and environmental impact are constantly a challenge for the manufacturers of weaving machines. Current technological developments concern with low energy costs, low environmental impact, high productivity, and constant product quality. The high degree of energy consumption of the method can be ascribed to the high need of compressed air. An energy efficiency method is applied to the air jet weaving technology. Such method identifies and classifies the main relevant energy consumers and processes from the exergy point of view and it leads to the identification of energy efficiency potentials during the weft insertion process. Starting from the design phase, energy efficiency is considered as the central requirement to be satisfied. The initial phase of the method consists of an analysis of the state of the art of the main weft insertion components in order to point out a prioritization of the high demanding energy components and processes. The identified major components are investigated to reduce the high demand of energy of the weft insertion process. During the interaction of the flow field coming from the relay nozzles within the profiled reed, only a minor part of the stream is really accelerating the weft yarn, hence resulting in large energy inefficiency. Different tools such as FEM analysis, CFD simulation models and experimental analysis are used in order to design a more energy efficient design of the involved components in the filling insertion. A different concept for the metal strip of the profiled reed is developed. The developed metal strip allows a reduction of the machine energy consumption. Based on a parametric and aerodynamic study, the designed reed transmits higher values of the flow power to the filling yarn. The innovative reed fulfills both the requirement of raising energy efficiency and the compliance with the weaving constraints.Keywords: air jet weaving, aerodynamic simulation, energy efficiency, experimental validation, weft insertion
Procedia PDF Downloads 186396 Healthcare-SignNet: Advanced Video Classification for Medical Sign Language Recognition Using CNN and RNN Models
Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan
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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network
Procedia PDF Downloads 12395 Image Processing-Based Maize Disease Detection Using Mobile Application
Authors: Nathenal Thomas
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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot
Procedia PDF Downloads 67394 Trip Reduction in Turbo Machinery
Authors: Pranay Mathur, Carlo Michelassi, Simi Karatha, Gilda Pedoto
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Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start has major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these CTQs are measured with 2 metrics, MTBT(Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability. Baker Hughes Trip reduction program structured to reduce these unwanted trip 1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition. 2. Real time alerting system based on analytics available remotely. 3. Remote access to trip logs and alarms from control system to identify the cause of events. 4. Continuous support to field engineers by remotely connecting with subject matter expert. 5. Live tracking of key CTQs 6. Benchmark against fleet 7. Break down to the cause of failure to component level 8. Investigate top contributor, identify design and operational root cause 9. Implement corrective and preventive action 10. Assessing effectiveness of implemented solution using reliability growth models. 11. Develop analytics for predictive maintenance With this approach , Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This Presentation explains these approach while providing successful case studies, in particular where 12nos. of LNG and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBTKeywords: reliability, availability, sustainability, digital infrastructure, weibull, effectiveness, automation, trips, fail start
Procedia PDF Downloads 66393 Understanding the Classification of Rain Microstructure and Estimation of Z-R Relationship using a Micro Rain Radar in Tropical Region
Authors: Tomiwa, Akinyemi Clement
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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.Keywords: remote sensing, precipitation, drop size distribution, micro rain radar
Procedia PDF Downloads 18392 Corporate Digital Responsibility in Construction Engineering-Construction 4.0: Ethical Guidelines for Digitization and Artificial Intelligence
Authors: Weber-Lewerenz Bianca
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Digitization is developing fast and has become a powerful tool for digital planning, construction, and operations. Its transformation bears high potentials for companies, is critical for success, and thus, requires responsible handling. This study provides an assessment of calls made in the sustainable development goals by the United Nations (SDGs), White Papers on AI by international institutions, EU-Commission and German Government requesting for the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of artificial intelligence (AI) in construction engineering from an ethical perspective by generating data via conducting case studies and interviewing experts as part of the qualitative method. This research evaluates critically opportunities and risks revolving around corporate digital responsibility (CDR) in the construction industry. To the author's knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to the digitization and AI, to mitigate digital transformation both in large, medium-sized, and small companies. No study addressed the key research question: Where can CDR be allocated, how shall its adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Now is the right timing for constructive approaches and apply ethics-by-design in order to develop and implement a safe and efficient AI. This represents the first study in construction engineering applying a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation, examine benefits of AI and define ethical principles as the key driver for success, resources-cost-time efficiency, and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. Innovative corporate organizations starting new business models are more likely to succeed than those dominated by conservative, traditional attitudes.Keywords: construction engineering, digitization, digital transformation, artificial intelligence, ethics, corporate digital responsibility, digital innovation
Procedia PDF Downloads 231391 Enhancing Robustness in Federated Learning through Decentralized Oracle Consensus and Adaptive Evaluation
Authors: Peiming Li
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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.Keywords: federated learning system, block chain, decentralized oracles, hidden markov model
Procedia PDF Downloads 52390 Improving Cell Type Identification of Single Cell Data by Iterative Graph-Based Noise Filtering
Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott
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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.Keywords: cancer research, graph theory, machine learning, single cell analysis
Procedia PDF Downloads 100389 Contextual SenSe Model: Word Sense Disambiguation using Sense and Sense Value of Context Surrounding the Target
Authors: Vishal Raj, Noorhan Abbas
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Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)
Procedia PDF Downloads 94388 News Reading Practices: Traditional Media versus New Media
Authors: Nuran Öze
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People always want to be aware of what is happening around them. The nature of man constantly triggers the need for gathering information because of curiosity. The media has emerged to save people the need for information. It is known that the media has changed with the technological developments over time, diversified and, people's information needs are provided in different ways. Today, the Internet has become an integral part of everyday life. The invasion of the Internet into everyday life practices at this level affects every aspect of life. These effects cause people to change their life practices. Technological developments have always influenced of people, the way they reach information. Looking at the history of the media, the breaking point about the dissemination of information is seen as the invention of the machine of the printing press. This adventure that started with written media has now become a multi-dimensional structure. Written, audio, visual media has now changed shape with new technologies. Especially emerging of the internet to everyday life, of course, has effects on media field. 'New media' has appeared which contains most of traditional media features in its'. While in the one hand this transformation enables captures a harmony between traditional and new media, on the other hand, new media and traditional media are rivaling each other. The purpose of this study is to examine the problematic relationship between traditional media and new media through the news reading practices of individuals. This study can be evaluated as a kind of media sociology. To reach this aim, two different field researches will be done besides literature review. The research will be conducted in Northern Cyprus. Northern Cyprus Northern Cyprus is located in the Mediterranean Sea. North Cyprus is a country which is not recognized by any country except Turkey. Despite this, takes its share from all technological developments take place in the world. One of the field researches will consist of the questionnaires to be applied on media readers' news reading practices. This survey will be conducted in a social media environment. The second field survey will be conducted in the form of interviews with general editorials or news directors in traditional media. In the second field survey, in-depth interview method will be applied. As a result of these investigations, supporting sides between the new media and the traditional media and directions which contrast with each other will be revealed. In addition to that, it will try to understand the attitudes and perceptions of readers about the traditional media and the new media in this study.Keywords: new media, news, North Cyprus, traditional media
Procedia PDF Downloads 220387 Smart Meters and In-Home Displays to Encourage Water Conservation through Behavioural Change
Authors: Julia Terlet, Thomas H. Beach, Yacine Rezgui
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Urbanization, population growth, climate change and the current increase in water demand have made the adoption of innovative demand management strategies crucial to the water industry. Water conservation in urban areas has to be improved by encouraging consumers to adopt more sustainable habits and behaviours. This includes informing and educating them about their households’ water consumption and advising them about ways to achieve significant savings on a daily basis. This paper presents a study conducted in the context of the European FP7 WISDOM Project. By integrating innovative Information and Communication Technologies (ICT) frameworks, this project aims at achieving a change in water savings. More specifically, behavioural change will be attempted by implementing smart meters and in-home displays in a trial group of selected households within Cardiff (UK). Using this device, consumers will be able to receive feedback and information about their consumption but will also have the opportunity to compare their consumption to the consumption of other consumers and similar households. Following an initial survey, it appeared necessary to implement these in-home displays in a way that matches consumer's motivations to save water. The results demonstrated the importance of various factors influencing people’s daily water consumption. Both the relevant literature on the subject and the results of our survey therefore led us to include within the in-home device a variety of elements. It first appeared crucial to make consumers aware of the economic aspect of water conservation and especially of the significant financial savings that can be achieved by reducing their household’s water consumption on the long term. Likewise, reminding participants of the impact of their consumption on the environment by making them more aware of water scarcity issues around the world will help increasing their motivation to save water. Additionally, peer pressure and social comparisons with neighbours and other consumers, accentuated by the use of online social networks such as Facebook or Twitter, will likely encourage consumers to reduce their consumption. Participants will also be able to compare their current consumption to their past consumption and to observe the consequences of their efforts to save water through diverse graphs and charts. Finally, including a virtual water game within the display will help the whole household, children and adults, to achieve significant reductions by providing them with simple tips and advice to save water on a daily basis. Moreover, by setting daily and weekly goals for them to reach, the game will expectantly generate cooperation between family members. Members of each household will indeed be encouraged to work together to reduce their water consumption within different rooms of the house, such as the bathroom, the kitchen, or the toilets. Overall, this study will allow us to understand the elements that attract consumers the most and the features that are most commonly used by the participants. In this way, we intend to determine the main factors influencing water consumption in order to identify the measures that will most encourage water conservation in both the long and short term.Keywords: behavioural change, ICT technologies, water consumption, water conservation
Procedia PDF Downloads 329386 Agri-Food Transparency and Traceability: A Marketing Tool to Satisfy Consumer Awareness Needs
Authors: Angelo Corallo, Maria Elena Latino, Marta Menegoli
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The link between man and food plays, in the social and economic system, a central role where cultural and multidisciplinary aspects intertwine: food is not only nutrition, but also communication, culture, politics, environment, science, ethics, fashion. This multi-dimensionality has many implications in the food economy. In recent years, the consumer became more conscious about his food choices, involving a consistent change in consumption models. This change concerns several aspects: awareness of food system issues, employment of socially and environmentally conscious decision-making, food choices based on different characteristics than nutritional ones i.e. origin of food, how it’s produced, and who’s producing it. In this frame the ‘consumption choices’ and the ‘interests of the citizen’ become one part of the others. The figure of the ‘Citizen Consumer’ is born, a responsible and ethically motivated individual to change his lifestyle, achieving the goal of sustainable consumption. Simultaneously the branding, that before was guarantee of the product quality, today is questioned. In order to meet these needs, Agri-Food companies are developing specific product lines that follow two main philosophies: ‘Back to basics’ and ‘Less is more’. However, the issue of ethical behavior does not seem to find an adequate on market offer. Most likely due to a lack of attention on the communication strategy used, very often based on market logic and rarely on ethical one. The label in its classic concept of ‘clean labeling’ can no longer be the only instrument through which to convey product information and its evolution towards a concept of ‘clear label’ is necessary to embrace ethical and transparent concepts in progress the process of democratization of the Food System. The implementation of a voluntary traceability path, relying on the technological models of the Internet of Things or Industry 4.0, would enable the Agri-Food Supply Chain to collect data that, if properly treated, could satisfy the information need of consumers. A change of approach is therefore proposed towards Agri-Food traceability that is no longer intended as a tool to be used to respond to the legislator, but rather as a promotional tool useful to tell the company in a transparent manner and then reach the slice of the market of food citizens. The use of mobile technology can also facilitate this information transfer. However, in order to guarantee maximum efficiency, an appropriate communication model based on the ethical communication principles should be used, which aims to overcome the pipeline communication model, to offer the listener a new way of telling the food product, based on real data collected through processes traceability. The Citizen Consumer is therefore placed at the center of the new model of communication in which he has the opportunity to choose what to know and how. The new label creates a virtual access point capable of telling the product according to different point of views, following the personal interests and offering the possibility to give several content modalities to support different situations and usability.Keywords: agri food traceability, agri-food transparency, clear label, food system, internet of things
Procedia PDF Downloads 152385 An Efficient Motion Recognition System Based on LMA Technique and a Discrete Hidden Markov Model
Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier
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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model
Procedia PDF Downloads 198384 Hand Gesture Detection via EmguCV Canny Pruning
Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae
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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.Keywords: canny pruning, hand recognition, machine learning, skin tracking
Procedia PDF Downloads 174383 Deep Cryogenic Treatment With Subsequent Aging Applied to Martensitic Stainless Steel: Evaluation of Hardness, Tenacity and Microstructure
Authors: Victor Manuel Alcántara Alza
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The way in which the application of the deep cryogenic treatment DCT(-196°C) affects, applied with subsequent aging, was investigated, regarding the mechanical properties of hardness, toughness and microstructure, applied to martensitic stainless steels, with the aim of establishing a different methodology compared to the traditional DCT cryogenic treatment with subsequent tempering. For this experimental study, a muffle furnace was used, first subjecting the specimens to deep cryogenization in a liquid Nitrogen bath/4h, after being previously austenitized at the following temperatures: 1020-1030-1040-1050 (°C) / 1 hour; and then tempered in oil. A first group of cryogenic samples were subjected to subsequent aging at 150°C, with immersion times: 2.5 -5- 10 - 20 - 50 – 100 (h). The next group was subjected to subsequent tempering at temperatures: 480-500-510-520-530-540 (°C)/ 2h. The hardness tests were carried out under standards, using a Universal Durometer, and the readings were made on the HRC scale. The Impact Resistance tests were carried out in a Charpy machine following the ASTM E 23 – 93ª standard. Measurements were taken in joules. Microscopy was performed at the optical level using a 1000X microscope. It was found: For the entire aging interval, the samples austenitized at 1050°C present greater hardness than austenitized at 1040°C, with the maximum peak aged being at 30h. In all cases, the aged samples exceed the hardness of the tempered samples, even in their minimum values. In post-tempered samples, the tempering temperature hardly have effect on the impact strength of material. In the Cryogenic Treatment: DCT + subsequent aging, the maximum hardness value (58.7 HRC) is linked to an impact toughness value (54J) obtained with aging time of 39h, which is considered an optimal condition. The higher hardness of steel after the DCT treatment is attributed to the transformation of retained austenite into martensite. The microstructure is composed mainly of lath martensite; and the original grain size of the austenite can be appreciated. The choice of the combination: Hardness-toughness, is subject to the required service conditions of steel.Keywords: deep cryogenic treatment; aged precipitation; martensitic steels;, mechanical properties; martensitic steels, hardness, carbides precipitaion
Procedia PDF Downloads 65382 Performance Evaluation of Production Schedules Based on Process Mining
Authors: Kwan Hee Han
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External environment of enterprise is rapidly changing majorly by global competition, cost reduction pressures, and new technology. In these situations, production scheduling function plays a critical role to meet customer requirements and to attain the goal of operational efficiency. It deals with short-term decision making in the production process of the whole supply chain. The major task of production scheduling is to seek a balance between customer orders and limited resources. In manufacturing companies, this task is so difficult because it should efficiently utilize resource capacity under the careful consideration of many interacting constraints. At present, many computerized software solutions have been utilized in many enterprises to generate a realistic production schedule to overcome the complexity of schedule generation. However, most production scheduling systems do not provide sufficient information about the validity of the generated schedule except limited statistics. Process mining only recently emerged as a sub-discipline of both data mining and business process management. Process mining techniques enable the useful analysis of a wide variety of processes such as process discovery, conformance checking, and bottleneck analysis. In this study, the performance of generated production schedule is evaluated by mining event log data of production scheduling software system by using the process mining techniques since every software system generates event logs for the further use such as security investigation, auditing and error bugging. An application of process mining approach is proposed for the validation of the goodness of production schedule generated by scheduling software systems in this study. By using process mining techniques, major evaluation criteria such as utilization of workstation, existence of bottleneck workstations, critical process route patterns, and work load balance of each machine over time are measured, and finally, the goodness of production schedule is evaluated. By using the proposed process mining approach for evaluating the performance of generated production schedule, the quality of production schedule of manufacturing enterprises can be improved.Keywords: data mining, event log, process mining, production scheduling
Procedia PDF Downloads 271381 Interfacial Adhesion and Properties Improvement of Polyethylene/Thermoplastic Starch Blend Compatibilized by Stearic Acid-Grafted-Starch
Authors: Nattaporn Khanoonkon, Rangrong Yoksan, Amod A. Ogale
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Polyethylene (PE) is one of the most petroleum-based thermoplastic materials used in many applications including packaging due to its cheap, light-weight, chemically inert and capable to be converted into various shapes and sizes of products. Although PE is a commercially potential material, its non-biodegradability caused environmental problems. At present, bio-based polymers become more interesting owing to its bio-degradability, non-toxicity, and renewability as well as being eco-friendly. Thermoplastic starch (TPS) is a bio-based and biodegradable plastic produced from the plasticization of starch under applying heat and shear force. In many researches, TPS was blended with petroleum-based polymers including PE in order to reduce the cost and the use of those polymers. However, the phase separation between hydrophobic PE and hydrophilic TPS limited the amount of TPS incorporated. The immiscibility of two different polarity polymers can be diminished by adding compatibilizer. PE-based compatibilizers, e.g. polyethylene-grafted-maleic anhydride, polyethylene-co-vinyl alcohol, etc. have been applied for the PE/TPS blend system in order to improve their miscibility. Until now, there is no report about the utilization of starch-based compatibilizer for PE/TPS blend system. The aims of the present research were therefore to synthesize a new starch-based compatibilizer, i.e. stearic acid-grafted starch (SA-g-starch) and to study the effect of SA-g-starch on chemical interaction, morphological properties, tensile properties and water vapor as well as oxygen barrier properties of the PE/TPS blend films. PE/TPS blends without and with incorporating SA-g-starch with a content of 1, 3 and 5 part(s) per hundred parts of starch (phr) were prepared using a twin screw extruder and then blown into films using a film blowing machine. Incorporating 1 phr and 3 phr of SA-g-starch could improve miscibility of the two polymers as confirmed from the reduction of TPS phase size and the good dispersion of TPS phase in PE matrix. In addition, the blend containing SA-g-starch with contents of 1 phr and 3 phr exhibited higher tensile strength and extensibility, as well as lower water vapor and oxygen permeabilities than the naked blend. The above results suggested that SA-g-starch could be potentially applied as a compatibilizer for the PE/TPS blend system.Keywords: blend, compatibilizer, polyethylene, thermoplastic starch
Procedia PDF Downloads 434380 Austempered Compacted Graphite Irons: Influence of Austempering Temperature on Microstructure and Microscratch Behavior
Authors: Rohollah Ghasemi, Arvin Ghorbani
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This study investigates the effect of austempering temperature on microstructure and scratch behavior of the austempered heat-treated compacted graphite irons. The as-cast was used as base material for heat treatment practices. The samples were extracted from as-cast ferritic CGI pieces and were heat treated under austenitising temperature of 900°C for 60 minutes which followed by quenching in salt-bath at different austempering temperatures of 275°C, 325°C and 375°C. For all heat treatments, an austempering holding time of 30 minutes was selected for this study. Light optical microscope (LOM) and scanning electron microscope (SEM) and electron back scattered diffraction (EBSD) analysis confirmed the ausferritic matrix formed in all heat-treated samples. Microscratches were performed under the load of 200, 600 and 1000 mN using a sphero-conical diamond indenter with a tip radius of 50 μm and induced cone angle 90° at a speed of 10 μm/s at room temperature ~25°C. An instrumented nanoindentation machine was used for performing nanoindentation hardness measurement and microscratch testing. Hardness measurements and scratch resistance showed a significant increase in Brinell, Vickers, and nanoindentation hardness values as well as microscratch resistance of the heat-treated samples compared to the as-cast ferritic sample. The increase in hardness and improvement in microscratch resistance are associated with the formation of the ausferrite matrix consisted of carbon-saturated retained austenite and acicular ferrite in austempered matrix. The maximum hardness was observed for samples austempered at 275°C which resulted in the formation of very fine acicular ferrite. In addition, nanohardness values showed a quite significant variation in the matrix due to the presence of acicular ferrite and carbon-saturated retained austenite. It was also observed that the increase of austempering temperature resulted in increase of volume of the carbon-saturated retained austenite and decrease of hardness values.Keywords: austempered CGI, austempering, scratch testing, scratch plastic deformation, scratch hardness
Procedia PDF Downloads 125379 A Dynamic Curriculum as a Platform for Continuous Competence Development
Authors: Niina Jallinoja, Anu Moisio
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Focus on adult learning is vital to overcome economic challenges as well as to respond to the demand for new competencies and sustained productivity in the digitalized world economy. Employees of all ages must be able to carry on continuous professional development to remain competitive in the labor market. According to EU policies, countries should offer more flexible opportunities for adult learners who study online and in so-called ‘second chance’ qualification programmes. Traditionally, adult education in Finland has comprised of not only liberal adult education but also the government funding to study for Bachelor, Master's, and Ph.D. degrees in Finnish Universities and Universities of Applied Sciences (UAS). From the beginning of 2021, public funding is allocated not only to degrees but also to courses to achieve new competencies for adult learners in Finland. Consequently, there will be degree students (often younger of age) and adult learners studying in the same evening, online and blended courses. The question is thus: How are combined studies meeting the different needs of degree students and adult learners? Haaga-Helia University of Applied Sciences (UAS), located in the metropolitan area of Finland, is taking up the challenge of continuous learning for adult learners. Haaga-Helia has been reforming the bachelor level education and respective shorter courses from 2019 in the biggest project in its history. By the end of 2023, Haaga-Helia will have a flexible, modular curriculum for the bachelor's degrees of hospitality management, business administration, business information technology, journalism and sports management. Building on the shared key competencies, degree students will have the possibility to build individual study paths more flexibly, thanks to the new modular structure of the curriculum. They will be able to choose courses across all degrees, and thus, build their own unique competence combinations. All modules can also be offered as separate courses or learning paths to non-degree students, both publicly funded and as commercial services for employers. Consequently, there will be shared course implementations for degree studies and adult learners with various competence requirements. The newly designed courses are piloted in parallel of the designing of the curriculum in Haaga-Helia during 2020 and 2021. Semi-structured online surveys are composed among the participants for the key competence courses. The focus of the research is to understand how students in the bachelor programme and adult learners from Open UAE perceive the learning experience in such a diverse learning group. A comparison is also executed between learning methods of in-site teaching, online implementation, blended learning and virtual self-learning courses to understand how the pedagogy is meeting the learning objectives of these two different groups. The new flexible curricula and the study modules are to be designed to fill the most important competence gaps that exist in the Finnish labor markets. The new curriculum will be dynamic and constantly evolving over time according to the future competence needs in the labor market. This type of approach requires constant dialogue between Haaga-Helia and workplaces during and after designing of the shared curriculum.Keywords: ccompetence development, continuous learning, curriculum, higher education
Procedia PDF Downloads 119378 Discerning Divergent Nodes in Social Networks
Authors: Mehran Asadi, Afrand Agah
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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.Keywords: online social networks, data mining, social cloud computing, interaction and collaboration
Procedia PDF Downloads 144377 Determination of Community Based Reference Interval of Aspartate Aminotransferase to Platelet Ratio Index (APRI) among Healthy Populations in Mekelle City Tigray, Northern Ethiopia
Authors: Getachew Belay Kassahun
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Background: Aspartate aminotransferase to Platelet Ratio Index (APRI) currently becomes a biomarker for screening liver fibrosis since liver biopsy procedure is invasive and variation in pathological interpretation. Clinical Laboratory Standard Institute recommends establishing age, sex and environment specific reference interval for biomarkers in a homogenous population. The current study was aimed to derive community based reference interval of APRI aged between 12 and 60 years old in Mekelle city Tigrai, Northern Ethiopia. Method: Six hundred eighty eight study participants were collected from three districts in Mekelle city. The 3 districts were selected through random sampling technique and sample size to kebelles (small administration) were distributed proportional to household number in each district. Lottery method was used at household level if more than 2 study participants to each age partition were found. A community based cross sectional in a total of 534 study participants, 264 male and 270 females, were included in the final laboratory and data analysis but around 154 study participants were excluded through exclusion criteria. Aspartate aminotransferase was analyzed through Biosystem chemistry analyzer and Sysmix machine was used to analyze platelet. Man Whitney U test non parametric stastical tool was used to appreciate stastical difference among gender after excluding the outliers through Box and Whisker. Result: The study appreciated stastical difference among gender for APRI reference interval. The combined, male and female reference interval in the current study was 0.098-0.390, 0.133-0.428 and 0.090-0.319 respectively. The upper and lower reference interval of males was higher than females in all age partition and there was no stastical difference (p-value (<0.05)) between age partition. Conclusion: The current study showed using sex specific reference interval is significant to APRI biomarker in clinical practice for result interpretation.Keywords: reference interval, aspartate aminotransferase to platelet ratio Index, Ethiopia, tigray
Procedia PDF Downloads 98376 Machine That Provides Mineral Fertilizer Equal to the Soil on the Slopes
Authors: Huseyn Nuraddin Qurbanov
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The reliable food supply of the population of the republic is one of the main directions of the state's economic policy. Grain growing, which is the basis of agriculture, is important in this area. In the cultivation of cereals on the slopes, the application of equal amounts of mineral fertilizers the under the soil before sowing is a very important technological process. The low level of technical equipment in this area prevents producers from providing the country with the necessary quality cereals. Experience in the operation of modern technical means has shown that, at present, there is a need to provide an equal amount of fertilizer on the slopes to under the soil, fully meeting the agro-technical requirements. No fundamental changes have been made to the industrial machines that fertilize the under the soil, and unequal application of fertilizers under the soil on the slopes has been applied. This technological process leads to the destruction of new seedlings and reduced productivity due to intolerance to frost during the winter for the plant planted in the fall. In special climatic conditions, there is an optimal fertilization rate for each agricultural product. The application of fertilizers to the soil is one of the conditions that increase their efficiency in the field. As can be seen, the development of a new technical proposal for fertilizing and plowing the slopes in equal amounts on the slopes, improving the technological and design parameters, and taking into account the physical and mechanical properties of fertilizers is very important. Taking into account the above-mentioned issues, a combined plough was developed in our laboratory. Combined plough carries out pre-sowing technological operation in the cultivation of cereals, providing a smooth equal amount of mineral fertilizers under the soil on the slopes. Mathematical models of a smooth spreader that evenly distributes fertilizers in the field have been developed. Thus, diagrams and graphs obtained without distribution on the 8 partitions of the smooth spreader are constructed under the inclined angles of the slopes. Percentage and productivity of equal distribution in the field were noted by practical and theoretical analysis.Keywords: combined plough, mineral fertilizer, equal sowing, fertilizer norm, grain-crops, sowing fertilizer
Procedia PDF Downloads 128375 AI/ML Atmospheric Parameters Retrieval Using the “Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN)”
Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen
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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval
Procedia PDF Downloads 160374 Analysis of Residents’ Travel Characteristics and Policy Improving Strategies
Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong
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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation
Procedia PDF Downloads 128373 Study on the Prediction of Serviceability of Garments Based on the Seam Efficiency and Selection of the Right Seam to Ensure Better Serviceability of Garments
Authors: Md Azizul Islam
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Seam is the line of joining two separate fabric layers for functional or aesthetic purposes. Different kinds of seams are used for assembling the different areas or parts of the garment to increase serviceability. To empirically support the importance of seam efficiency on serviceability of garments, this study is focused on choosing the right type of seams for particular sewing parts of the garments based on the seam efficiency to ensure better serviceability. Seam efficiency is the ratio of seam strength and fabric strength. Single jersey knitted finished fabrics of four different GSMs (gram per square meter) were used to make the test garments T-shirt. Three distinct types of the seam: superimposed, lapped and flat seam was applied to the side seams of T-shirt and sewn by lockstitch (stitch class- 301) in a flat-bed plain sewing machine (maximum sewing speed: 5000 rpm) to make (3x4) 12 T-shirts. For experimental purposes, needle thread count (50/3 Ne), bobbin thread count (50/2 Ne) and the stitch density (stitch per inch: 8-9), Needle size (16 in singer system), stitch length (31 cm), and seam allowance (2.5cm) were kept same for all specimens. The grab test (ASTM D5034-08) was done in the Universal tensile tester to measure the seam strength and fabric strength. The produced T-shirts were given to 12 soccer players who wore the shirts for 20 soccer matches (each match of 90 minutes duration). Serviceability of the shirt were measured by visual inspection of a 5 points scale based on the seam conditions. The study found that T-shirts produced with lapped seam show better serviceability and T-shirts made of flat seams perform the lowest score in serviceability score. From the calculated seam efficiency (seam strength/ fabric strength), it was obvious that the performance (in terms of strength) of the lapped and bound seam is higher than that of the superimposed seam and the performance of superimposed seam is far better than that of the flat seam. So it can be predicted that to get a garment of high serviceability, lapped seams could be used instead of superimposed or other types of the seam. In addition, less stressed garments can be assembled by others seems like superimposed seams or flat seams.Keywords: seam, seam efficiency, serviceability, T-shirt
Procedia PDF Downloads 191372 Fault-Tolerant Control Study and Classification: Case Study of a Hydraulic-Press Model Simulated in Real-Time
Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla
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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time
Procedia PDF Downloads 165371 Modern Technology-Based Methods in Neurorehabilitation for Social Competence Deficit in Children with Acquired Brain Injury
Authors: M. Saard, A. Kolk, K. Sepp, L. Pertens, L. Reinart, C. Kööp
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Introduction: Social competence is often impaired in children with acquired brain injury (ABI), but evidence-based rehabilitation for social skills has remained undeveloped. Modern technology-based methods create effective and safe learning environments for pediatric social skills remediation. The aim of the study was to implement our structured model of neuro rehab for socio-cognitive deficit using multitouch-multiuser tabletop (MMT) computer-based platforms and virtual reality (VR) technology. Methods: 40 children aged 8-13 years (yrs) have participated in the pilot study: 30 with ABI -epilepsy, traumatic brain injury and/or tic disorder- and 10 healthy age-matched controls. From the patients, 12 have completed the training (M = 11.10 yrs, SD = 1.543) and 20 are still in training or in the waiting-list group (M = 10.69 yrs, SD = 1.704). All children performed the first individual and paired assessments. For patients, second evaluations were performed after the intervention period. Two interactive applications were implemented into rehabilitation design: Snowflake software on MMT tabletop and NoProblem on DiamondTouch Table (DTT), which allowed paired training (2 children at once). Also, in individual training sessions, HTC Vive VR device was used with VR metaphors of difficult social situations to treat social anxiety and train social skills. Results: At baseline (B) evaluations, patients had higher deficits in executive functions on the BRIEF parents’ questionnaire (M = 117, SD = 23.594) compared to healthy controls (M = 22, SD = 18.385). The most impaired components of social competence were emotion recognition, Theory of Mind skills (ToM), cooperation, verbal/non-verbal communication, and pragmatics (Friendship Observation Scale scores only 25-50% out of 100% for patients). In Sentence Completion Task and Spence Anxiety Scale, the patients reported a lack of friends, behavioral problems, bullying in school, and social anxiety. Outcome evaluations: Snowflake on MMT improved executive and cooperation skills and DTT developed communication skills, metacognitive skills, and coping. VR, video modelling and role-plays improved social attention, emotional attitude, gestural behaviors, and decreased social anxiety. NEPSY-II showed improvement in Affect Recognition [B = 7, SD = 5.01 vs outcome (O) = 10, SD = 5.85], Verbal ToM (B = 8, SD = 3.06 vs O = 10, SD = 4.08), Contextual ToM (B = 8, SD = 3.15 vs O = 11, SD = 2.87). ToM Stories test showed an improved understanding of Intentional Lying (B = 7, SD = 2.20 vs O = 10, SD = 0.50), and Sarcasm (B=6, SD = 2.20 vs O = 7, SD = 2.50). Conclusion: Neurorehabilitation based on the Structured Model of Neurorehab for Socio-Cognitive Deficit in children with ABI were effective in social skills remediation. The model helps to understand theoretical connections between components of social competence and modern interactive computerized platforms. We encourage therapists to implement these next-generation devices into the rehabilitation process as MMT and VR interfaces are motivating for children, thus ensuring good compliance. Improving children’s social skills is important for their and their families’ quality of life and social capital.Keywords: acquired brain injury, children, social skills deficit, technology-based neurorehabilitation
Procedia PDF Downloads 112370 Improving the Efficiency of a High Pressure Turbine by Using Non-Axisymmetric Endwall: A Comparison of Two Optimization Algorithms
Authors: Abdul Rehman, Bo Liu
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Axial flow turbines are commonly designed with high loads that generate strong secondary flows and result in high secondary losses. These losses contribute to almost 30% to 50% of the total losses. Non-axisymmetric endwall profiling is one of the passive control technique to reduce the secondary flow loss. In this paper, the non-axisymmetric endwall profile construction and optimization for the stator endwalls are presented to improve the efficiency of a high pressure turbine. The commercial code NUMECA Fine/ Design3D coupled with Fine/Turbo was used for the numerical investigation, design of experiments and the optimization. All the flow simulations were conducted by using steady RANS and Spalart-Allmaras as a turbulence model. The non-axisymmetric endwalls of stator hub and shroud were created by using the perturbation law based on Bezier Curves. Each cut having multiple control points was supposed to be created along the virtual streamlines in the blade channel. For the design of experiments, each sample was arbitrarily generated based on values automatically chosen for the control points defined during parameterization. The Optimization was achieved by using two algorithms i.e. the stochastic algorithm and gradient-based algorithm. For the stochastic algorithm, a genetic algorithm based on the artificial neural network was used as an optimization method in order to achieve the global optimum. The evaluation of the successive design iterations was performed using artificial neural network prior to the flow solver. For the second case, the conjugate gradient algorithm with a three dimensional CFD flow solver was used to systematically vary a free-form parameterization of the endwall. This method is efficient and less time to consume as it requires derivative information of the objective function. The objective function was to maximize the isentropic efficiency of the turbine by keeping the mass flow rate as constant. The performance was quantified by using a multi-objective function. Other than these two classifications of the optimization methods, there were four optimizations cases i.e. the hub only, the shroud only, and the combination of hub and shroud. For the fourth case, the shroud endwall was optimized by using the optimized hub endwall geometry. The hub optimization resulted in an increase in the efficiency due to more homogenous inlet conditions for the rotor. The adverse pressure gradient was reduced but the total pressure loss in the vicinity of the hub was increased. The shroud optimization resulted in an increase in efficiency, total pressure loss and entropy were reduced. The combination of hub and shroud did not show overwhelming results which were achieved for the individual cases of the hub and the shroud. This may be caused by fact that there were too many control variables. The fourth case of optimization showed the best result because optimized hub was used as an initial geometry to optimize the shroud. The efficiency was increased more than the individual cases of optimization with a mass flow rate equal to the baseline design of the turbine. The results of artificial neural network and conjugate gradient method were compared.Keywords: artificial neural network, axial turbine, conjugate gradient method, non-axisymmetric endwall, optimization
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