Search results for: passive optical networks (PON)

Authors: Stylianos Kampakis

Abstract:

This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

Procedia PDF Downloads 442

Authors: Sumitra Nuanmeesri

Abstract:

The research focus on study, analyze and design the model of the infrastructure and computer networks for distance education, online learning via new media, e-learning and blended learning. The collected information from study and analyze process that information was evaluated by the index of item objective congruence (IOC) by 9 specialists to design model. The results of evaluate the model with the mean and standard deviation by the sample of 9 specialists value is 3.85. The results showed that the infrastructure and computer networks are designed to be appropriate to a great extent appropriate to a great extent.

Keywords: blended learning, new media, infrastructure and computer network, tele-education, online learning

Procedia PDF Downloads 391

Authors: Tatiana A. Dolenko, Sergey A. Burikov, Alexander O. Efitorov, Sergey A. Dolenko

Abstract:

In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average.

Keywords: inverse problems, multi-component solutions, neural networks, Raman spectroscopy

Procedia PDF Downloads 516

Authors: Shyam Ranjan Kumar, Shashikant Rajpal

Abstract:

Zinc Telluride (ZnTe) is a binary II-VI direct band gap semiconducting material. This semiconducting material has several applications in sensors, photo-electrochemical devices and photovoltaic solar cell. In this study, Zinc telluride (ZnTe) thin films were deposited on nickel substrate by electrodeposition technique using potentiostat/galvanostat at -0.85 V using AR grade of Zinc Chloride (ZnCl2), Tellurium Tetrachloride (TeCl4) in non-aqueous bath. The developed films were physically stable and showed good adhesion. The as deposited ZnTe films were annealed at 400ºC in air. The solid state properties and optical properties of the as deposited and annealed films were carried out by XRD, EDS, SEM, AFM, UV–Visible spectrophotometer, and photoluminescence spectrophotometer. The diffraction peak observed at 2θ = 49.58° with (111) plane indicate the crystalline nature of ZnTe film. Annealing improves the crystalline nature of the film. Compositional analysis reveals the presence of Zn and Te with tellurium rich ZnTe film. SEM photograph at 10000X shows that grains of film are spherical in nature and densely distributed over the surface. The average roughness of the film is measured by atomic force microscopy and it is nearly equal to 60 nm. The direct wide band gap of 2.12 eV is observed by UV-Vis spectroscopy. Luminescence peak of the ZnTe films are also observed in as deposited and annealed case.

Keywords: annealing, electrodeposition, optical properties, thin film, XRD, ZnTe

Procedia PDF Downloads 181

Authors: Kahlil Fredrick Cui, Marissa Pastor

Abstract:

Earthquakes are complex phenomena, exhibiting complex correlations in space, time, and magnitude. Recently, the concept of complex networks has been used to shed light on the statistical and dynamical characteristics of regional seismicity. In this work, we study the relationships and interactions of seismic regions in Chile, Japan, and the Philippines through weighted and directed complex network analysis. Geographical areas are digitized into cells of fixed dimensions which in turn become the nodes of the network when an earthquake has occurred therein. Nodes are linked if a correlation exists between them as determined and measured by a correlation metric. The networks are found to be scale-free, exhibiting power-law behavior in the distributions of their different centrality measures: the in- and out-degree and the in- and out-strength. The evidence is also found of preferential interaction between seismically active regions through their degree-degree correlations suggesting that seismicity is dictated by the activity of a few active regions. The importance of a seismic region to the overall seismicity is measured using a generalized centrality metric taken to be an indicator of its activity or passivity. The spatial distribution of earthquake activity indicates the areas where strong earthquakes have occurred in the past while the passivity distribution points toward the likely locations an earthquake would occur whenever another one happens elsewhere. Finally, we propose a method that would project the location of the next possible earthquake using the generalized centralities coupled with correlations calculated between the latest earthquakes and a geographical point in the future.

Keywords: complex networks, correlations, earthquake, hazard assessment

Procedia PDF Downloads 198

Authors: Mariana-Dana Damaceanu

Abstract:

Electrochemical oxidation is one of the most convenient ways to obtain conjugated polymer films as polypyrrole, polyaniline, polythiophene or polycarbazole. The research in the field has been mainly directed to the study of electrical conduction properties of the materials obtained by electropolymerization, often the main reason being their use as electroconducting electrodes, and very little attention has been paid to the morphological and optical quality of the films electrodeposited on flat surfaces. Electropolymerization of the monomer solution was scarcely used in the past to manufacture polymer-based light-emitting diodes (PLED), most probably due to the difficulty of obtaining defectless polymer films with good mechanical and optical properties, or conductive polymers with well controlled molecular weights. Here we report our attempts in using electrochemical deposition as appropriate method for preparing ultrathin films of fluorene-based polymers for PLED applications. The properties of these films were evaluated in terms of structural morphology, optical properties, and electrochemical conduction. Thus, electropolymerization of 4,4'-(9-fluorenylidene)-dianiline was performed in dichloromethane solution, at a concentration of 10-2 M, using 0.1 M tetrabutylammonium tetrafluoroborate as electrolyte salt. The potential was scanned between 0 and 1.3 V on the one hand, and 0 - 2 V on the other hand, when polymer films with different structures and properties were obtained. Indium tin oxide-coated glass substrate of different size was used as working electrode, platinum wire as counter electrode and calomel electrode as reference. For each potential range 100 cycles were recorded at a scan rate of 100 mV/s. The film obtained in the potential range from 0 to 1.3 V, namely poly(FDA-NH), is visible to the naked eye, being light brown, transparent and fluorescent, and displays an amorphous morphology. Instead, the electrogrowth poly(FDA) film in the potential range of 0 - 2 V is yellowish-brown and opaque, presenting a self-assembled structure in aggregates of irregular shape and size. The polymers structure was identified by FTIR spectroscopy, which shows the presence of broad bands specific to a polymer, the band centered at approx. 3443 cm-1 being ascribed to the secondary amine. The two polymer films display two absorption maxima, at 434-436 nm assigned to π-π* transitions of polymers, and another at 832 and 880 nm assigned to polaron transitions. The fluorescence spectra indicated the presence of emission bands in the blue domain, with two peaks at 422 and 488 nm for poly (FDA-NH), and four narrow peaks at 422, 447, 460 and 484 nm for poly(FDA), peaks originating from fluorene-containing segments of varying degrees of conjugation. Poly(FDA-NH) exhibited two oxidation peaks in the anodic region and the HOMO energy value of 5.41 eV, whereas poly(FDA) showed only one oxidation peak and the HOMO level localized at 5.29 eV. The electrochemical data are discussed in close correlation with the proposed chemical structure of the electrogrowth films. Further research will be carried out to study their use and performance in light-emitting devices.

Keywords: electrogrowth polymer films, fluorene, morphology, optical properties

Procedia PDF Downloads 336

Authors: Humaira Athar, Brajeshwar Singh

Abstract:

Generally, the brightness of the indoor environment of buildings is entirely maintained by the artificial lighting which has consumed a large amount of resources. It is reported that lighting consumes about 19% of the total generated electricity which accounts for about 30-40% of total energy consumption. One possible way is to reduce the lighting energy by exploiting sunlight either through the use of suitable devices or energy efficient materials like translucent concrete. Translucent concrete is one such architectural concrete which allows the passage of natural light as well as artificial light through it. Several attempts have been made on different aspects of translucent concrete such as light guiding materials (glass fibers, plastic fibers, cylinder etc.), concrete mix design and manufacturing methods for use as building elements. Concerns are, however, raised on various related issues such as poor compatibility between the optical fibers and cement paste, unaesthetic appearance due to disturbance occurred in the arrangement of fibers during vibration and high shrinkage in flowable concrete due to its high water/cement ratio. Need is felt to develop translucent concrete to meet the requirement of structural safety as OPC concrete with the maximized saving in energy towards the power of illumination and thermal load in buildings. Translucent concrete was produced using pre-treated plastic optical fibers (POF, 2mm dia.) and high slump white concrete. The concrete mix was proportioned in the ratio of 1:1.9:2.1 with a w/c ratio of 0.40. The POF was varied from 0.8-9 vol.%. The mechanical properties and light transmission of this concrete were determined. Thermal conductivity of samples was measured by a transient plate source technique. Daylight illumination was measured by a lux grid method as per BIS:SP-41. It was found that the compressive strength of translucent concrete increased with decreasing optical fiber content. An increase of ~28% in the compressive strength of concrete was noticed when fiber was pre-treated. FE-SEM images showed little-debonded zone between the fibers and cement paste which was well supported with pull-out bond strength test results (~187% improvement over untreated). The light transmission of concrete was in the range of 3-7% depending on fiber spacing (5-20 mm). The average daylight illuminance (~75 lux) was nearly equivalent to the criteria specified for illumination for circulation (80 lux). The thermal conductivity of translucent concrete was reduced by 28-40% with respect to plain concrete. The thermal load calculated by heat conduction equation was ~16% more than the plain concrete. Based on Design-Builder software, the total annual illumination energy load of a room using one side translucent concrete was 162.36 kW compared with the energy load of 249.75 kW for a room without concrete. The calculated energy saving on an account of the power of illumination was ~25%. A marginal improvement towards thermal comfort was also noticed. It is concluded that the translucent concrete has the advantages of the existing concrete (load bearing) with translucency and insulation characteristics. It saves a significant amount of energy by providing natural daylight instead of artificial power consumption of illumination.

Keywords: energy saving, light transmission, microstructure, plastic optical fibers, translucent concrete

Procedia PDF Downloads 115

Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

Procedia PDF Downloads 75

Authors: Yogesh Kumar Tiwari

Abstract:

Chhattisgarh has realized the fundamental role of information and communication technology in the globalized world where knowledge is at the top for the growth and intellectual development. They are spreading so widely that one feels lagging behind if not using them. The influence of these radiating and technological tools has encompassed all aspects of the educational, business, and economic sectors of our world. Undeniably the computer has not only established itself globally in all walks of life but has acquired a fundamental role of paramount importance in the educational process also. This role is getting all pervading and more powerful as computers are being manufactured to be cheaper, smaller in size, adaptable and easy to handle. Computers are becoming indispensable to teachers because of their enormous capabilities and extensive competence. This study aims at observing the effect of using computer based software program of English language on the achievement of undergraduate level students studying in tribal area like Sarguja Division, Chhattisgarh, India. To testify the effect of an innovative teaching in the graduate classroom in tribal area 50 students were randomly selected and separated into two groups. The first group of 25 students were taught English grammar i.e., passive voice/narration, through traditional method using chalk and blackboard asking some formal questions. The second group, the experimental one, was taught English grammar i.e., passive voice/narration, using computer, projector with power point presentation of grammatical items. The statistical analysis was done on the students’ learning capacities and achievement. The result was extremely mesmerizing not only for the teacher but for taught also. The process of the recapitulation demonstrated that the students of experimental group responded the answers of the questions enthusiastically with innovative sense of learning. In light of the findings of the study, it was recommended that teachers and professors of English ought to use self-made instructional program in their teaching process particularly in tribal areas.

Keywords: achievement computer assisted language learning, use of instructional program

Procedia PDF Downloads 143

Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

Abstract:

This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

Procedia PDF Downloads 321

Authors: Goodness Onwuka, Khaled Abou-El-Hossein

Abstract:

The increase in the demand for precision optics, coupled with the absence of much research output in the ultra high precision grinding of precision optics as compared to the ultrahigh precision diamond turning of optical metals has fostered the need for more research in the ultra high precision grinding of an optical lens. Furthermore, the increase in the stringent demands for nanometric surface finishes through lapping, polishing and grinding processes necessary for the use of borosilicate-crown glass in the automotive and optics industries has created the demand to effectively monitor the surface roughness during the production process. Acoustic emission phenomenon has been proven as useful monitoring technique in several manufacturing processes ranging from monitoring of bearing production to tool wear estimation. This paper introduces a rare and unique approach with the application of acoustic emission technique to monitor the surface roughness of borosilicate-crown glass during an ultra high precision grinding process. This research was carried out on a 4-axes Nanoform 250 ultrahigh precision lathe machine using an ultra high precision grinding spindle to machine the flat surface of the borosilicate-crown glass with the tip of the grinding wheel. A careful selection of parameters and design of experiment was implemented using Box-Behnken method to vary the wheel speed, feed rate and depth of cut at three levels with a 3-center point design. Furthermore, the average surface roughness was measured using Taylor Hobson PGI Dimension XL optical profilometer, and an acoustic emission data acquisition device from National Instruments was utilized to acquire the signals while the data acquisition codes were designed with National Instrument LabVIEW software for acquisition at a sampling rate of 2 million samples per second. The results show that the raw and root mean square amplitude values of the acoustic signals increased with a corresponding increase in the measured average surface roughness values for the different parameter combinations. Therefore, this research concludes that acoustic emission monitoring technique is a potential technique for monitoring the surface roughness in the ultra high precision grinding of borosilicate-crown glass.

Keywords: acoustic emission, borosilicate-crown glass, surface roughness, ultra high precision grinding

Procedia PDF Downloads 284

Authors: Kurniawan D. Irianto, Demetres D. Kouvatsos

Abstract:

The growth of wireless devices affects the availability of limited frequencies or spectrum bands as it has been known that spectrum bands are a natural resource that cannot be added. Many studies about available spectrum have been done and it shows that licensed frequencies are idle most of the time. Cognitive radio is one of the solutions to solve those problems. Cognitive radio is a promising technology that allows the unlicensed users known as secondary users (SUs) to access licensed bands without making interference to licensed users or primary users (PUs). As cloud computing has become popular in recent years, cognitive radio networks (CRNs) can be integrated with cloud platform. One of the important issues in CRNs is security. It becomes a problem since CRNs use radio frequencies as a medium for transmitting and CRNs share the same issues with wireless communication systems. Another critical issue in CRNs is performance. Security has adverse effect to performance and there are trade-offs between them. The goal of this paper is to investigate the performance related to security trade-off in CRNs with supporting cloud platforms. Furthermore, Queuing Network Models with preemptive resume and preemptive repeat identical priority are applied in this project to measure the impact of security to performance in CRNs with or without cloud platform. The generalized exponential (GE) type distribution is used to reflect the bursty inter-arrival and service times at the servers. The results show that the best performance is obtained when security is disable and cloud platform is enable.

Keywords: performance vs. security, cognitive radio networks, cloud platforms, GE-type distribution

Procedia PDF Downloads 333

Authors: Svitov David, Alyamkin Sergey

Abstract:

The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax

Procedia PDF Downloads 135

Authors: Berkay Buluş, Aytekin İşman, Kübra Yüzüncüyıl

Abstract:

Developments in media and communication technologies have changed the way we use media. The importance of convergence culture has been increasing day by day within the framework of these developments. With new media, it is possible to say that social networks are the most powerful platforms that are integrated to this digitalization process. Although social networks seem like the place that people can socialize, they can also be utilized as places of production. On the other hand, audience has become users within the framework of transformation from national to global broadcasting. User generated contents make conventional media and new media collide. In this study, these communication platforms will be examined not as platforms that replace one another but mediums that unify each other. In the light of this information, information that is produced by users regarding new media platforms and all new media use practices are called convergence culture. In other words, convergence culture means intersections of conventional and new media. In this study, examples of convergence culture will be analyzed in detail.

Keywords: new media, convergence culture, convergence, use of new media, user generated content

Procedia PDF Downloads 255

Authors: Maha Aladdin, Khaled Nagaty, Abeer Hamdy

Abstract:

The rapid advancement cellular networks and wireless networks have laid a solid basis for the Internet of Things. IoT has evolved into a unique standard that allows diverse physical devices to collaborate with one another. A service provider gives a variety of services that may be accessed via smart apps anywhere, at any time, and from any location over the Internet. Because of the public environment of mobile communication and the Internet, these services are highly vulnerable to a several malicious attacks, such as unauthorized disclosure by hostile attackers. As a result, the best option for overcoming these vulnerabilities is a strong authentication method. In this paper, a lightweight authentication scheme that is based on numerical series cryptography is proposed for the IoT environments. It allows mutual authentication between IoT devices Parametric study and formal proofs are utilized to illustrate that the pro-posed approach is resistant to a variety of security threats.

Keywords: internet of things, authentication, cryptography, security protocol

Procedia PDF Downloads 106

Authors: Georgios Samaras

Abstract:

New media such as Facebook, YouTube and Twitter introduced the world to a new era of instant communication. An era where online interactions could replace a lot of offline actions. Technology can create a mediated environment in which participants can communicate (one-to-one, one-to-many, and many-to-many) both synchronously and asynchronously and participate in reciprocal message exchanges. Currently, social networks are attracting similar academic attention to that of the internet after its mainstream implementation into public life. Websites and platforms are seen as the forefront of a new political change. There is a significant backdrop of previous methodologies employed to research the effects of social networks. New approaches are being developed to be able to adapt to the growth of social networks and the invention of new platforms. Golden Dawn was the first openly neo-Nazi party post World War II to win seats in the parliament of a European country. Its racist rhetoric and violent tactics on social networks were rewarded by their supporters, who in the face of Golden Dawn’s leaders saw a ‘new dawn’ in Greek politics. Mainstream media banned its leaders and members of the party indefinitely after Ilias Kasidiaris attacked Liana Kanelli, a member of the Greek Communist Party, on live television. This media ban was seen as a treasonous move by a significant percentage of voters, who believed that the system was desperately trying to censor Golden Dawn to favor mainstream parties. The shocking attack on live television received international coverage and while European countries were condemning this newly emerged neo-Nazi rhetoric, almost 7 percent of the Greek population rewarded Golden Dawn with 18 seats in the Greek parliament. Many seem to think that Golden Dawn mobilised its voters online and this approach played a significant role in spreading their message and appealing to wider audiences. No strict online censorship existed back in 2012 and although Golden Dawn was openly used neo-Nazi symbolism, it was allowed to use social networks without serious restrictions until 2017. This paper used qualitative methods to investigate Golden Dawn’s rise in social networks from 2012 to 2019. The focus of the content analysis was set on three social networking platforms: Facebook, Twitter and YouTube, while the existence of Golden Dawn’s website, which was used as a news sharing hub, was also taken into account. The content analysis included text and visual analyses that sampled content from their social networking pages to translate their political messaging through an ideological lens focused on extreme-right populism. The absence of hate speech regulations on social network platforms in 2012 allowed the free expression of those heavily ultranationalist and populist views, as they were employed by Golden Dawn in the Greek political scene. On YouTube, Facebook and Twitter, the influence of their rhetoric was particularly strong. Official channels and MPs profiles were investigated to explore the messaging in-depth and understand its ideological elements.

Keywords: populism, far-right, social media, Greece, golden dawn

Procedia PDF Downloads 135

Authors: Yanqun Li, Hong Geng

Abstract:

The rapid urbanization process taking big cities as the main body leads to the unequal configuration of urban and rural areas in the aspects of land supply, industrial division of labor, service supply and space allocation, and induces the shrinking characterization of service energy, industrial system and population vitality in small towns. As an important spatial unit in the spectrum of urbanization that serves, connects and couples urban and rural areas, the shrinking phenomenon faced by small towns has an important influence on the healthy development of urbanization. Based on the census of small towns in Wuhan metropolitan area, we have found that the shrinking of small towns is a passive contraction of elastic tension under the squeeze in cities. Once affected by the external forces such as policy regulation, planning guidance, and population return, small towns will achieve expansion and growth. Based on the theory of complex adaptive systems, this paper comprehensively constructs the development index evaluation system of small towns from five aspects of population, economy, space, society and ecology, measures the shrinking level of small towns, further analyzes the shrinking characteristics of small towns, and identifies whether the shrinking is elastic or not. And then this paper measures the resilience ability index of small town contract from the above-mentioned five aspects. Finally, this paper proposes an adaptive mechanism of urban-rural interaction evolution under fine division of labor to response the passive shrinking in small towns of Wuhan. Based on the above, the paper creatively puts forward the planning response measures of the small towns on the aspects of spatial layout, function orientation and service support, which can provide reference for other regions.

Keywords: complex adaptive systems, resiliency shrinking, adaptation mechanism, planning response

Procedia PDF Downloads 102

Authors: M. Monisha, Sudha D. Kamath

Abstract:

We investigate the optical and photoluminescence properties from Dy³+ singly doped and Dy³+ co-doped with Ce³+alumino borosilicate glasses prepared using high temperature melt-quenching technique. The glass composition formula is 25SiO₂-(40-x-y)B2O₃-10Al₂O₃-15NaF-10ZnO-xDy₂O₃ yCe₂O₃ where, x = 0.5 mol% and y = 0, 0.1, and 0.5 mol%. The XRD study reveals the amorphous nature of both singly doped and co-doped glasses. Absorption study on Dy3+ singly doped glass shows nearly twelve absorption peaks arising from the ground level of Dy³+ ions (⁶H₁₅/₂) to various upper levels, and for Dy³+/Ce³+ co-doped glasses, few of the transitions in the visible region are suppressed. The absorption band edge is shifted towards the higher wavelength region on increasing Ce3+concentration. The decrease in indirect energy bandgap and increase in Urbach energy of the prepared glasses is observed due to codoping with Ce3+ ions. The photoluminescence studies on singly doped glass under 350 nm excitation showed three peaks at the blue (482 nm), yellow (575 nm), and red (663 nm) region. For codoped glasses, the emission peak at 403 nm is raised due to the 4d to 5f transition of Ce3+ ions. Lifetime values (ms) of co-doped glass is found to be higher than singly doped glass. Under 350 nm excitation, CIE coordinates of the co-doped glasses moved towards the bright white light region. The correlated color temperature (CCT) values were obtained in the range 4500 – 4700 K. Thus, the prepared glasses can be used for photonics device applications.

Keywords: absorption spectra, borosilicate glasses, Ce³+, Dy³+, photoluminescence

Procedia PDF Downloads 136

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 28

Authors: Pavel Kotin, Sergey Dotofeev, Daniil Kozlov, Alexey Garshev

Abstract:

We propose the investigation of CdSe quantum dots which were synthesized in the presence of silver halides. To understand a process of nanoparticle formation in more detail, we varied the silver halide amount in the synthesis and proposed a sampling during colloidal growth. The attempts were focused on the investigation of shape, structure and optical properties of nanoparticles. We used the colloidal method of synthesis. Cadmium oleate, tri-n-octylphosphine selenide (TOPSe) and AgHal in TOP were precursors of cadmium, selenium and silver halides correspondingly. The molar Ag/Cd ratio in synthesis was varied from 1/16 to 1/1. The sampling was basically realized in 20 sec, 5 min, and 30 min after the beginning of quantum dots nucleation. To investigate nanoparticles we used transmission electron microscopy (including high resolution one), X-ray diffraction, and optical spectroscopy. It was established that silver halides lead to obtaining tetrapods with different leg length and large ellipsoidal nanoparticles possessing an intensive near IR photoluminescence. The change of the amount of silver halide in synthesis and the selection of an optimal growth time allows controlling the shape and the share of tetrapods or ellipsoidal nanoparticles in the product. Our main attempts were focused on a detailed investigation of the quantum dots structure and shape evolution and, finally, on mechanisms of such nanoparticle formation.

Keywords: colloidal quantum dots, shape evolution, silver doping, tetrapods

Procedia PDF Downloads 280

Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

Abstract:

Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

Procedia PDF Downloads 473

Authors: Namrata Mishra

Abstract:

Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.

Keywords: breadth first search, directed graph, health behaviors, social network analysis

Procedia PDF Downloads 461

Authors: F. M. Pisano, M. Ciminello

Abstract:

Aerospace, mechanical, and civil engineering infrastructures can take advantages from damage detection and identification strategies in terms of maintenance cost reduction and operational life improvements, as well for safety scopes. The challenge is to detect so called “barely visible impact damage” (BVID), due to low/medium energy impacts, that can progressively compromise the structure integrity. The occurrence of any local change in material properties, that can degrade the structure performance, is to be monitored using so called Structural Health Monitoring (SHM) systems, in charge of comparing the structure states before and after damage occurs. SHM seeks for any "anomalous" response collected by means of sensor networks and then analyzed using appropriate algorithms. Independently of the specific analysis approach adopted for structural damage detection and localization, textual reports, tables and graphs describing possible outlier coordinates and damage severity are usually provided as artifacts to be elaborated for information extraction about the current health conditions of the structure under investigation. Visual Analytics can support the processing of monitored measurements offering data navigation and exploration tools leveraging the native human capabilities of understanding images faster than texts and tables. Herein, a SHM system enrichment by integration of a Visual Analytics component is investigated. Analytical dashboards have been created by combining worksheets, so that a useful Visual Analytics tool is provided to structural analysts for exploring the structure health conditions examined by a Principal Component Analysis based algorithm.

Keywords: interactive dashboards, optical fibers, structural health monitoring, visual analytics

Procedia PDF Downloads 115

Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

Abstract:

Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

Procedia PDF Downloads 349

Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

Abstract:

Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

Procedia PDF Downloads 464

Authors: Agampodi Promoda Perera, Yong Shin, Mi Kyoung Park

Abstract:

The successful detection of pathogenic bacteria in blood provides important information for early detection, diagnosis and the prevention and treatment of infectious diseases. Silicon microring resonators are refractive-index-based optical biosensors that provide highly sensitive, label-free, real-time multiplexed detection of biomolecules. We demonstrate the technique of using GO (graphene oxide) to enhance the signal output of the silicon microring optical sensor. The activated carboxylic groups in GO molecules bind directly to single stranded DNA with an amino modified 5’ end. This conjugation amplifies the shift in resonant wavelength in a real-time manner. We designed a capture probe for strain Staphylococcus aureus of 21 bp and a longer complementary target sequence of 70 bp. The mismatched target sequence we used was of Streptococcus agalactiae of 70 bp. GO is added after the complementary binding of the probe and target. GO conjugates to the unbound single stranded segment of the target and increase the wavelength shift on the silicon microring resonator. Furthermore, our results show that GO could successfully differentiate between the mismatched DNA sequences from the complementary DNA sequence. Therefore, the proposed concept could effectively enhance sensitivity of pathogen detection sensors.

Keywords: label free biosensor, pathogenic bacteria, graphene oxide, diagnosis

Procedia PDF Downloads 452

Authors: Sanaz Seraj, Shohre Rouhani

Abstract:

Recently, graphene has gained much attention because of its unique optical, mechanical, electrical, and thermal properties. Graphene has been used as a key material in the technological applications in various areas such as sensors, drug delivery, super capacitors, transparent conductor, and solar cell. It has a superior quenching efficiency for various fluorophores. Based on these unique properties, the optical sensors with graphene materials as the energy acceptors have demonstrated great success in recent years. During quenching, the emission of a fluorophore is perturbed by a quencher which can be a substrate or biomolecule, and due to this phenomenon, fluorophore-quencher has been used for selective detection of target molecules. Among fluorescence dyes, 1,8-naphthalimide is well known for its typical intramolecular charge transfer (ICT) and photo-induced charge transfer (PET) fluorophore, strong absorption and emission in the visible region, high photo stability, and large Stokes shift. Derivatives of 1,8-naphthalimides have found applications in some areas, especially fluorescence sensors. Herein, the fluorescence quenching of graphene oxide has been carried out on a naphthalimide dye as a fluorescent probe model. The quenching ability of graphene oxide on naphthalimide dye was studied by UV-VIS and fluorescence spectroscopy. This study showed that graphene is an efficient quencher for fluorescent dyes. Therefore, it can be used as a suitable candidate sensing platform. To the best of our knowledge, studies on the quenching and absorption of naphthalimide dyes by graphene oxide are rare.

Keywords: fluorescence, graphene oxide, naphthalimide dye, quenching

Procedia PDF Downloads 578

Authors: Tugce Talay, Kadir Erkan

Abstract:

In this study, the necessary steps for the design of axial flow permanent magnet motors are shown. The design and analysis of the engine were carried out based on ANSYS Maxwell program. The design parameters of the ANSYS Maxwell program and the artificial neural network system were established in MATLAB and the most efficient design parameters were found with the trained neural network. The results of the Maxwell program and the results of the artificial neural networks are compared and optimal working design parameters are found. The most efficient design parameters were submitted to the ANSYS Maxwell 3D design and the cogging torque was examined and design studies were carried out to reduce the cogging torque.

Keywords: AFPM, ANSYS Maxwell, cogging torque, design optimisation, efficiency, NNTOOL

Procedia PDF Downloads 206

Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

Procedia PDF Downloads 247

Authors: Zeina Merabi, Arij Dao

Abstract:

The temporal order of information processing in the brain is an important code in many acoustic signals, including speech, music, and animal vocalizations. Despite its significance, surprisingly little is known about its underlying cellular mechanisms and network manifestations. In the songbird telencephalic nucleus HVC, a subset of neurons shows temporal combination sensitivity (TCS). These neurons show a high temporal specificity, responding differently to distinct patterns of spectral elements and their combinations. HVC neuron types include basal-ganglia-projecting HVCX, forebrain-projecting HVCRA, and interneurons (HVC¬INT), each exhibiting distinct cellular, electrophysiological and functional properties. In this work, we develop conductance-based neural network models connecting the different classes of HVC neurons via different wiring scenarios, aiming to explore possible neural mechanisms that orchestrate the combination sensitivity property exhibited by HVCX, as well as replicating in vivo firing patterns observed when TCS neurons are presented with various auditory stimuli. The ionic and synaptic currents for each class of neurons that are presented in our networks and are based on pharmacological studies, rendering our networks biologically plausible. We present for the first time several realistic scenarios in which the different types of HVC neurons can interact to produce this behavior. The different networks highlight neural mechanisms that could potentially help to explain some aspects of combination sensitivity, including 1) interplay between inhibitory interneurons’ activity and the post inhibitory firing of the HVCX neurons enabled by T-type Ca2+ and H currents, 2) temporal summation of synaptic inputs at the TCS site of opposing signals that are time-and frequency- dependent, and 3) reciprocal inhibitory and excitatory loops as a potent mechanism to encode information over many milliseconds. The result is a plausible network model characterizing auditory processing in HVC. Our next step is to test the predictions of the model.

Keywords: combination sensitivity, songbirds, neural networks, spatiotemporal integration

Procedia PDF Downloads 52