Search results for: graph convolutional networks

Authors: Malek Miladi, Miguel Vegara, Maria Perez-Infantes, Khaled Mohamed Ramadan, Antonio Ruiz-Canales, Damaris Nunez-Gomez

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Coffee is the second consumed commodity worldwide, yet it also generates colossal waste. Proper management of coffee waste is proposed by converting them into products with higher added value to achieve sustainability of the economic and ecological footprint and protect the environment. Based on this, a study looking at the recovery of coffee waste is becoming more relevant in recent decades. Spent coffee grounds (SCG's) resulted from brewing coffee represents the major waste produced among all coffee industry. The fact that SCGs has no economic value be abundant in nature and industry, do not compete with agriculture and especially its high oil content (between 7-15% from its total dry matter weight depending on the coffee varieties, Arabica or Robusta), encourages its use as a sustainable feedstock for bio-oil production. The bio-oil extraction is a crucial step towards biodiesel production by the transesterification process. However, conventional methods used for oil extraction are not recommended due to their high consumption of energy, time, and generation of toxic volatile organic solvents. Thus, finding a sustainable, economical, and efficient extraction technique is crucial to scale up the process and to ensure more environment-friendly production. Under this perspective, the aim of this work was the statistical study to know an efficient strategy for oil extraction by n-hexane using indirect sonication. The coffee waste mixed Arabica and Robusta, which was used in this work. The temperature effect, sonication time, and solvent-to-solid ratio on the oil yield were statistically investigated as dependent variables by Central Composite Rotatable Design (CCRD) 23. The results were analyzed using STATISTICA 7 StatSoft software. The CCRD showed the significance of all the variables tested (P < 0.05) on the process output. The validation of the model by analysis of variance (ANOVA) showed good adjustment for the results obtained for a 95% confidence interval, and also, the predicted values graph vs. experimental values confirmed the satisfactory correlation between the model results. Besides, the identification of the optimum experimental conditions was based on the study of the surface response graphs (2-D and 3-D) and the critical statistical values. Based on the CCDR results, 29 ºC, 56.6 min, and solvent-to-solid ratio 16 were the better experimental conditions defined statistically for coffee waste oil extraction using n-hexane as solvent. In these conditions, the oil yield was >9% in all cases. The results confirmed the efficiency of using an ultrasound bath in extracting oil as a more economical, green, and efficient way when compared to the Soxhlet method.

Keywords: coffee waste, optimization, oil yield, statistical planning

Procedia PDF Downloads 101

Authors: B. Farahani, R. Fadil, A. Aboonabi, B. Hoffmann, J. Loscheider, K. Tavakolian, S. Arzanpour

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Wheelchair users spend long hours in a sitting position, and selecting the right cushion is highly critical in preventing pressure ulcers in that demographic. Pressure mapping systems (PMS) are typically used in clinical settings by therapists to identify the sitting profile and pressure points in the sitting area to select the cushion that fits the best for the users. A PMS is a flexible mat composed of arrays of distributed networks of flexible sensors. The output of the PMS systems is a color-coded image that shows the intensity of the pressure concentration. Therapists use the PMS images to compare different cushions fit for each user. This process is highly subjective and requires good visual memory for the best outcome. This paper aims to develop an image processing technique to analyze the images of PMS and provide an objective measure to assess the cushions based on their pressure distribution mappings. In this paper, we first reviewed the skeletal anatomy of the human sitting area and its relation to the PMS image. This knowledge is then used to identify the important features that must be considered in image processing. We then developed an algorithm based on those features to analyze the images and rank them according to their fit to the users' needs.

Keywords: dynamic cushion, image processing, pressure mapping system, wheelchair

Procedia PDF Downloads 152

Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

Procedia PDF Downloads 97

Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

Procedia PDF Downloads 359

Authors: Nicanor Garcia Alvarez, Belarmino Adenso-Diaz, Laura Calzada Infante

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In recent years, researchers have shown an interest in modelling maritime traffic as a complex network. In this paper, we propose a bipartite weighted network to model maritime traffic and detect port maritime communities. The bipartite weighted network considers two different types of nodes. The first one represents Spanish ports, while the second one represents the countries with which there is major import/export activity. The flow among both types of nodes is modeled by weighting the volume of product transported. To illustrate the model, the data is segmented by each type of traffic. This will allow fine tuning and the creation of communities for each type of traffic and therefore finding similar ports for a specific type of traffic, which will provide decision-makers with tools to search for alliances or identify their competitors. The traffic with the greatest impact on the Spanish gross domestic product is selected, and the evolution of the communities formed by the most important ports and their differences between 2019 and 2009 will be analyzed. Finally, the set of communities formed by the ports of the Spanish port system will be inspected to determine global similarities between them, analyzing the sum of the membership of the different ports in communities formed for each type of traffic in particular.

Keywords: bipartite networks, competition, infomap, maritime traffic, port communities

Procedia PDF Downloads 135

Authors: Kruti Dave

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Social media marketing is an essential component of 21st-century business. Social media platforms enable small and medium-sized businesses to enhance brand recognition, generate leads and sales. However, the research on social media marketing is still fragmented and focuses on specific topics, such as effective communication techniques. Since the various ways in which social media impacts individuals and companies alike, the authors of this article focus on the origin, impacts, and current state of Social Media, emphasizing their significance as customer empowerment agents. It illustrates their potential and current responsibilities as part of the corporate business strategy and also suggests several methods to engage them as marketing tools. The focus of social media marketing ranges from defenders to explorers, the culture of Social media marketing encompasses the poles of conservatism and modernity, social media marketing frameworks lie between hierarchies and networks, and its management goes from autocracy to anarchy. This research proposes an integrative framework for small and medium-sized businesses through social media, and the influence of the same will be measured. This strategy will help industry experts to understand this new era. We propose an axiom: Social Media is always a function of marketing as a revenue generator.

Keywords: social media, marketing strategy, media marketing, brand awareness, customer engagement, revenue generator, brand recognition

Procedia PDF Downloads 172

Authors: Amir Andria Gad Shehata

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

Procedia PDF Downloads 37

Authors: C. C. Liang

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MSN used to be the most popular application for communicating among social networks, but Facebook chat is now the most popular. Facebook and MSN have similar characteristics, including usefulness, ease-of-use, and a similar function, which is the exchanging of information with friends. Facebook outperforms MSN in both of these areas. However, the adoption of Facebook and abandonment of MSN have occurred for other reasons. Functions can be improved, but users’ willingness to use does not just depend on functionality. Flow status has been established to be crucial to users’ adoption of cyber applications and to affects users’ adoption of software applications. If users experience flow in using software application, they will enjoy using it frequently, and even change their preferred application from an old to this new one. However, no investigation has examined choice behavior related to switching from Facebook to MSN based on a consideration of flow experiences and functions. This investigation discusses the flow experiences and functions of social-networking applications. Flow experience is found to affect perceived ease of use and perceived usefulness; perceived ease of use influences information ex-change with friends, and perceived usefulness; information exchange influences perceived usefulness, but information exchange has no effect on flow experience.

Keywords: consumer behavior, social media, technology acceptance model, flow experience

Procedia PDF Downloads 339

Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

Procedia PDF Downloads 60

Authors: Harshan Baskar, Anirudh S. Chakravarthy, Prateek Garg, Divyam Goel, Abhijith S. Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V. Sushant, Bijay Kumar Rout

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In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing – our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a new benchmark dataset called Reside-β Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a new network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task, particularly for autonomous navigation applications, and we hope that our work will open up new frontiers in research. Our dataset and code will be made publicly available upon acceptance of our paper.

Keywords: dehazing, image enhancement, nighttime, computer vision

Procedia PDF Downloads 130

Authors: Mengmeng Liu, J. David Frost

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Interstate highway system within the Atlanta Perimeter plays an important role in residents’ daily life. The serious influence of Atlanta I-85 Collapses implies that transportation system in the region lacks a cohesive and comprehensive transportation plan. Therefore, disaster resilience analysis of the transportation system is necessary. Resilience is the system’s capability to persist or to maintain transportation services when exposed to changes or shocks. This paper analyzed the resilience of the whole transportation system within the Perimeter and see how removing interstates within the Perimeter will affect the resilience of the transportation system. The data used in the paper are Atlanta transportation networks and LEHD Origin-Destination Employment Statistics data. First, we calculate the traffic flow on each road section based on LEHD data assuming each trip travel along the shortest travel time paths. Second, we calculate the measure of resilience, which is flow-based connectivity and centrality of the transportation network, and see how they will change if we remove each section of interstates from the current transportation system. Finally, we get the resilience function curve of the interstates and identify the most resilient interstates section. The resilience analysis results show that the framework of calculation resilience is effective and can provide some useful information for the transportation planning and sustainability analysis of the transportation infrastructures.

Keywords: connectivity, interstate highway system, network analysis, resilience analysis

Procedia PDF Downloads 240

Authors: Jorge R. Santos, Pedro Sebastiao

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In recent years there has been a great growth in the use of drones, being used in several areas such as security, agriculture, or research. The existence of some systems that allow the remote control of drones is a reality; however, these systems are quite simple and directed to specific functionality. This paper proposes the development of a web platform made in Vue.js and Node.js to control, manage, and monitor drones in real time. Using a microservice architecture, the proposed project will be able to integrate algorithms that allow the optimization of processes. Communication with remote devices is suggested via HTTP through 3G, 4G, and 5G networks and can be done in real time or by scheduling routes. This paper addresses the case of forest fires as one of the services that could be included in a system similar to the one presented. The results obtained with the elaboration of this project were a success. The communication between the web platform and drones allowed its remote control and monitoring. The incorporation of the fire detection algorithm in the platform proved possible a real time analysis of the images captured by the drone without human intervention. The proposed system has proved to be an asset to the use of drones in fire detection. The architecture of the application developed allows other algorithms to be implemented, obtaining a more complex application with clear expansion.

Keywords: drone control, microservices, node.js, unmanned aerial vehicles, vue.js

Procedia PDF Downloads 118

Authors: Anyia Nduka, Aslan Bin Amad Senin

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Taking a behavioral perspective of Organization Culture, Strategic Leadership, and performance (OC, SLP). We examine the role of Strategic Leadership as key vicious mechanism linking OC,SLP to the organizational capacities. Given the increasing degree of dependence of modern businesses on the use and scientific discovery of relevant data, research efforts around the entire globe have been accelerated. In today's corporate world, Strategic Leadership is still the most sustainable option of performance and competitive advantage. This is why it is critical to gain a deep understanding of research area and to strengthen new collaborative networks in efforts to support research transition towards these integrative efforts. This bibliometric analysis is aimed to examine global trends in OC,SLP research based on publication output, author co-authorships, and co-occurrences of author keywords among authors and affiliated countries. 2829 journal articles were retrieved from the Scopus database Between 1974 and 2021. From the research findings, there is a significant increase in number of publications with strong global collaboration (e.g., USA & UK). We also discovered that while most countries/territories without affiliations were centered in developing countries, the outstanding performance of Asian countries and the volume of their collaborations should be emulated.

Keywords: organizational culture, strategic leadership, organizational resilience, performance

Procedia PDF Downloads 59

Authors: Alim Asamatdinov

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Superabsorbent polymer hydrogels can swell to absorb huge volumes of water or aqueous solutions. This property has led to many practical applications of these new materials, particularly in agriculture for improving the water retention of soils and the water supply of plants. This article reviews the methods of polymeric hydrogels, measurements and treatments of their properties, as well as their effects on soil and on plant growth. The thermodynamic approach used to describe the swelling behaviour of polymer networks proves to be quite helpful in modelling the hydrogel efficiency of water-absorbing additives. The paper presents the results of a study of the physical and chemical properties of hydrogels based on of the production of "Nitron" (Polyacrylonitrile) wastes fibre and salts of the 3-rd transition metals and formalin. The developed hydrogels HG-Al, HG-Cr and HG-formalin have been tested for water holding the capacity of sand. Such a conclusion was also confirmed by data from the method of determining the wilting point by vegetative thumbnails. In the entering process using a dose of 0.1% of the swelling polymeric hydrogel in sand with a culture of barley the difference between the wilting point in comparison with the control was negligible. This indicates that the moisture which was contained in the hydrogel is involved in moisture availability for plant growth, to the same extent as that in the capillaries.

Keywords: hydrogel, chemical, polymer, sandy, colloid

Procedia PDF Downloads 126

Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 94

Authors: N. Mehrotra, V. Ahuja, N. Sridharan

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Resilience is an all hazard and a proactive approach, require a multidisciplinary input in the inter related variables of the city system. This research based to identify and operationalize indicators for assessment in domain of institutions, infrastructure and knowledge, all three operating in task oriented community networks. This paper gives a brief account of the methodology developed for assessment of Urban Resilience and its prioritized components for a target population within a newly planned urban complex integrating Surajpur and Kasna village as nodes. People’s perception of Urban Resilience has been examined by conducting questionnaire survey among the target population of Greater Noida. As defined by experts, Urban Resilience of a place is considered to be both a product and process of operation to regain normalcy after an event of disturbance of certain level. Based on this methodology, six indicators are identified that contribute to perception of urban resilience both as in the process of evolution and as an outcome. The relative significance of 6 R’ has also been identified. The dependency factor of various resilience indicators have been explored in this paper, which helps in generating new perspective for future research in disaster management. Based on the stated factors this methodology can be applied to assess urban resilience requirements of a well planned town, which is not an end in itself, but calls for new beginnings.

Keywords: disaster, resilience, system, urban

Procedia PDF Downloads 442

Authors: James Hilton Williams

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Recent tsunami events, including the 2011 Tohoku Tsunami, Japan, and the 2015 Illapel Tsunami, Chile, have highlighted the potential for tsunami impacts on the built environment. International research in the tsunami impacts domain has been largely focused toward impacts on buildings and casualty estimations, while only limited attention has been placed on the impacts on infrastructure which is critical for the recovery of impacted communities. New Zealand, with 75% of the population within 10 km of the coast, has a large amount of coastal infrastructure exposed to local, regional and distant tsunami sources. To effectively manage tsunami risk for New Zealand critical infrastructure, including energy, transportation, and communications, the vulnerability of infrastructure networks and components must first be determined. This research develops infrastructure asset vulnerability, functionality and repair- cost functions based on international post-event tsunami impact assessment data from technologically similar countries, including Japan and Chile, and adapts these to New Zealand. These functions are then utilized within a New Zealand based impact framework, allowing for cost benefit analyses, effective tsunami risk management strategies and mitigation options for exposed critical infrastructure to be determined, which can also be applied internationally.

Keywords: impact assessment, infrastructure, tsunami impacts, vulnerability functions

Procedia PDF Downloads 139

Authors: Charity Ifunanya Osakwe, Victoria Ogochuchukwu Obi-Nwosu, Chima Kenneth Anachedo

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The study investigates how automated teller machines (ATM) and mobile banking affect deposit money banks in the Nigerian economy. The study made use of time series data which were obtained from the Central Bank of Nigeria Statistical Bulletin from 2009 to 2021. The Central Bank of Nigeria (CBN) data on automated teller machine and mobile phones were used to proxy electronic banking while total deposit in banks proxied the performance of deposit money banks. The analysis for the study was done using ordinary least square econometric technique with the aid of economic view statistical package. The results show that the automated teller machine has a positive and significant effect on the total deposits of deposit money banks in Nigeria and that making use of deposits of deposit money banks in Nigeria. It was concluded in the study that e-banking has equally increased banking access to customers and also created room for banks to expand their operations to more customers. The study recommends that banks in Nigeria should prioritize the expansion and maintenance of ATM networks as well as continue to invest in and develop more mobile banking services.

Keywords: electronic, banking, automated teller machines, mobile, deposit

Procedia PDF Downloads 36

Authors: Peter J. Glinos

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The Alternative Education Resource Organization (AERO), one of the largest international hubs for alternative educators led by Jerry Mintz, has had a major impact on the global alternative education movement. The organization’s publications, like the AERO-Gramme Newsletter and its successor, the Education Revolution Magazine, allowed members across the globe to discuss issues, share support, and submit writings on policies and reforms. Stored on AERO's online digital archive, this work uses these publications from 1989 to 2011 to investigate the network's entanglements with America, Canada, Russia, Ukraine, Israel, Palestine, Japan, India, and Guatemala. Inspired by Reinhart Koselleck, this historical analysis will trace AERO’s entanglements within the United States, Japan, and Russia, contextualizing each of these multiple temporalities within the history of each nation’s education system, the developments within AERO, and the global geo-political climate at the time of AERO’s expansion. To help remedy the lack of attention paid by global historians to the role state organizations play supporting global networks, as noted in What is Global History? by Sebastian Conrad, this work will focus on the relationship between AERO and state actors.

Keywords: global history, history of education, neoliberalism, transnational history, alternative education

Procedia PDF Downloads 13

Authors: Salha Abdullah Ali Al-Shamrani, Maha Muhammad Dhaher Aljuhani, Eman Ali Ahmed Aldhaheri

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With the proliferation of Internet of Things (IoT) devices in various industries, there has been a concurrent rise in security vulnerabilities, particularly spoofing attacks. This study explores the potential of blockchain technology in enhancing the security of IoT systems and mitigating these attacks. Blockchain's decentralized and immutable ledger offers significant promise for improving data integrity, transaction transparency, and tamper-proofing. This research develops and implements a blockchain-based IoT architecture and a reference network to simulate real-world scenarios and evaluate a blockchain-integrated intrusion detection system. Performance measures including time delay, security, and resource utilization are used to assess the system's effectiveness, comparing it to conventional IoT networks without blockchain. The results provide valuable insights into the practicality and efficacy of employing blockchain as a security mechanism, shedding light on the trade-offs between speed and security in blockchain deployment for IoT. The study concludes that despite minor increases in time consumption, the security benefits of incorporating blockchain technology into IoT systems outweigh potential drawbacks, demonstrating a significant potential for blockchain in bolstering IoT security.

Keywords: internet of things, spoofing, IoT, access control, blockchain, raspberry pi

Procedia PDF Downloads 51

Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

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Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

Procedia PDF Downloads 70

Authors: Ali Kadivar, Kaveh Niayesh

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MVDC markets for green power generations, Navy, subsea oil and gas electrification, and transportation electrification are extending rapidly. The lack of fast and powerful DC circuit breakers (CB) is the most significant barrier to realizing the medium voltage DC (MVDC) networks. A concept of hybrid circuit breakers (HCBs) benefiting from ultrafast disconnectors (UFD) is proposed. A set of mechanical switches substitute the power electronic commutation switches to reduce the losses during normal operation in HCB. The success of current commutation in such breakers relies on the behaviour of elongated, wall constricted arcs during the opening across the contacts inside the UFD. The arc voltage dependencies on the contact speed of UFDs is discussed through multiphysics simulations contact opening speeds of 10, 20 and 40 m/s. The arc voltage at a given current increases exponentially with the contact opening velocity. An empirical equation for the dynamic arc characteristics is presented for the tested UFD, and the experimentally verfied characteristics for voltage-current are utilized for the current commutation simulation prior to apply on a 14 kV experimental setup. Different failures scenarios due to the current commutation are investigated

Keywords: MVDC breakers, DC circuit breaker, fast operating breaker, ultra-fast elongated arc

Procedia PDF Downloads 68

Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

Procedia PDF Downloads 369

Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

Procedia PDF Downloads 460

Authors: Sojung Kim

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This paper explores how the Partition of Korea is absorbed into everyday life through North Korean migrant women’s rituals for traditional holidays in Korea. In national holidays called myungjul, Koreans traditionally visit their paternal ancestor’s hometowns to hold jesa, the rites for the ancestors, at the graves and home. Due to the physical gaps in the kinship networks, marked by the kin left behind in North Korea, North Korean migrants gather among themselves in the neighborhood in South Korea as if they make the myungjul ritual of the family gatherings. This impossibility of the proper practice of the rites insinuates the violence of the Partition refracted into the family relations between those in the South and those in the North. Yet, the myungjul gathering creates a kind of collective hometown, beside one’s genealogical hometown, where they can express lamentation and guilt over not being able to visit their parents and ancestors in their hometowns, which they are traditionally required to do. In this as-if ritual, myungjul is re-created for and by the women and for others in the community. Yet, the texture of this ritual is marked by discontent and dissatisfaction. Attending to fostering discontents that seep into the collective events, this paper aims to seek ways to study the violence that permeated in everyday life in partitioned Korea.

Keywords: as-if ritual, everyday life, kinship, migration

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Authors: Nuri Caglayan, H. Kursat Celik

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There are many factors that influence the health and productivity of the animals in livestock production fields, including temperature, humidity, carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), physical activity and particulate matter. High NH3 concentrations reduce feed consumption and cause daily weight gain. At high concentrations, H2S causes respiratory problems and CO2 displace oxygen, which can cause suffocation or asphyxiation. Good air quality in livestock facilities can have an impact on the health and well-being of animals and humans. Air quality assessment basically depends on strictly given limits without taking into account specific local conditions between harmful gases and other meteorological factors. The stated limitations may be eliminated. using controlling systems based on neural networks and fuzzy logic. This paper describes a fuzzy logic based ventilation algorithm, which can calculate different fan speeds under pre-defined boundary conditions, for removing harmful gases from the production environment. In the paper, a fuzzy logic model has been developed based on a Mamedani’s fuzzy method. The model has been built on MATLAB software. As the result, optimum fan speeds under pre-defined boundary conditions have been presented.

Keywords: air quality, fuzzy logic model, livestock housing, fan speed

Procedia PDF Downloads 357

Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

Procedia PDF Downloads 149

Authors: Olaiya Folorunsho, Comfort Y. Daramola, Joel N. Ugwu, Lawrence B. Adewole, Olufisayo S. Ekundayo

Abstract:

In today world, there is a tremendous rise in the usage of internet due to the fact that almost all the communication and information sharing is done over the web. Conversely, there is a continuous growth of unauthorized access to confidential data. This has posed a challenge to information security expertise whose major goal is to curtail the menace. One of the approaches to secure the safety delivery of data/information to the rightful destination without any modification is steganography. Steganography is the art of hiding information inside an embedded information. This research paper aimed at designing a secured algorithm with the use of image steganographic technique that makes use of Least Significant Bit (LSB) algorithm for embedding the data into the bit map image (bmp) in order to enhance security and reliability. In the LSB approach, the basic idea is to replace the LSB of the pixels of the cover image with the Bits of the messages to be hidden without destroying the property of the cover image significantly. The system was implemented using C# programming language of Microsoft.NET framework. The performance evaluation of the proposed system was experimented by conducting a benchmarking test for analyzing the parameters like Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The result showed that image steganography performed considerably in securing data hiding and information transmission over the networks.

Keywords: steganography, image steganography, least significant bits, bit map image

Procedia PDF Downloads 245

Authors: Georgios Linardatos, Miltiadis Zamparas, Vlasoula Bekiari, Georgios Bokias, Georgios Hotos

Abstract:

Hydrogels are three-dimensional, hydrophilic, polymeric networks composed of homopolymers or copolymers and are insoluble in water due to the presence of chemical or physical cross-links. When hydrogels come in contact with aqueous solutions, they can effectively sorb and retain the dissolved substances, depending on the nature of the monomeric units comprising the hydrogel. For this reason, hydrogels have been proposed in several studies as water purification agents. At the present work anionic hydrogels bearing negatively charged –COO- groups were prepared and investigated. These gels are based on sodium acrylate (ANa), either homopolymerized (poly(sodiumacrylate), PANa) or copolymerized (P(DMAM-co-ANa)) with N,N Dimethylacrylamide (DMAM). The hydrogels were used to extract some model organic dyes from water. It is found that cationic dyes are strongly sorbed and retained by the hydrogels, while sorption of anionic dyes was negligible. In all cases it was found that both maximum sorption capacity and equilibrium binding constant varied from one dye to the other depending on the chemical structure of the dye, the presence of functional chemical groups and the hydrophobic-hydrophilic balance. Finally, the nonionic hydrogel of the homopolymer poly(N,N-dimethylacrylamide), PDMAM, was also used for reasons of comparison.

Keywords: anionic organic hydrogels, sorption, organic dyes, water purification agents

Procedia PDF Downloads 241

Authors: Yousef Almutairi, Yajue Wu

Abstract:

Various large-scale trends have characterized the current century thus far, including increasing shifts towards urbanization and greater movement. It is predicted that there will be 9.3 billion people on Earth in 2050 and that over two-thirds of this population will be city dwellers. Moreover, in larger cities worldwide, mass transportation systems, including underground systems, have grown to account for the majority of travel in those settings. Underground networks are vulnerable to fires, however, endangering travellers’ safety, with various examples of fire outbreaks in this setting. This study aims to increase knowledge of the impacts of extreme climatic conditions on fires, including the role of the high ambient temperatures experienced in Middle Eastern countries and specifically in Saudi Arabia. This is an element that is not always included when assessments of fire safety are made (considering visibility, temperatures, and flows of smoke). This paper focuses on a tunnel within Riyadh’s underground system as a case study and includes simulations based on computational fluid dynamics using ANSYS Fluent, which investigates the impact of various ventilation systems while identifying smoke density, speed, pressure and temperatures within this tunnel.

Keywords: fire, subway tunnel, CFD, mechanical ventilation, smoke, temperature, harsh weather

Procedia PDF Downloads 105