Search results for: optical burst switching networks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4812

Search results for: optical burst switching networks

3522 Learning from Dendrites: Improving the Point Neuron Model

Authors: Alexander Vandesompele, Joni Dambre

Abstract:

The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

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3521 Optical Characterization and Surface Morphology of SnO2 Thin Films Prepared by Spin Coating Technique

Authors: J. O. Ajayi, S. S. Oluyamo, D. B. Agunbiade

Abstract:

In this work, tin oxide thin films (SnO2) were prepared using the spin coating technique. The effects of precursor concentration on the thin film properties were investigated. Tin oxide was synthesized from anhydrous Tin (II) Chloride (SnCl2) dispersed in Methanol and Acetic acid. The metallic oxide (SnO2) films deposited were characterized using the UV Spectrophotometer and the Scanning Electron Microscope (SEM). From the absorption spectra, absorption increases with decrease in precursor concentration. Absorbance in the VIS region is lower than 0 % at higher concentration. The optical transmission spectrum shows that transmission increases as the concentration of precursor decreases and the maximum transmission in visible region is about 90% for films prepared with 0.2 M. Also, there is increase in the reflectance of thin films as concentration of precursor increases. The films have high transparency (more than 85%) and low reflectance (less than 40%) in the VIS region. Investigation showed that the direct band gap value increased from 3.79eV, to 3.82eV as the precursor concentration decreased from 0.6 M to 0.2 M. Average direct bandgap energy for all the tin oxide films was estimated to be 3.80eV. The effect of precursor concentration was directly observed in crystal outgrowth and surface particle densification. They were found to increase proportionately with higher concentration.

Keywords: anhydrous TIN (II) chloride, densification, NIS- VIS region, spin coating technique

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3520 Prediction of Changes in Optical Quality by Tissue Redness after Pterygium Surgery

Authors: Mohd Radzi Hilmi, Mohd Zulfaezal Che Azemin, Khairidzan Mohd Kamal, Azrin Esmady Ariffin, Mohd Izzuddin Mohd Tamrin, Norfazrina Abdul Gaffur, Tengku Mohd Tengku Sembok

Abstract:

Purpose: The purpose of this study is to predict optical quality changes after pterygium surgery using tissue redness grading. Methods: Sixty-eight primary pterygium participants were selected from patients who visited an ophthalmology clinic. We developed a semi-automated computer program to measure the pterygium fibrovascular redness from digital pterygium images. The outcome of this software is a continuous scale grading of 1 (minimum redness) to 3 (maximum redness). The region of interest (ROI) was selected manually using the software. Reliability was determined by repeat grading of all 68 images and its association with contrast sensitivity function (CSF) and visual acuity (VA) was examined. Results: The mean and standard deviation of redness of the pterygium fibrovascular images was 1.88 ± 0.55. Intra- and inter-grader reliability estimates were high with intraclass correlation ranging from 0.97 to 0.98. The new grading was positively associated with CSF (p<0.01) and VA (p<0.01). The redness grading was able to predict 25% and 23% of the variance in the CSF and the VA respectively. Conclusions: The new grading of pterygium fibrovascular redness can be reliably measured from digital images and show a good correlation with CSF and VA. The redness grading can be used in addition to the existing pterygium grading.

Keywords: contrast sensitivity, pterygium, redness, visual acuity

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3519 Projective Lag Synchronization in Drive-Response Dynamical Networks via Hybrid Feedback Control

Authors: Mohd Salmi Md Noorani, Ghada Al-Mahbashi, Sakhinah Abu Bakar

Abstract:

This paper investigates projective lag synchronization (PLS) behavior in drive response dynamical networks (DRDNs) model with identical nodes. A hybrid feedback control method is designed to achieve the PLS with mismatch and without mismatch terms. The stability of the error dynamics is proven theoretically using the Lyapunov stability theory. Finally, analytical results show that the states of the dynamical network with non-delayed coupling can be asymptotically synchronized onto a desired scaling factor under the designed controller. Moreover, the numerical simulations results demonstrate the validity of the proposed method.

Keywords: drive-response dynamical network, projective lag synchronization, hybrid feedback control, stability theory

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3518 Comparison of Feedforward Back Propagation and Self-Organizing Map for Prediction of Crop Water Stress Index of Rice

Authors: Aschalew Cherie Workneh, K. S. Hari Prasad, Chandra Shekhar Prasad Ojha

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Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops.

Keywords: artificial neural networks; crop water stress index; canopy temperature, prediction capability

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3517 The Overexpression of Horsegram MURLK Improves Regulation of Cell Death and Defense Responses to Microbial Pathogens

Authors: Shikha Masand, Sudesh Kumar Yadav

Abstract:

Certain protein kinases have been shown to be crucial for plant cell signaling pathways associated with plant immune responses. Here we identified a horsegram [Macrotyloma uniflorum (Lam.) Verdc.] malectin-like leucine rich receptor-like protein kinase (RLK) gene MuRLK. The functional MuRLK protein preferentially binds to mannose and N-acetyl glucosamine residues. MuRLK exists in the cytoplasm and also localizes to the plasma membrane of plant cells via its N-terminus. Over-expression of MuRLK in Arabidopsis enhances the basal resistance to infection with Pseudomonas syringae pv. tomato, Alternaria brassicicola and Hyaloperonospora arabidopsidis, are associated with elevated ROS bursts, MAPK activation, thus ultimately leading to hypersensitive cell death. Moreover, salicylic acid-dependent and jasmonic acid-dependent defense responses are also enhanced in the MuRLK-overexpressed plants that lead to HR-induced cell death. Together, these results suggest that MuRLK plays a key role in the regulation of plant cell death, early and late defense responses after the recognition of microbial pathogens.

Keywords: horsegram, Pseudomonas syringae pv. tomato, MuRLK, ROS burst, cell death, plant defense

Procedia PDF Downloads 238
3516 Optimal Simultaneous Sizing and Siting of DGs and Smart Meters Considering Voltage Profile Improvement in Active Distribution Networks

Authors: T. Sattarpour, D. Nazarpour

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This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.

Keywords: active distribution network (ADN), distributed generations (DGs), smart meters (SMs), demand response programs (DRPs), adaptive power factor (APF)

Procedia PDF Downloads 289
3515 Multi Criteria Authentication Method in Cognitive Radio Networks

Authors: Shokoufeh Monjezi Kouchak

Abstract:

Cognitive radio network (CRN) is future network .Without this network wireless devices can’t work appropriately in the next decades. Today, wireless devices use static spectrum access methods and these methods don’t use spectrums optimum so we need use dynamic spectrum access methods to solve shortage spectrum challenge and CR is a great device for DSA but first of all its challenges should be solved .security is one of these challenges .In this paper we provided a survey about CR security. You can see this survey in tables 1 to 7 .After that we proposed a multi criteria authentication method in CRN. Our criteria in this method are: sensing results, following sending data rules, position of secondary users and no talk zone. Finally we compared our method with other authentication methods.

Keywords: authentication, cognitive radio, security, radio networks

Procedia PDF Downloads 375
3514 Predicting Survival in Cancer: How Cox Regression Model Compares to Artifial Neural Networks?

Authors: Dalia Rimawi, Walid Salameh, Amal Al-Omari, Hadeel AbdelKhaleq

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Predication of Survival time of patients with cancer, is a core factor that influences oncologist decisions in different aspects; such as offered treatment plans, patients’ quality of life and medications development. For a long time proportional hazards Cox regression (ph. Cox) was and still the most well-known statistical method to predict survival outcome. But due to the revolution of data sciences; new predication models were employed and proved to be more flexible and provided higher accuracy in that type of studies. Artificial neural network is one of those models that is suitable to handle time to event predication. In this study we aim to compare ph Cox regression with artificial neural network method according to data handling and Accuracy of each model.

Keywords: Cox regression, neural networks, survival, cancer.

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3513 Analysis of Combined Heat Transfer through the Core Materials of VIPs with Various Scattering Properties

Authors: Jaehyug Lee, Tae-Ho Song

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Vacuum insulation panel (VIP) can achieve very low thermal conductivity by evacuating its inner space. Heat transfer in the core materials of highly-evacuated VIP occurs by conduction through the solid structure and radiation through the pore. The effect of various scattering modes in combined conduction-radiation in VIP is investigated through numerical analysis. The discrete ordinates interpolation method (DOIM) incorporated with the commercial code FLUENT® is employed. It is found that backward scattering is more effective in reducing the total heat transfer while isotropic scattering is almost identical with pure absorbing/emitting case of the same optical thickness. For a purely scattering medium, the results agree well with additive solution with diffusion approximation, while a modified term is added in the effect of optical thickness to backward scattering is employed. For other scattering phase functions, it is also confirmed that backwardly scattering phase function gives a lower effective thermal conductivity. Thus, the materials with backward scattering properties, with radiation shields are desirable to lower the thermal conductivity of VIPs.

Keywords: combined conduction and radiation, discrete ordinates interpolation method, scattering phase function, vacuum insulation panel

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3512 The Influence of Hydrogen Addition to Natural Gas Networks on Gas Appliances

Authors: Yitong Xie, Chaokui Qin, Zhiguang Chen, Shuangqian Guo

Abstract:

Injecting hydrogen, a competitive carbon-free energy carrier, into existing natural gas networks has become a promising step toward alleviating global warming. Considering the differences in properties of hydrogen and natural gas, there is very little evidence showing how many degrees of hydrogen admixture can be accepted and how to adjust appliances to adapt to gas constituents' variation. The lack of this type of analysis provides more uncertainty in injecting hydrogen into networks because of the short the basis of burner design and adjustment. First, the properties of methane and hydrogen were compared for a comprehensive analysis of the impact of hydrogen addition to methane. As the main determinant of flame stability, the burning velocity was adopted for hydrogen addition analysis. Burning velocities for hydrogen-enriched natural gas with different hydrogen percentages and equivalence ratios were calculated by the software CHEMKIN. Interchangeability methods, including single index methods, multi indices methods, and diagram methods, were adopted to determine the limit of hydrogen percentage. Cooktops and water heaters were experimentally tested in the laboratory. Flame structures of different hydrogen percentages and equivalence ratios were observed and photographed. Besides, the change in heat efficiency, burner temperature, emission by hydrogen percentage, and equivalence ratio was studied. The experiment methodologies and results in this paper provide an important basis for the introduction of hydrogen into gas pipelines and the adjustment of gas appliances.

Keywords: hydrogen, methane, combustion, appliances, interchangeability

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3511 Optimized Cluster Head Selection Algorithm Based on LEACH Protocol for Wireless Sensor Networks

Authors: Wided Abidi, Tahar Ezzedine

Abstract:

Low-Energy Adaptive Clustering Hierarchy (LEACH) has been considered as one of the effective hierarchical routing algorithms that optimize energy and prolong the lifetime of network. Since the selection of Cluster Head (CH) in LEACH is carried out randomly, in this paper, we propose an approach of electing CH based on LEACH protocol. In other words, we present a formula for calculating the threshold responsible for CH election. In fact, we adopt three principle criteria: the remaining energy of node, the number of neighbors within cluster range and the distance between node and CH. Simulation results show that our proposed approach beats LEACH protocol in regards of prolonging the lifetime of network and saving residual energy.

Keywords: wireless sensors networks, LEACH protocol, cluster head election, energy efficiency

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3510 A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks: Prediction of Influential Factors on Eating Behaviors

Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

Abstract:

Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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3509 FSO Performance under High Solar Irradiation: Case Study Qatar

Authors: Syed Jawad Hussain, Abir Touati, Farid Touati

Abstract:

Free-Space Optics (FSO) is a wireless technology that enables the optical transmission of data though the air. FSO is emerging as a promising alternative or complementary technology to fiber optic and wireless radio-frequency (RF) links due to its high-bandwidth, robustness to EMI, and operation in unregulated spectrum. These systems are envisioned to be an essential part of future generation heterogeneous communication networks. Despite the vibrant advantages of FSO technology and the variety of its applications, its widespread adoption has been hampered by rather disappointing link reliability for long-range links due to atmospheric turbulence-induced fading and sensitivity to detrimental climate conditions. Qatar, with modest cloud coverage, high concentrations of airborne dust and high relative humidity particularly lies in virtually rainless sunny belt with a typical daily average solar radiation exceeding 6 kWh/m2 and 80-90% clear skies throughout the year. The specific objective of this work is to study for the first time in Qatar the effect of solar irradiation on the deliverability of the FSO Link. In order to analyze the transport media, we have ported Embedded Linux kernel on Field Programmable Gate Array (FPGA) and designed a network sniffer application that can run into FPGA. We installed new FSO terminals and configure and align them successively. In the reporting period, we carry out measurement and relate them to weather conditions.

Keywords: free space optics, solar irradiation, field programmable gate array, FSO outage

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3508 Differentially Response of Superoxide Dismutase in Wheat Susceptible and Resistant Cultivars against FHB

Authors: M. Sorahi Nobar, V. Niknam, H. Ebrahimzadeh, H. Soltanloo

Abstract:

Fusarium graminearum is one of the most destructive crop diseases in the world. Infection occurs during the flowering period in warm and humid conditions. It causes reduction in yield. Moreover, harvested grain is often contaminated with mycotoxins and its acetylated derivatives. Fusarium mycotoxines are potent inhibitor of protein synthesis, and thereby presents hazards for both human and animal health. A rapid production of reactive oxygen intermediates, primarily superoxide and hydrogen peroxide at the site of attempted infection considered as key feature underlying successful pathogen recognition. Here, we compared the time course activity of superoxide dismutase (SOD) as a first line of defenses against ROS- induced oxidative burst between FHB- resistant Sumai3 and susceptible Falat at 48, 96 and 144 hours after infection. Our results showed that Sumai3 SOD activity increased with time and reached the highest-level 4 days after infection while in susceptible cultivar Falat, SOD activity decreased during the first 96 h. after infection. Decreased was followed by an increased at 6 days after infection. According to our results rapid induction of SOD activity in resistant cultivar may play an important role in resistance against FHB in wheat.

Keywords: Fusarium graminearum, mycotoxins, resistant cultivar, superoxide dismutase

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3507 Power Allocation Algorithm for Orthogonal Frequency Division Multiplexing Based Cognitive Radio Networks

Authors: Bircan Demiral

Abstract:

Cognitive radio (CR) is the promising technology that addresses the spectrum scarcity problem for future wireless communications. Orthogonal Frequency Division Multiplexing (OFDM) technology provides more power band ratios for cognitive radio networks (CRNs). While CR is a solution to the spectrum scarcity, it also brings up the capacity problem. In this paper, a novel power allocation algorithm that aims at maximizing the sum capacity in the OFDM based cognitive radio networks is proposed. Proposed allocation algorithm is based on the previously developed water-filling algorithm. To reduce the computational complexity calculating in water filling algorithm, proposed algorithm allocates the total power according to each subcarrier. The power allocated to the subcarriers increases sum capacity. To see this increase, Matlab program was used, and the proposed power allocation was compared with average power allocation, water filling and general power allocation algorithms. The water filling algorithm performed worse than the proposed algorithm while it performed better than the other two algorithms. The proposed algorithm is better than other algorithms in terms of capacity increase. In addition the effect of the change in the number of subcarriers on capacity was discussed. Simulation results show that the increase in the number of subcarrier increases the capacity.

Keywords: cognitive radio network, OFDM, power allocation, water filling

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3506 Using AI to Advance Factory Planning: A Case Study to Identify Success Factors of Implementing an AI-Based Demand Planning Solution

Authors: Ulrike Dowie, Ralph Grothmann

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Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices.

Keywords: agile software development, AI project success factors, deep learning, demand forecasting, forecast uncertainty, neural networks, supply chain management

Procedia PDF Downloads 169
3505 Facebook Spam and Spam Filter Using Artificial Neural Networks

Authors: A. Fahim, Mutahira N. Naseem

Abstract:

SPAM is any unwanted electronic message or material in any form posted to many people. As the world is growing as global world, social networking sites play an important role in making world global providing people from different parts of the world a platform to meet and express their views. Among different social networking sites facebook become the leading one. With increase in usage different users start abusive use of facebook by posting or creating ways to post spam. This paper highlights the potential spam types nowadays facebook users faces. This paper also provide the reason how user become victim to spam attack. A methodology is proposed in the end discusses how to handle different types of spam.

Keywords: artificial neural networks, facebook spam, social networking sites, spam filter

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3504 Design and Implementation of Grid-Connected Photovoltaic Inverter

Authors: B. H. Lee

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Nowadays, a grid-connected photovoltaic (PV) inverter is adopted in various places like as home, factory, because grid-connected PV inverter can reduce total power consumption by supplying electricity from PV array. In this paper, design and implementation of a 300 W grid-connected PV inverter are described. It is implemented with TI Piccolo DSP core and operated at 100 kHz switching frequency in order to reduce harmonic contents. The maximum operating input voltage is up to 45 V. The characteristics of the designed system that include maximum power point tracking (MPPT), single operation and battery charging are verified by simulation and experimental results.

Keywords: design, grid-connected, implementation, photovoltaic

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3503 Examination of Contaminations in Fabricated Cadmium Selenide Quantum Dots Using Laser Induced Plasma Spectroscopy

Authors: Walid Tawfik, W. Askam Farooq, Sultan F. Alqhtani

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Quantum dots (QDots) are nanometer-sized crystals, less than 10 nm, comprise a semiconductor or metallic materials and contain from 100 - 100,000 atoms in each crystal. QDots play an important role in many applications; light emitting devices (LEDs), solar cells, drug delivery, and optical computers. In the current research, a fundamental wavelength of Nd:YAG laser was applied to analyse the impurities in homemade cadmium selenide (CdSe) QDots through laser-induced plasma (LIPS) technique. The CdSe QDots were fabricated by using hot-solution decomposition method where a mixture of Cd precursor and trioctylphosphine oxide (TOPO) is prepared at concentrations of TOPO under controlled temperatures 200-350ºC. By applying laser energy of 15 mJ, at frequency 10 Hz, and delay time 500 ns, LIPS spectra of CdSe QDots samples were observed. The qualitative LIPS analysis for CdSe QDs revealed that the sample contains Cd, Te, Se, H, P, Ar, O, Ni, C, Al and He impurities. These observed results gave precise details of the impurities present in the QDs sample. These impurities are important for future work at which controlling the impurity contents in the QDs samples may improve the physical, optical and electrical properties of the QDs used for solar cell application.

Keywords: cadmium selenide, TOPO, LIPS spectroscopy, quantum dots

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3502 Application of Combined Cluster and Discriminant Analysis to Make the Operation of Monitoring Networks More Economical

Authors: Norbert Magyar, Jozsef Kovacs, Peter Tanos, Balazs Trasy, Tamas Garamhegyi, Istvan Gabor Hatvani

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Water is one of the most important common resources, and as a result of urbanization, agriculture, and industry it is becoming more and more exposed to potential pollutants. The prevention of the deterioration of water quality is a crucial role for environmental scientist. To achieve this aim, the operation of monitoring networks is necessary. In general, these networks have to meet many important requirements, such as representativeness and cost efficiency. However, existing monitoring networks often include sampling sites which are unnecessary. With the elimination of these sites the monitoring network can be optimized, and it can operate more economically. The aim of this study is to illustrate the applicability of the CCDA (Combined Cluster and Discriminant Analysis) to the field of water quality monitoring and optimize the monitoring networks of a river (the Danube), a wetland-lake system (Kis-Balaton & Lake Balaton), and two surface-subsurface water systems on the watershed of Lake Neusiedl/Lake Fertő and on the Szigetköz area over a period of approximately two decades. CCDA combines two multivariate data analysis methods: hierarchical cluster analysis and linear discriminant analysis. Its goal is to determine homogeneous groups of observations, in our case sampling sites, by comparing the goodness of preconceived classifications obtained from hierarchical cluster analysis with random classifications. The main idea behind CCDA is that if the ratio of correctly classified cases for a grouping is higher than at least 95% of the ratios for the random classifications, then at the level of significance (α=0.05) the given sampling sites don’t form a homogeneous group. Due to the fact that the sampling on the Lake Neusiedl/Lake Fertő was conducted at the same time at all sampling sites, it was possible to visualize the differences between the sampling sites belonging to the same or different groups on scatterplots. Based on the results, the monitoring network of the Danube yields redundant information over certain sections, so that of 12 sampling sites, 3 could be eliminated without loss of information. In the case of the wetland (Kis-Balaton) one pair of sampling sites out of 12, and in the case of Lake Balaton, 5 out of 10 could be discarded. For the groundwater system of the catchment area of Lake Neusiedl/Lake Fertő all 50 monitoring wells are necessary, there is no redundant information in the system. The number of the sampling sites on the Lake Neusiedl/Lake Fertő can decrease to approximately the half of the original number of the sites. Furthermore, neighbouring sampling sites were compared pairwise using CCDA and the results were plotted on diagrams or isoline maps showing the location of the greatest differences. These results can help researchers decide where to place new sampling sites. The application of CCDA proved to be a useful tool in the optimization of the monitoring networks regarding different types of water bodies. Based on the results obtained, the monitoring networks can be operated more economically.

Keywords: combined cluster and discriminant analysis, cost efficiency, monitoring network optimization, water quality

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3501 Musical Instrument Recognition in Polyphonic Audio Through Convolutional Neural Networks and Spectrograms

Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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3500 Automated Multisensory Data Collection System for Continuous Monitoring of Refrigerating Appliances Recycling Plants

Authors: Georgii Emelianov, Mikhail Polikarpov, Fabian Hübner, Jochen Deuse, Jochen Schiemann

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Recycling refrigerating appliances plays a major role in protecting the Earth's atmosphere from ozone depletion and emissions of greenhouse gases. The performance of refrigerator recycling plants in terms of material retention is the subject of strict environmental certifications and is reviewed periodically through specialized audits. The continuous collection of Refrigerator data required for the input-output analysis is still mostly manual, error-prone, and not digitalized. In this paper, we propose an automated data collection system for recycling plants in order to deduce expected material contents in individual end-of-life refrigerating appliances. The system utilizes laser scanner measurements and optical data to extract attributes of individual refrigerators by applying transfer learning with pre-trained vision models and optical character recognition. Based on Recognized features, the system automatically provides material categories and target values of contained material masses, especially foaming and cooling agents. The presented data collection system paves the way for continuous performance monitoring and efficient control of refrigerator recycling plants.

Keywords: automation, data collection, performance monitoring, recycling, refrigerators

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3499 Crushing Analysis of Foam-Filled Thin-Walled Aluminum Profiles Subjected to Axial Loading

Authors: Michał Rogala, Jakub Gajewski

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As the automotive industry develops, passive safety is becoming an increasingly important aspect when designing motor vehicles. A commonly used solution is energy absorption by thin-walled construction. One such structure is a closed thin-walled profile fixed to the vehicle stringers. The article presents numerical tests of conical thin-walled profiles filled with aluminum foam. The columns were loaded axially with constant energy. On the basis of the results obtained, efficiency indicators were calculated. The efficiency of the foam filling was evaluated. Artificial neural networks were used for data analysis. The application of regression analysis was used as a tool to study the relationship between the quantities characteristic of the dynamic crush.

Keywords: aluminium foam, crashworthiness, neural networks, thin-walled structure

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3498 Optimization Method of Dispersed Generation in Electrical Distribution Systems

Authors: Mahmoud Samkan

Abstract:

Dispersed Generation (DG) is a promising solution to many power system problems such as voltage regulation and power loss. This paper proposes a heuristic two-step method to optimize the location and size of DG for reducing active power losses and, therefore, improve the voltage profile in radial distribution networks. In addition to a DG placed at the system load gravity center, this method consists in assigning a DG to each lateral of the network. After having determined the central DG placement, the location and size of each lateral DG are predetermined in the first step. The results are then refined in the second step. This method is tested for 33-bus system for 100% DG penetration. The results obtained are compared with those of other methods found in the literature.

Keywords: optimal location, optimal size, dispersed generation (DG), radial distribution networks, reducing losses

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3497 Seasonal Variation in Aerosols Characteristics over Ahmedabad

Authors: Devansh Desai, Chamandeep Kaur, Nirmal Kullu, George Christopher

Abstract:

Study of aerosols has become very important tool in assuming the climatic changes over a region.Spectral and temporal variability’s in aerosol optical depth(AOD) and size distribution are investigated using ground base measurements over Ahmedabad during the months of January(2013) to may (2013). Angstrom coefficient (ἁ) was found to be higher in winter season (January to march) indicating the dominance of fine mode aerosol concentration over Ahmedabad, and the Angstrom coefficient (ἁ) was found to be lower indicating the dominance of coarse mode aerosol concentration over Ahmedabad. The different values of alpha are observed when calculated over different wavelength ranges indicating bimodal aerosol size distribution. Discrimination of aerosol size during different seasons is made using the coefficient of polynomial fit (ἁ1 and ἁ2) which shows the presence of changing dominant aerosol types as a function of season over Ahmedabad. The ἁ2- ἁ1 value is used to get the confirmation on the dominant aerosol mode over Ahmedabad in both seasons. During pre-monsoon about 90% of AOD spectra is dominated by coarse mode aerosols and during winter about 60% of AOD spectra is dominated by fine mode aerosols. This characterization of aerosols is important in assessing the response of different aerosols type in radiative forcing and over climate of Ahmedabad.

Keywords: radiative forcing, aerosol optical depth, fine mode, coarse mode

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3496 MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-Independent and Identically Distributed Distribution

Authors: Akash Amalan, Rui Wang, Yanqi Qiao, Emmanouil Panaousis, Kaitai Liang

Abstract:

Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled General Adversarial Networks (GANs) to benefit from the rich distributed training data while preserving privacy. However, in a non-IID setting, current federated GAN architectures are unstable, struggling to learn the distinct features, and vulnerable to mode collapse. In this paper, we propose an architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse, and instability for non-IID datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e., high inception score) on average over 20 clients compared to baseline FLGAN.

Keywords: federated learning, generative adversarial network, inference attack, non-IID data distribution

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3495 Hall Coefficient in the Presence of Strong Electromagnetic Waves Caused by Confined Electrons and Phonons in a Rectangular Quantum Wire

Authors: Nguyen Quang Bau, Nguyen Thu Huong, Dang Thi Thanh Thuy

Abstract:

The analytic expression for the Hall Coefficient (HC) caused by the confined electrons in the presence of a strong electromagnetic wave (EMW) including the effect of phonon confinement in rectangular quantum wires (RQWs) is calculated by using the quantum kinetic equation for electrons in the case of electron - optical phonon scattering. It is because the expression of the HC for the confined phonon case contains indexes m, m’ which are specific to the phonon confinement. The expression in a RQW is different from that for the case of unconfined phonons in a RQW or in 2D. The results are numerically calculated and discussed for a GaAs/GaAsAl RQW. The numerical results show that HC in a RQW can have both negative and positive values. This is different from the case of the absence of EMW and the case presence of EMW including the effect of phonon unconfinement in a RQW. These results are also compared with those in the case of unconfined phonons in a RQW and confined phonons in a quantum well. The conductivity in the case of confined phonon has more resonance peaks compared with that in case of unconfined phonons in a RQW. This new property is the same in quantum well. All results are compared with the case of unconfined phonons to see differences.

Keywords: Hall coefficient, rectangular quantum wires, electron-optical phonon interaction, quantum kinetic equation, confined phonons

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3494 Examining the Importance of the Structure Based on Grid Computing Service and Virtual Organizations

Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

Abstract:

Vast changes and developments achieved in information technology field in recent decades have made the review of different issues such as organizational structures unavoidable. Applying informative technologies such as internet and also vast use of computer and related networks have led to new organizational formations with a nature completely different from the traditional, great and bureaucratic ones; some common specifications of such organizations are transfer of the affairs out of the organization, benefiting from informative and communicative networks and centered-science workers. Such communicative necessities have led to network sciences development including grid computing. First, the grid computing was only to relate some sites for short – time and use their sources simultaneously, but now it has gone beyond such idea. In this article, the grid computing technology was examined, and at the same time, virtual organization concept was discussed.

Keywords: grid computing, virtual organizations, software engineering, organization

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3493 Smart Web Services in the Web of Things

Authors: Sekkal Nawel

Abstract:

The Web of Things (WoT), integration of smart technologies from the Internet or network to Web architecture or application, is becoming more complex, larger, and dynamic. The WoT is associated with various elements such as sensors, devices, networks, protocols, data, functionalities, and architectures to perform services for stakeholders. These services operate in the context of the interaction of stakeholders and the WoT elements. Such context is becoming a key information source from which data are of various nature and uncertain, thus leading to complex situations. In this paper, we take interest in the development of intelligent Web services. The key ingredients of this “intelligent” notion are the context diversity, the necessity of a semantic representation to manage complex situations and the capacity to reason with uncertain data. In this perspective, we introduce a multi-layered architecture based on a generic intelligent Web service model dealing with various contexts, which proactively predict future situations and reactively respond to real-time situations in order to support decision-making. For semantic context data representation, we use PR-OWL, which is a probabilistic ontology based on Multi-Entity Bayesian Networks (MEBN). PR-OWL is flexible enough to represent complex, dynamic, and uncertain contexts, the key requirements of the development for the intelligent Web services. A case study was carried out using the proposed architecture for intelligent plant watering to show the role of proactive and reactive contextual reasoning in terms of WoT.

Keywords: smart web service, the web of things, context reasoning, proactive, reactive, multi-entity bayesian networks, PR-OWL

Procedia PDF Downloads 51