Search results for: deep residual networks
3988 Topological Analyses of Unstructured Peer to Peer Systems: A Survey
Authors: Hend Alrasheed
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Due to their different properties that have led to avoid several limitations of classic client/server systems, there has been a great interest in the development and the improvement of different peer to peer systems. Understanding the properties of complex peer to peer networks is essential for their future improvements. It was shown that the performances of peer to peer protocols are directly related to their underlying topologies. Therefore, multiple efforts have analyzed the topologies of different peer to peer systems. This study presents an overview of major findings of close experimental analyses to different topologies of three unstructured peer to peer systems: BitTorrent, Gnutella, and FreeNet.Keywords: peer to peer networks, network topology, graph diameter, clustering coefficient, small-world property, random graph, degree distribution
Procedia PDF Downloads 3763987 Gulfnet: The Advent of Computer Networking in Saudi Arabia and Its Social Impact
Authors: Abdullah Almowanes
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The speed of adoption of new information and communication technologies is often seen as an indicator of the growth of knowledge- and technological innovation-based regional economies. Indeed, technological progress and scientific inquiry in any society have undergone a particularly profound transformation with the introduction of computer networks. In the spring of 1981, the Bitnet network was launched to link thousands of nodes all over the world. In 1985 and as one of the first adopters of Bitnet, Saudi Arabia launched a Bitnet-based network named Gulfnet that linked computer centers, universities, and libraries of Saudi Arabia and other Gulf countries through high speed communication lines. In this paper, the origins and the deployment of Gulfnet are discussed as well as social, economical, political, and cultural ramifications of the new information reality created by the network. Despite its significance, the social and cultural aspects of Gulfnet have not been investigated in history of science and technology literature to a satisfactory degree before. The presented research is based on an extensive archival research aimed at seeking out and analyzing of primary evidence from archival sources and records. During its decade and a half-long existence, Gulfnet demonstrated that the scope and functionality of public computer networks in Saudi Arabia have to be fine-tuned for compliance with Islamic culture and political system of the country. It also helped lay the groundwork for the subsequent introduction of the Internet. Since 1980s, in just few decades, the proliferation of computer networks has transformed communications world-wide.Keywords: Bitnet, computer networks, computing and culture, Gulfnet, Saudi Arabia
Procedia PDF Downloads 2413986 The Morphogenesis of an Informal Settlement: An Examination of Street Networks through the Informal Development Stages Framework
Authors: Judith Margaret Tymon
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As cities struggle to incorporate informal settlements into the fabric of urban areas, the focus has often been on the provision of housing. This study explores the underlying structure of street networks, with the goal of understanding the morphogenesis of informal settlements through the lens of the access network. As the stages of development progress from infill to consolidation and eventually, to a planned in-situ settlement, the access networks retain the form of the core segments; however, a majority of street patterns are adapted to a grid design to support infrastructure in the final upgraded phase. A case study is presented to examine the street network in the informal settlement of Gobabis Namibia as it progresses from its initial stages to a planned, in-situ, and permanently upgraded development. The Informal Development Stages framework of foundation, infill, and consolidation, as developed by Dr. Jota Samper, is utilized to examine the evolution of street networks. Data is gathered from historical Google Earth satellite images for the time period between 2003 and 2022. The results demonstrate that during the foundation through infill stages, incremental changes follow similar patterns, with pathways extended, lengthened, and densified as housing is created and the settlement grows. In the final stage of consolidation, the resulting street layout is transformed to support the installation of infrastructure; however, some elements of the original street patterns remain. The core pathways remain intact to accommodate the installation of infrastructure and the creation of housing plots, defining the shape of the settlement and providing the basis of the urban form. The adaptations, growth, and consolidation of the street network are critical to the eventual formation of the spatial layout of the settlement. This study will include a comparative analysis of findings with those of recent research performed by Kamalipour, Dovey, and others regarding incremental urbanism within informal settlements. Further comparisons will also include studies of street networks of well-established urban centers that have shown links between the morphogenesis of access networks and the eventual spatial layout of the city. The findings of the study can be used to guide and inform strategies for in-situ upgrading and can contribute to the sustainable development of informal settlements.Keywords: Gobabis Namibia, incremental urbanism, informal development stages, informal settlements, street networks
Procedia PDF Downloads 573985 A Study of Behaviors in Using Social Networks of Corporate Personnel of Suan Sunandha Rajabhat University
Authors: Wipada Chaiwchan
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This research aims to study behaviors in using social networks of Corporate personnel of Suan Sunandha Rajabhat University. The sample used in the study were two groups: 1) Academic Officer 70 persons and 2) Operation Officer 143 persons were used in this study. The tools in this research consisted of questionnaire which the data were analyzed by using percentage, average (X) and Standard deviation (S.D.) and Independent Sample T-Test to test the difference between the mean values obtained from two independent samples, and One-way anova to analysis of variance, and Multiple comparisons to test that the average pair of different methods by Fisher’s Least Significant Different (LSD). The study result found that the most of corporate personnel have purpose in using social network to information awareness aspect was knowledge and online conference with social media. By using the average more than 3 hours per day in everyday. Using time in working in one day and there are computers connected to the Internet at home, by using the communication in the operational processes. Behaviors using social networks in relation to gender, age, job title, department, and type of personnel. Hypothesis testing, and analysis of variance for the effects of this analysis is divided into three aspects: The use of online social networks, the attitude of the users and the security analysis has found that Corporate Personnel of Suan Sunandha Rajabhat University. Overall and specifically at the high level, and considering each item found all at a high level. By sorting of the social network (X=3.22), The attitude of the users (X= 3.06) and the security (X= 3.11). The overall behaviors using of each side (X=3.11).Keywords: social network, behaviors, social media, computer information systems
Procedia PDF Downloads 3913984 Factors Affecting Weld Line Movement in Tailor Welded Blank
Authors: Sanjay Patil, Shakil A. Kagzi, Harit K. Raval
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Tailor Welded Blanks (TWB) are utilized in automotive industries widely because of their advantage of weight and cost reduction and maintaining required strength and structural integrity. TWB consist of two or more sheet having dissimilar or similar material and thickness; welded together to form a single sheet before forming it to desired shape. Forming of the tailor welded blank is affected by ratio of thickness of blanks, ratio of their strength, etc. mainly due to in-homogeneity of material. In the present work the relative effect of these parameters on weld line movement is studied during deep drawing of TWB using FE simulation using HYPERWORKS. The simulation is validated with results from the literature. Simulations were than performed based on Taguchi orthogonal array followed by the ANOVA analysis to determine the significance of these parameters on forming of TWB.Keywords: ANOVA, deep drawing, Tailor Welded Blank (TWB), weld line movement
Procedia PDF Downloads 3083983 Deep Foundations: Analysis of the Lateral Response of Closed Ended Steel Tubular Piles Embedded in Sandy Soil Using P-Y Curves
Authors: Ameer A. Jebur, William Atherton, Rafid M. Alkhaddar, Edward Loffill
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Understanding the behaviour of the piles under the action of the independent lateral loads and the precise prediction of the capacity of piles subjected to different lateral loads are vital topics in foundation design and analysis. Moreover, the laterally loaded behaviour of deep foundations penetrated in cohesive and non-cohesive soils is basically analysed by the Winkler Model (beam on elastic foundation), in which the interaction between the pile embedded depth and contacted soil is simulated by nonlinear p–y curves. The presence of many approaches to interpret the behaviour of soil-pile interaction has resulted in numerous outputs and indicates that no general approach has yet been adopted. The current study presents the result of numerical modelling of the behaviour of steel tubular piles (25.4mm) outside diameter with various embedment depth-to-diameter ratios (L/d) embedded in a sand calibrated chamber of known relative density. The study revealed that the shear strength parameters of the sand specimens and the (L/d) ratios are the most significant factor influencing the response of the pile and its capacity while taking into consideration the complex interaction between the pile and soil. Good agreement has been achieved when comparing the application of this modelling approach with experimental physical modelling carried out by another researcher.Keywords: deep foundations, slenderness ratio, soil-pile interaction, winkler model (beam on elastic foundation), non-cohesive soil
Procedia PDF Downloads 2953982 Movie and Theater Marketing Using the Potentials of Social Networks
Authors: Seyed Reza Naghibulsadat
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The nature of communication includes various forms of media productions, which include film and theater. In the current situation, since social networks have emerged, they have brought their own communication capabilities and have features that show speed, public access, lack of media organization and the production of extensive content, and the development of critical thinking; Also, they contain capabilities to develop access to all kinds of media productions, including movies and theater shows; Of course, this works differently in different conditions and communities. In terms of the scale of exploitation, the film has a more general audience, and the theater has a special audience. The film industry is more developed based on more modern technologies, but the theater, based on the older ways of communication, contains more intimate and emotional aspects. ; But in general, the main focus is the development of access to movies and theater shows, which is emphasized by those involved in this field due to the capabilities of social networks. In this research, we will look at these 2 areas and the relevant components for both areas through social networks and also the common points of both types of media production. The main goal of this research is to know the strengths and weaknesses of using social networks for the marketing of movies and theater shows and, at the same time are, also considered the opportunities and threats of this field. The attractions of these two types of media production, with the emergence of social networks, and the ability to change positions, can provide the opportunity to become a media with greater exploitation and higher profitability; But the main consideration is the opinions about these capabilities and the ability to use them for film and theater marketing. The main question of the research is, what are the marketing components for movies and theaters using social media capabilities? What are its strengths and weaknesses? And what opportunities and threats are facing this market? This research has been done with two methods SWOT and meta-analysis. Non-probability sampling has been used with purposeful technique. The results show that a recent approach is an approach based on eliminating threats and weaknesses and emphasizing strengths, and exploiting opportunities in the direction of developing film and theater marketing based on the capabilities of social networks within the framework of local cultural values and presenting achievements on an international scale or It is universal. This introduction leads to the introduction of authentic Iranian culture and foreign enthusiasts in the framework of movies and theater art. Therefore, for this issue, the model for using the capabilities of social networks for movie or theater marketing, according to the results obtained from Respondents, is a model based on SO strategies and, in other words, offensive strategies so that it can take advantage of the internal strengths and made maximum use of foreign situations and opportunities to develop the use of movies and theater performances.Keywords: marketing, movies, theatrical show, social network potentials
Procedia PDF Downloads 703981 Presentation of a Mix Algorithm for Estimating the Battery State of Charge Using Kalman Filter and Neural Networks
Authors: Amin Sedighfar, M. R. Moniri
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Determination of state of charge (SOC) in today’s world becomes an increasingly important issue in all the applications that include a battery. In fact, estimation of the SOC is a fundamental need for the battery, which is the most important energy storage in Hybrid Electric Vehicles (HEVs), smart grid systems, drones, UPS and so on. Regarding those applications, the SOC estimation algorithm is expected to be precise and easy to implement. This paper presents an online method for the estimation of the SOC of Valve-Regulated Lead Acid (VRLA) batteries. The proposed method uses the well-known Kalman Filter (KF), and Neural Networks (NNs) and all of the simulations have been done with MATLAB software. The NN is trained offline using the data collected from the battery discharging process. A generic cell model is used, and the underlying dynamic behavior of the model has used two capacitors (bulk and surface) and three resistors (terminal, surface, and end), where the SOC determined from the voltage represents the bulk capacitor. The aim of this work is to compare the performance of conventional integration-based SOC estimation methods with a mixed algorithm. Moreover, by containing the effect of temperature, the final result becomes more accurate.Keywords: Kalman filter, neural networks, state-of-charge, VRLA battery
Procedia PDF Downloads 1863980 Modern Machine Learning Conniptions for Automatic Speech Recognition
Authors: S. Jagadeesh Kumar
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This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning
Procedia PDF Downloads 4363979 Osseointegration Outcomes Following Amputee Lengthening
Authors: Jason Hoellwarth, Atiya Oomatia, Anuj Chavan, Kevin Tetsworth, Munjed Al Muderis
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Introduction: Percutaneous EndoProsthetic Osseointegration for Limbs (PEPOL) facilitates improved quality of life (QOL) and objective mobility for most amputees discontent with their traditional socket prosthesis (TSP) experience. Some amputees desiring PEPOL have residual bone much shorter than the currently marketed press-fit implant lengths of 14-16 cm, potentially a risk for failure to integrate. We report on the techniques used, complications experienced, the management of those complications, and the overall mobility outcomes of seven patients who had femur distraction osteogenesis (DO) with a Freedom nail followed by PEPOL. Method: Retrospective evaluation of a prospectively maintained database identified nine patients (5 females) who had transfemoral DO in preparation for PEPOL with two years of follow-up after PEPOL. Six patients had traumatic causes of amputation, one had perinatal complications, one was performed to manage necrotizing fasciitis and one was performed as a result of osteosarcoma. Result: The average age at which DO commenced was 39.4±15.9 years, and seven patients had their amputation more than ten years prior (average 25.5±18.8 years). The residual femurs, on average, started at 102.2±39.7 mm and were lengthened 58.1±20.7 mm, 98±45% of the goal (99±161% of the original bone length). Five patients (56%) had a complication requiring additional surgery: four events of inadequate regeneration were managed with continued lengthening to the desired goal followed by autograft placement harvested from contralateral femur reaming; one patient had the cerclage wires break, which required operative replacement. All patients had osseointegration performed at 355±123 days after the initial lengthening nail surgery. One patient had K-level >2 before DO, at a mean of 3.4±0.6 (2.6-4.4) years following osseointegration. Six patients had K-level >2. The 6-Minute Walk Test remained unchanged (267±56 vs. 308 ± 117 meters). Patient self-rating of prosthesis function, problems, and amputee situation did not significantly change from before DO to after osseointegration. Six patients required additional surgery following osseointegration: six to remove fixation plates placed to maintain distraction osteogenesis length at osseointegration; two required irritation and debridement for infection. Conclusion: Extremely short residual femurs, which make TSP use troublesome, can be lengthened with externally controlled telescoping nails and successfully achieve osseointegration. However, it is imperative to counsel patients that additional surgery to address inadequate regeneration or to remove painful hardware used to maintain fixation may be necessary. This may improve the amputee’s expectations before beginning a potentially arduous process.Keywords: osseointegration, limb lengthening, quality of life, amputation
Procedia PDF Downloads 663978 Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcome
Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler
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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model
Procedia PDF Downloads 1513977 Using Machine Learning to Classify Different Body Parts and Determine Healthiness
Authors: Zachary Pan
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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.Keywords: body part, healthcare, machine learning, neural networks
Procedia PDF Downloads 943976 Graphical User Interface Testing by Using Deep Learning
Authors: Akshat Mathur, Sunil Kumar Khatri
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This paper presents brief about how the use of Artificial intelligence in respect to GUI testing can reduce workload by using DL-fueled method. This paper also discusses about how graphical user interface and event driven software testing can derive benefits from the use of AI techniques. The use of AI techniques not only reduces the task and work load but also helps in getting better output than manual testing. Although results are same, but the use of Artifical intelligence techniques for GUI testing has proven to provide ideal results. DL-fueled framework helped us to find imperfections of the entire webpage and provides test failure result in a score format between 0 and 1which signifies that are test meets it quality criteria or not. This paper proposes DL-fueled method which helps us to find the genuine GUI bugs and defects and also helped us to scale the existing labour-intensive and skill-intensive methodologies.Keywords: graphical user interface, GUI, artificial intelligence, deep learning, ML technology
Procedia PDF Downloads 1643975 A Feasibility Study of Crowdsourcing Data Collection for Facility Maintenance Management
Authors: Mohamed Bin Alhaj, Hexu Liu, Mohammed Sulaiman, Osama Abudayyeh
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An effective facility maintenance management (FMM) system plays a crucial role in improving the quality of services and maintaining the facility in good condition. Current FMM heavily relies on the quality of the data collection function of the FMM systems, at times resulting in inefficient FMM decision-making. The new technology-based crowdsourcing provides great potential to improve the current FMM practices, especially in terms of timeliness and quality of data. This research aims to investigate the feasibility of using new technology-driven crowdsourcing for FMM and highlight its opportunities and challenges. A survey was carried out to understand the human, data, system, geospatial, and automation characteristics of crowdsourcing for an educational campus FMM via social networks. The survey results were analyzed to reveal the challenges and recommendations for the implementation of crowdsourcing for FMM. This research contributes to the body of knowledge by synthesizing the challenges and opportunities of using crowdsourcing for facility maintenance and providing a road map for applying crowdsourcing technology in FMM. In future work, a conceptual framework will be proposed to support data-driven FMM using social networks.Keywords: crowdsourcing, facility maintenance management, social networks
Procedia PDF Downloads 1613974 Evaluation of Sequential Polymer Flooding in Multi-Layered Heterogeneous Reservoir
Authors: Panupong Lohrattanarungrot, Falan Srisuriyachai
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Polymer flooding is a well-known technique used for controlling mobility ratio in heterogeneous reservoirs, leading to improvement of sweep efficiency as well as wellbore profile. However, low injectivity of viscous polymer solution attenuates oil recovery rate and consecutively adds extra operating cost. An attempt of this study is to improve injectivity of polymer solution while maintaining recovery factor, enhancing effectiveness of polymer flooding method. This study is performed by using reservoir simulation program to modify conventional single polymer slug into sequential polymer flooding, emphasizing on increasing of injectivity and also reduction of polymer amount. Selection of operating conditions for single slug polymer including pre-injected water, polymer concentration and polymer slug size is firstly performed for a layered-heterogeneous reservoir with Lorenz coefficient (Lk) of 0.32. A selected single slug polymer flooding scheme is modified into sequential polymer flooding with reduction of polymer concentration in two different modes: Constant polymer mass and reduction of polymer mass. Effects of Residual Resistance Factor (RRF) is also evaluated. From simulation results, it is observed that first polymer slug with the highest concentration has the main function to buffer between displacing phase and reservoir oil. Moreover, part of polymer from this slug is also sacrificed for adsorption. Reduction of polymer concentration in the following slug prevents bypassing due to unfavorable mobility ratio. At the same time, following slugs with lower viscosity can be injected easily through formation, improving injectivity of the whole process. A sequential polymer flooding with reduction of polymer mass shows great benefit by reducing total production time and amount of polymer consumed up to 10% without any downside effect. The only advantage of using constant polymer mass is slightly increment of recovery factor (up to 1.4%) while total production time is almost the same. Increasing of residual resistance factor of polymer solution yields a benefit on mobility control by reducing effective permeability to water. Nevertheless, higher adsorption results in low injectivity, extending total production time. Modifying single polymer slug into sequence of reduced polymer concentration yields major benefits on reducing production time as well as polymer mass. With certain design of polymer flooding scheme, recovery factor can even be further increased. This study shows that application of sequential polymer flooding can be certainly applied to reservoir with high value of heterogeneity since it requires nothing complex for real implementation but just a proper design of polymer slug size and concentration.Keywords: polymer flooding, sequential, heterogeneous reservoir, residual resistance factor
Procedia PDF Downloads 4673973 A Study on Using Network Coding for Packet Transmissions in Wireless Sensor Networks
Authors: Rei-Heng Cheng, Wen-Pinn Fang
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A wireless sensor network (WSN) is composed by a large number of sensors and one or a few base stations, where the sensor is responsible for detecting specific event information, which is sent back to the base station(s). However, how to save electricity consumption to extend the network lifetime is a problem that cannot be ignored in the wireless sensor networks. Since the sensor network is used to monitor a region or specific events, how the information can be reliably sent back to the base station is surly important. Network coding technique is often used to enhance the reliability of the network transmission. When a node needs to send out M data packets, it encodes these data with redundant data and sends out totally M + R packets. If the receiver can get any M packets out from these M + R packets, it can decode and get the original M data packets. To transmit redundant packets will certainly result in the excess energy consumption. This paper will explore relationship between the quality of wireless transmission and the number of redundant packets. Hopefully, each sensor can overhear the nearby transmissions, learn the wireless transmission quality around it, and dynamically determine the number of redundant packets used in network coding.Keywords: energy consumption, network coding, transmission reliability, wireless sensor networks
Procedia PDF Downloads 3863972 “Octopub”: Geographical Sentiment Analysis Using Named Entity Recognition from Social Networks for Geo-Targeted Billboard Advertising
Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri
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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing
Procedia PDF Downloads 5813971 Prediction of Bodyweight of Cattle by Artificial Neural Networks Using Digital Images
Authors: Yalçın Bozkurt
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Prediction models were developed for accurate prediction of bodyweight (BW) by using Digital Images of beef cattle body dimensions by Artificial Neural Networks (ANN). For this purpose, the animal data were collected at a private slaughter house and the digital images and the weights of each live animal were taken just before they were slaughtered and the body dimensions such as digital wither height (DJWH), digital body length (DJBL), digital body depth (DJBD), digital hip width (DJHW), digital hip height (DJHH) and digital pin bone length (DJPL) were determined from the images, using the data with 1069 observations for each traits. Then, prediction models were developed by ANN. Digital body measurements were analysed by ANN for body prediction and R2 values of DJBL, DJWH, DJHW, DJBD, DJHH and DJPL were approximately 94.32, 91.31, 80.70, 83.61, 89.45 and 70.56 % respectively. It can be concluded that in management situations where BW cannot be measured it can be predicted accurately by measuring DJBL and DJWH alone or both DJBD and even DJHH and different models may be needed to predict BW in different feeding and environmental conditions and breedsKeywords: artificial neural networks, bodyweight, cattle, digital body measurements
Procedia PDF Downloads 3613970 Worst-Case Load Shedding in Electric Power Networks
Authors: Fu Lin
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We consider the worst-case load-shedding problem in electric power networks where a number of transmission lines are to be taken out of service. The objective is to identify a prespecified number of line outages that lead to the maximum interruption of power generation and load at the transmission level, subject to the active power-flow model, the load and generation capacity of the buses, and the phase-angle limit across the transmission lines. For this nonlinear model with binary constraints, we show that all decision variables are separable except for the nonlinear power-flow equations. We develop an iterative decomposition algorithm, which converts the worst-case load shedding problem into a sequence of small subproblems. We show that the subproblems are either convex problems that can be solved efficiently or nonconvex problems that have closed-form solutions. Consequently, our approach is scalable for large networks. Furthermore, we prove the convergence of our algorithm to a critical point, and the objective value is guaranteed to decrease throughout the iterations. Numerical experiments with IEEE test cases demonstrate the effectiveness of the developed approach.Keywords: load shedding, power system, proximal alternating linearization method, vulnerability analysis
Procedia PDF Downloads 1363969 Analysis of Decentralized on Demand Cross Layer in Cognitive Radio Ad Hoc Network
Authors: A. Sri Janani, K. Immanuel Arokia James
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Cognitive radio ad hoc networks different unlicensed users may acquire different available channel sets. This non-uniform spectrum availability imposes special design challenges for broadcasting in CR ad hoc networks. Cognitive radio automatically detects available channels in wireless spectrum. This is a form of dynamic spectrum management. Cross-layer optimization is proposed, using this can allow far away secondary users can also involve into channel work. So it can increase the throughput and it will overcome the collision and time delay.Keywords: cognitive radio, cross layer optimization, CR mesh network, heterogeneous spectrum, mesh topology, random routing optimization technique
Procedia PDF Downloads 3543968 Deep Reinforcement Learning with Leonard-Ornstein Processes Based Recommender System
Authors: Khalil Bachiri, Ali Yahyaouy, Nicoleta Rogovschi
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Improved user experience is a goal of contemporary recommender systems. Recommender systems are starting to incorporate reinforcement learning since it easily satisfies this goal of increasing a user’s reward every session. In this paper, we examine the most effective Reinforcement Learning agent tactics on the Movielens (1M) dataset, balancing precision and a variety of recommendations. The absence of variability in final predictions makes simplistic techniques, although able to optimize ranking quality criteria, worthless for consumers of the recommendation system. Utilizing the stochasticity of Leonard-Ornstein processes, our suggested strategy encourages the agent to investigate its surroundings. Research demonstrates that raising the NDCG (Discounted Cumulative Gain) and HR (HitRate) criterion without lowering the Ornstein-Uhlenbeck process drift coefficient enhances the diversity of suggestions.Keywords: recommender systems, reinforcement learning, deep learning, DDPG, Leonard-Ornstein process
Procedia PDF Downloads 1343967 Study of Oxidative Stability, Cold Flow Properties and Iodine Value of Macauba Biodiesel Blends
Authors: Acacia A. Salomão, Willian L. Gomes da Silva, Gustavo G. Shimamoto, Matthieu Tubino
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Biodiesel physical and chemical properties depend on the raw material composition used in its synthesis. Saturated fatty acid esters confer high oxidative stability, while unsaturated fatty acid esters improve the cold flow properties. In this study, an alternative vegetal source - the macauba kernel oil - was used in the biodiesel synthesis instead of conventional sources. Macauba can be collected from native palm trees and is found in several regions in Brazil. Its oil is a promising source when compared to several other oils commonly obtained from food products, such as soybean, corn or canola oil, due to its specific characteristics. However, the usage of biodiesel made from macauba oil alone is not recommended due to the difficulty of producing macauba in large quantities. For this reason, this project proposes the usage of blends of the macauba oil with conventional oils. These blends were prepared by mixing the macauba biodiesel with biodiesels obtained from soybean, corn, and from residual frying oil, in the following proportions: 20:80, 50:50 e 80:20 (w/w). Three parameters were evaluated, using the standard methods, in order to check the quality of the produced biofuel and its blends: oxidative stability, cold filter plugging point (CFPP), and iodine value. The induction period (IP) expresses the oxidative stability of the biodiesel, the CFPP expresses the lowest temperature in which the biodiesel flows through a filter without plugging the system and the iodine value is a measure of the number of double bonds in a sample. The biodiesels obtained from soybean, residual frying oil and corn presented iodine values higher than 110 g/100 g, low oxidative stability and low CFPP. The IP values obtained from these biodiesels were lower than 8 h, which is below the recommended standard value. On the other hand, the CFPP value was found within the allowed limit (5 ºC is the maximum). Regarding the macauba biodiesel, a low iodine value was observed (31.6 g/100 g), which indicates the presence of high content of saturated fatty acid esters. The presence of saturated fatty acid esters should imply in a high oxidative stability (which was found accordingly, with IP = 64 h), and high CFPP, but curiously the latter was not observed (-3 ºC). This behavior can be explained by looking at the size of the carbon chains, as 65% of this biodiesel is composed by short chain saturated fatty acid esters (less than 14 carbons). The high oxidative stability and the low CFPP of macauba biodiesel are what make this biofuel a promising source. The soybean, corn and residual frying oil biodiesels also have low CFPP, but low oxidative stability. Therefore the blends proposed in this work, if compared to the common biodiesels, maintain the flow properties but present enhanced oxidative stability.Keywords: biodiesel, blends, macauba kernel oil, stability oxidative
Procedia PDF Downloads 5303966 Residual Analysis and Ground Motion Prediction Equation Ranking Metrics for Western Balkan Strong Motion Database
Authors: Manuela Villani, Anila Xhahysa, Christopher Brooks, Marco Pagani
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The geological structure of Western Balkans is strongly affected by the collision between Adria microplate and the southwestern Euroasia margin, resulting in a considerably active seismic region. The Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project (BSHAP) (2007-2011, 2012-2015) by NATO supported the preparation of new seismic hazard maps of the Western Balkan, but when inspecting the seismic hazard models produced later by these countries on a national scale, significant differences in design PGA values are observed in the border, for instance, North Albania-Montenegro, South Albania- Greece, etc. Considering the fact that the catalogues were unified and seismic sources were defined within BSHAP framework, obviously, the differences arise from the Ground Motion Prediction Equations selection, which are generally the component with highest impact on the seismic hazard assessment. At the time of the project, a modest database was present, namely 672 three-component records, whereas nowadays, this strong motion database has increased considerably up to 20,939 records with Mw ranging in the interval 3.7-7 and epicentral distance distribution from 0.47km to 490km. Statistical analysis of the strong motion database showed the lack of recordings in the moderate-to-large magnitude and short distance ranges; therefore, there is need to re-evaluate the Ground Motion Prediction Equation in light of the recently updated database and the new generations of GMMs. In some cases, it was observed that some events were more extensively documented in one database than the other, like the 1979 Montenegro earthquake, with a considerably larger number of records in the BSHAP Analogue SM database when compared to ESM23. Therefore, the strong motion flat-file provided from the Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project was merged with the ESM23 database for the polygon studied in this project. After performing the preliminary residual analysis, the candidate GMPE-s were identified. This process was done using the GMPE performance metrics available within the SMT in the OpenQuake Platform. The Likelihood Model and Euclidean Distance Based Ranking (EDR) were used. Finally, for this study, a GMPE logic tree was selected and following the selection of candidate GMPEs, model weights were assigned using the average sample log-likelihood approach of Scherbaum.Keywords: residual analysis, GMPE, western balkan, strong motion, openquake
Procedia PDF Downloads 733965 Comparative Analysis of Reinforcement Learning Algorithms for Autonomous Driving
Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian
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In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning
Procedia PDF Downloads 1383964 Perception-Oriented Model Driven Development for Designing Data Acquisition Process in Wireless Sensor Networks
Authors: K. Indra Gandhi
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Wireless Sensor Networks (WSNs) have always been characterized for application-specific sensing, relaying and collection of information for further analysis. However, software development was not considered as a separate entity in this process of data collection which has posed severe limitations on the software development for WSN. Software development for WSN is a complex process since the components involved are data-driven, network-driven and application-driven in nature. This implies that there is a tremendous need for the separation of concern from the software development perspective. A layered approach for developing data acquisition design based on Model Driven Development (MDD) has been proposed as the sensed data collection process itself varies depending upon the application taken into consideration. This work focuses on the layered view of the data acquisition process so as to ease the software point of development. A metamodel has been proposed that enables reusability and realization of the software development as an adaptable component for WSN systems. Further, observing users perception indicates that proposed model helps in improving the programmer's productivity by realizing the collaborative system involved.Keywords: data acquisition, model-driven development, separation of concern, wireless sensor networks
Procedia PDF Downloads 4263963 Groundwater Quality and Its Suitability for Agricultural Use in the Jeloula Basin, Tunisia
Authors: Intissar Farid
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Groundwater quality assessment is crucial for sustainable water use, especially in semi-arid regions like the Jeloula basin in Tunisia, where groundwater is essential for domestic and agricultural needs. The present research aims to characterize the suitability of groundwater for irrigational purposes by considering various parameters: total salt concentration as measured by Electrical Conductivity EC, relative proportions of Na⁺ as expressed by %Na and SAR, Kelly’s ratio, Permeability Index, Magnesium hazard and Residual Sodium chloride. Chemical data indicate that the percent sodium (%Na) in the study area ranged from 26.3 to 45.3%. According to the Wilcox diagram, the quality classification of irrigation water suggests that analyzed groundwaters are suitable for irrigation purposes. The SAR values vary between 2.1 and 5. Most of the groundwater samples plot in the Richards’C3S1 water class and indicate little danger from sodium content to soil and plant growth. The Kelly’s ratio of the analyzed samples ranged from 0.3 to 0.8. These values indicate that the waters are fit for agricultural purposes. Magnesium hazard (MH) values range from 27.5 to 52.6, with an average of 38.9 in the analyzed waters. Hence, the Mg²⁺ content of the groundwater from the shallow aquifer cannot cause any problem to the soil permeability. Permeability index (PI) values computed for the area ranged from 33.6 to 52.7%. The above result, therefore, suggests that most of the water samples fall within class I of the Doneen chart and can be categorized as good irrigation water. The groundwaters collected from the Jeloula shallow aquifer were found to be within the safe limits and thus suitable for irrigation purposes.Keywords: Kelly's ratio, magnesium hazard, permeability index, residual sodium chloride
Procedia PDF Downloads 93962 Quantitative Analysis of Presence, Consciousness, Subconsciousness, and Unconsciousness
Authors: Hooshmand Kalayeh
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The human brain consists of reptilian, mammalian, and thinking brain. And mind consists of conscious, subconscious, and unconscious parallel neural-net programs. The primary objective of this paper is to propose a methodology for quantitative analysis of neural-nets associated with these mental activities in the neocortex. The secondary objective of this paper is to suggest a methodology for quantitative analysis of presence; the proposed methodologies can be used as a first-step to measure, monitor, and understand consciousness and presence. This methodology is based on Neural-Networks (NN), number of neuron in each NN associated with consciousness, subconsciouness, and unconsciousness, and number of neurons in neocortex. It is assumed that the number of neurons in each NN is correlated with the associated area and volume. Therefore, online and offline visualization techniques can be used to identify these neural-networks, and online and offline measurement methods can be used to measure areas and volumes associated with these NNs. So, instead of the number of neurons in each NN, the associated area or volume also can be used in the proposed methodology. This quantitative analysis and associated online and offline measurements and visualizations of different Neural-Networks enable us to rewire the connections in our brain for a more balanced living.Keywords: brain, mind, consciousness, presence, sub-consciousness, unconsciousness, skills, concentrations, attention
Procedia PDF Downloads 3073961 IoT and Deep Learning approach for Growth Stage Segregation and Harvest Time Prediction of Aquaponic and Vermiponic Swiss Chards
Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia
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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.Keywords: aquaponics, deep learning, internet of things, vermiponics
Procedia PDF Downloads 613960 New Approach for Minimizing Wavelength Fragmentation in Wavelength-Routed WDM Networks
Authors: Sami Baraketi, Jean Marie Garcia, Olivier Brun
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Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods.Keywords: WDM, lightpath, RWA, wavelength fragmentation, optimization, linear programming, heuristic
Procedia PDF Downloads 5183959 Forecasting Thermal Energy Demand in District Heating and Cooling Systems Using Long Short-Term Memory Neural Networks
Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios
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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand
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