Search results for: cross layer network topology

Authors: G. Sivakumar, T. Pratyusha, D. Gupta, W. Shen

Abstract:

To get ohmic contact between the cathode and organic semiconductor, transport layers are introduced between the active layer and the electrodes. Generally zinc oxide or titanium dioxide are used as electron transport layer. When electron transport layer is doped with lithium, the resultant film exhibited superior electronic properties, which enables faster electron transport. Doping is accomplished by heat treatment of films with Lithium salts. Li-doped films. We fabricated organic solar cell using PTB7(poly(3-hexylthiopene-2,5- diyl):PCBM(phenyl-C61-butyric acid methyl ester) and found that the solar cells prepared using Li doped films had better performance in terms of efficiency when compared to the undoped transport layers.

Keywords: electron transport layer, higher efficiency, lithium doping, ohmic contact

Procedia PDF Downloads 485

Authors: Keigo Ide, Toru Maruyama, Michihiro Ito, Hiroyuki Fujimura, Yoshikatu Nakano, Shoichiro Suda, Sachiyo Aburatani, Haruko Takeyama

Abstract:

Understanding environmental system is one of the important tasks. In recent years, conservation of coral environments has been focused for biodiversity issues. The damage of coral reef under environmental impacts has been observed worldwide. However, the casual relationship between damage of coral and environmental impacts has not been clearly understood. On the other hand, structure/diversity of marine bacterial community may be relatively robust under the certain strength of environmental impact. To evaluate the coral environment conditions, it is necessary to investigate relationship between marine bacterial composition in coral reef and environmental factors. In this study, the Time Scale Network Analysis was developed and applied to analyze the marine environmental data for investigating the relationship among coral, bacterial community compositions and environmental factors. Seawater samples were collected fifteen times from November 2014 to May 2016 at two locations, Ishikawabaru and South of Sesoko in Sesoko Island, Okinawa. The physicochemical factors such as temperature, photosynthetic active radiation, dissolved oxygen, turbidity, pH, salinity, chlorophyll, dissolved organic matter and depth were measured at the coral reef area. Metagenome and metatranscriptome in seawater of coral reef were analyzed as the biological factors. Metagenome data was used to clarify marine bacterial community composition. In addition, functional gene composition was estimated from metatranscriptome. For speculating the relationships between physicochemical and biological factors, cross-correlation analysis was applied to time scale data. Even though cross-correlation coefficients usually include the time precedence information, it also included indirect interactions between the variables. To elucidate the direct regulations between both factors, partial correlation coefficients were combined with cross correlation. This analysis was performed against all parameters such as the bacterial composition, the functional gene composition and the physicochemical factors. As the results, time scale network analysis revealed the direct regulation of seawater temperature by photosynthetic active radiation. In addition, concentration of dissolved oxygen regulated the value of chlorophyll. Some reasonable regulatory relationships between environmental factors indicate some part of mechanisms in coral reef area.

Keywords: coral environment, marine microbiology, network analysis, omics data analysis

Procedia PDF Downloads 237

Authors: Mehrdad Nouri Khajavi, Gollamhassan Payganeh, Mohsen Fallah Tafti

Abstract:

Fault diagnosis is an important aspect of maintaining rotating machinery health and increasing productivity. Many researches has been done in this regards. Many faults such as unbalance, misalignment, looseness, bearing faults, etc. have been considered and diagnosed with different techniques. Most of the researches in fault diagnosis of rotating machinery deal with single fault. Where as in reality faults usually occur simultaneously and it is, therefore, necessary to recognize them at the same time. In this research, two of the most common faults namely unbalance and misalignment have been considered simultaneously with different intensity and then identified and classified with the use of Multi-Layer Perception Neural Network (MLPNN). Processed Vibration signals are used as the input to the MLPNN, and the class of mixed unbalancy, and misalignment is the output of the NN.

Keywords: unbalance, parallel misalignment, combined faults, vibration signals

Procedia PDF Downloads 335

Authors: Ryu Wonjin, Choi Hyeok, Park Joonhong

Abstract:

Overlay welding for improving surface properties is a method of the surface treatments which improve surface properties of material by welding materials of alloy having corrosion resistance on the basic material surface. Overlay welding affects contents of chemical components and weld hardness from different parts by dilution of the lamination layer thickness, and it determines surface properties. Therefore, overlay welding has to take into account thickness of the lamination layers with the process. As a result in this study examined contents of Fe, weldability of the base metal and monel materials, hardness and surface flatness from different parts according to each the lamination layer parameters by overlay welding monel materials with corrosion resources to the base material of carbon steel. Through this, evaluated effect by the lamination layer parameters of welding and presented decision methods of the lamination layer parameters of the overlay welding by the purpose of use.

Keywords: clad pipe, lamination layer parameters, monel, overlay welding

Procedia PDF Downloads 245

Authors: Yaeji Lim, Hee-Seok Oh

Abstract:

The coherence analysis measures the linear time-invariant relationship between two data sets and has been studied various fields such as signal processing, engineering, and medical science. However classical coherence analysis tends to be sensitive to outliers and focuses only on mean relationship. In this paper, we generalized cross periodogram to quantile cross periodogram and provide richer inter-relationship between two data sets. This is a general version of Laplace cross periodogram. We prove its asymptotic distribution under the long range process and compare them with ordinary coherence through numerical examples. We also present real data example to confirm the usefulness of quantile coherence analysis.

Keywords: coherence, cross periodogram, spectrum, quantile

Procedia PDF Downloads 369

Authors: Yagya Raj Pandeya, Joonwhoan Lee

Abstract:

The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

Procedia PDF Downloads 194

Authors: Hazem J. Smadi

Abstract:

Performance of a supply chain is directly related to a distribution network that entails the location of storing materials or products and how products are delivered to the end customer through different stages in the supply chain. This study analyses the current distribution network used for delivering gas cylinders to end customer in Jordan. Evaluation of current distribution has been conducted across customer service components. A modification on the current distribution network in terms of central warehousing in each city in the country improves the response time and customer experience. 

Keywords: distribution network, gas cylinder, Jordan, supply chain

Procedia PDF Downloads 441

Authors: Ghazala Anjum, Farooq Hussain Bhat, Ravi Kumar

Abstract:

Multiferroic materials are vital for new application and memory devices, not only because of the presence of multiple types of domains but also as a result of cross correlation between coexisting forms of magnetic and electrical orders. In spite of wide studies done on multiferroic bulk ceramic materials their realization in thin film form is yet limited due to some crucial problems. During the last few years, special attention has been devoted to synthesis of thin films like of BiFeO₃. As they allow direct integration of the material into the device technology. Therefore owing to the process of exploration of new multiferroic thin films, preparation, and characterization of La₀.₈Bi₀.₂Fe₀.₇Mn₀.₃O₃ (LBFMO3) thin film on LaAlO₃ (LAO) substrate with LaNiO₃ (LNO) being the buffer layer has been done. The fact that all the electrical and magnetic properties are closely related to the electronic structure makes it inevitable to study the electronic structure of system under study. Without the knowledge of this, one may never be sure about the mechanism responsible for different properties exhibited by the thin film. Literature review reveals that studies on change in atomic and the hybridization state in multiferroic samples are still insufficient except few. The technique of x-ray absorption (XAS) has made great strides towards the goal of providing such information. It turns out to be a unique signature to a given material. In this milieu, it is time honoured to have the electronic structure study of the elements present in the LBFMO₃ multiferroic thin film on LAO substrate with buffer layer of LNO synthesized by RF sputtering technique. We report the electronic structure studies of well characterized LBFMO3 multiferroic thin film on LAO substrate with LNO as buffer layer using near-edge X-ray absorption fine structure (NEXAFS). Present exploration has been performed to find out the valence state and crystal field symmetry of ions present in the system. NEXAFS data of O K- edge spectra reveals a slight shift in peak position along with growth in intensities of low energy feature. Studies of Mn L₃,₂- edge spectra indicates the presence of Mn³⁺/Mn⁴⁺ network apart from very small contribution from Mn²⁺ ions in the system that substantiates the magnetic properties exhibited by the thin film. Fe L₃,₂- edge spectra along with spectra of reference compound reveals that Fe ions are present in +3 state. Electronic structure and valence state are found to be in accordance with the magnetic properties exhibited by LBFMO/LNO/LAO thin film.

Keywords: magnetic, multiferroic, NEXAFS, x-ray absorption fine structure, XMCD, x-ray magnetic circular dichroism

Procedia PDF Downloads 137

Authors: Riznaldi Akbar

Abstract:

In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

Procedia PDF Downloads 422

Authors: Rui Chen, Shu-min Zhou, Xiong-jie Zhang, Ren-bo Wang, Fan Huang, Bin Tang

Abstract:

During the temperature changes in reactor core, the nuclide cross section in reactor can vary with temperature, which eventually causes the changes of reactivity. To simulate the interaction between incident neutron and various materials at different temperatures on the nose, it is necessary to generate all the relevant reaction temperature-dependent cross section. Traditionally, the real time cross section generation method is used to avoid storing huge data but contains severe problems of low efficiency and adaptability for narrow energy region. Focused on the research on multi-temperature cross sections generation in real time during in neutron transport, this paper investigated the on-the-fly cross section generation method for resolved resonance region, thermal region and unresolved resonance region, and proposed the real time multi-temperature cross sections generation method based on double-exponential formula for resolved resonance region, as well as the Neville interpolation for thermal and unresolved resonance region. To prove the correctness and validity of multi-temperature cross sections generation based on wide energy region of incident neutron, the proposed method was applied in critical safety benchmark tests, which showed the capability for application in reactor multi-physical coupling simulation.

Keywords: cross section, neutron transport, numerical simulation, on-the-fly

Procedia PDF Downloads 178

Authors: Jenny Radeva, Anke-Gundula Roth, Christian Goebbert, Robert Niestroj-Pahl, Lars Daehne, Axel Wolfram, Juergen Wiese

Abstract:

Ceramic membranes for water purification combine excellent stability with long-life characteristics and high chemical resistance. Layer-by-Layer coating is a well-known technique for customization and optimization of filtration properties of membranes but is mostly used on polymeric membranes. Ceramic membranes comprising a metal oxide filtration layer of Al2O3 or TiO2 are charged and therefore highly suitable for polyelectrolyte adsorption. The high stability of the membrane support allows efficient backwash and chemical cleaning of the membrane. The presented study reports metal oxide/organic composite membrane with an increased rejection of bivalent salts like MgSO4 and the organic micropollutant Diclofenac. A self-build apparatus was used for applying the polyelectrolyte multilayers on the ceramic membrane. The device controls the flow and timing of the polyelectrolytes and washing solutions. As support for the Layer-by-Layer coat, ceramic mono-channel membranes were used with an inner capillary of 8 mm diameter, which is connected to the coating device. The inner wall of the capillary is coated subsequently with polycat- and anions. The filtration experiments were performed with a feed solution of MgSO4 and Diclofenac. The salt content of the permeate was detected conductometrically and Diclofenac was measured with UV-Adsorption. The concluded results show retention values of magnesium sulfate of 70% and diclofenac retention of 60%. Further experimental research studied various parameters of the composite membrane-like Molecular Weight Cut Off and pore size, Zeta potential and its mechanical and chemical robustness.

Keywords: water purification, polyelectrolytes, membrane modification, layer-by-layer coating, ceramic membranes

Procedia PDF Downloads 228

Authors: Mohamed Abd Elfattah Ibraheem Elghrbawy

Abstract:

Icons are a significant group of cultural heritage objects that deserve to be maintained and conserved, as these ions are performed according to religious standards and norms. The ideal structure of icons is five strata, the lower layer is a wood plate, and the upper layer is the varnish layer that is exposed to photo-oxidation, that is turned into a fragile yellow layer. In addition, the components of the icons are important in dating these ions, so X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD), and Scanning Electron Microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) patterns were used. SEM-EDX pattern revealed that the red pigment was vermillion (HgS), that was used in the late period, with a slight difference from the synthesized pigment. Pigments were subjected to chromatic alteration due to different agents, such as microbial agents and pollutants, in particular SO₂, whereas the pigment-based pigments are more sensitive. Moreover, cleaning, varnish removal, and retouching are important processes in the conservation of icons.

Keywords: conservation, cultural heritage, Egyptian icon, pigments

Procedia PDF Downloads 56

Authors: Ava Salmanpour, Hanieh Saeedi, Payam Rouhi, Elahe Hamzeil, Shima Alimohammadi, Siamak Hossein Khalaj, Mohammad Asadian

Abstract:

Software-defined network (SDN) is a network architecture designed to control network using software application in a central manner. This ability enables remote control of the whole network regardless of the network technology. In fact, in this architecture network intelligence is separated from physical infrastructure, it means that required network components can be implemented virtually using software applications. Today, power networks are characterized by a high range of complexity with a large number of intelligent devices, processing both huge amounts of data and important information. Therefore, reliable and secure communication networks are required. SDNs are the best choice to meet this issue. In this paper, SDN networks capabilities and characteristics will be reviewed and different basic controllers will be compared. The importance of using SDNs to escalate efficiency and reliability in utility power networks is going to be discussed and the comparison between the SDN-based power networks and traditional networks will be explained.

Keywords: software-defined network, SDNs, utility network, open flow, communication, gas and electricity, controller

Procedia PDF Downloads 88

Authors: Adeyemi Kamil Hamzah

Abstract:

The contiguity of nations in international system suggests that world is a composite of socio-economic unit with people exploring and exploiting the potentials in the world via migrations. Thus, cross border migration has made positive contributions to social and economic development of individuals and nations by increasing the household incomes of the host countries. However, the cross border migrations in West Africa are becoming part of a dynamic and unstable world migration system. This is due to the nature and consequences of trans-border crimes in West Africa, with both short and long term effects on the socio-economic viability of developing countries like West African States. The paper identified that migration influenced cross-border crimes as well as the high spate of insurgencies in the sub-region. Furthermore, the consequential effect of a global village has imbalanced population flows, making some countries host and parasites to others. Also, stern and deft cross-border rules and regulations, as well as territorial security and protections, ameliorate cross border crimes and migration in West African sub-regions. Therefore, the study concluded that cross border migration is the linchpin of all kinds of criminal activities which affect the security of states in the sub-region.

Keywords: cross-border migration, border crimes, security, West Africa, development, globalisation

Procedia PDF Downloads 205

Authors: A. Sreedevi, G. Anantaramu

Abstract:

The present space research organizations are striving towards the development of lighter, smaller, more efficient, low cost, and highly reliable power supply. Switch mode power supplies (SMPS) overcome the demerits of linear power supplies such as low efficiency, difficulties in thermal management, and in boosting the output voltage. Space applications require a constant DC voltage to supply its load. As the load varies, the battery terminal voltage tends to vary accordingly. To avoid this variation in the load terminal voltage, a DC-DC regulator is required. The conventional regulator for space applications is isolated boost topology. The proposed topology uses an interleaved push-pull converter with a current doubler secondary to reduce the EMI issues and increase efficiency. The proposed topology uses a floating technique where the converter derives power from the battery and generates only the voltage that is required to fill the gap between the bus and the battery voltage. The direct voltage sense and current loop provide tight regulation of output and better stability. Converter is designed with 50 kHz switching frequency using UC 1825 PWM controller employing both voltage and peak current mode control. Experimental tests have been carried out on the converter under different input and load conditions to validate the design. The experimental results showed that the efficiency was greater than 91%. Stability analysis is done using venable stability analyzer.

Keywords: push pull converter, current doubler, converter, PWM control

Procedia PDF Downloads 84

Authors: Tebogo Emma Makaba, Barnabas Ndlovu Gatsheni

Abstract:

The cities of Johannesburg and Pretoria both located in the Gauteng province are separated by a distance of 58 km. The traffic queues on the Ben Schoeman freeway which connects these two cities can stretch for almost 1.5 km. Vehicle traffic congestion impacts negatively on the business and the commuter’s quality of life. The goal of this paper is to identify variables that influence the flow of traffic and to design a vehicle traffic prediction model, which will predict the traffic flow pattern in advance. The model will unable motorist to be able to make appropriate travel decisions ahead of time. The data used was collected by Mikro’s Traffic Monitoring (MTM). Multi-Layer perceptron (MLP) was used individually to construct the model and the MLP was also combined with Bagging ensemble method to training the data. The cross—validation method was used for evaluating the models. The results obtained from the techniques were compared using predictive and prediction costs. The cost was computed using combination of the loss matrix and the confusion matrix. The predicted models designed shows that the status of the traffic flow on the freeway can be predicted using the following parameters travel time, average speed, traffic volume and day of month. The implications of this work is that commuters will be able to spend less time travelling on the route and spend time with their families. The logistics industry will save more than twice what they are currently spending.

Keywords: bagging ensemble methods, confusion matrix, multi-layer perceptron, vehicle traffic flow

Procedia PDF Downloads 321

Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

Abstract:

Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

Procedia PDF Downloads 359

Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

Abstract:

Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

Procedia PDF Downloads 424

Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

Abstract:

Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

Procedia PDF Downloads 492

Authors: Wei-Song Hung

Abstract:

We utilized pressure, vacuum, and evaporation-assisted self-assembly techniques through which graphene oxide (GO) was deposited on modified polyacrylonitrile (mPAN). The fabricated composite GO/mPAN membranes were applied to dehydrate 1-butanol mixtures by pervaporation. Varying driving forces in the self-assembly techniques induced different GO assembly layer microstructures. XRD results indicated that the GO layer d-spacing varied from 8.3 Å to 11.5 Å. The self-assembly technique with evaporation resulted in a heterogeneous GO layer with loop structures; this layer was shown to be hydrophobic, in contrast to the hydrophilic layer formed from the other two techniques. From the pressure-assisted technique, the composite membrane exhibited exceptional pervaporation performance at 30 C: concentration of water at the permeate side = 99.6 wt% and permeation flux = 2.54 kg m-2 h-1. Moreover, the membrane sustained its operating stability at a high temperature of 70 C: a high water concentration of 99.5 wt% was maintained, and a permeation flux as high as 4.34 kg m-2 h-1 was attained. This excellent separation performance stemmed from the dense, highly ordered laminate structure of GO.

Keywords: graphene oxide, self-assembly, alcohol dehydration, polyacrylonitrile (mPAN)

Procedia PDF Downloads 279

Authors: Jang kyun Cho, Jeong-dong Lee

Abstract:

The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

Procedia PDF Downloads 321

Authors: Meriem Boukais, B. Dennai, A. Ould- Abbas

Abstract:

The efficiency of SiGe solar cells might be improved by a wide-band-gap window layer. In this work we were simulated using the one dimensional simulation program called analysis of microelectronic and photonic structures (AMPS-1D). In the modeling, the thickness of silicon window was varied from 80 to 150 nm. The rest of layer’s thicknesses were kept constant, by varying thickness of window layer the simulated device performance was demonstrate in the form of current-voltage (I-V) characteristics and quantum efficiency (QE).

Keywords: modeling, SiGe, AMPS-1D, quantum efficiency, conversion, efficiency

Procedia PDF Downloads 695

Authors: H. B. Tibar, Z. Y. Jiang

Abstract:

In this study, the influence of rolling process parameters such as the work roll cross angle and work roll shifting value on the strip shape and profile of aluminum have been investigated under dry conditions at a speed ratio of 1.3 using Hille 100 experimental mill. The strip profile was found to improve significantly with increase in work roll cross angle from 0^o to 1^o, with an associated decrease in rolling force. The effect of roll shifting (from 0 to 8mm) was not as significant as the roll cross angle. However, an increase in work roll shifting value achieved a similar decrease in rolling force as that of work roll cross angle. The effect of work roll shifting was also found to be maximum at an optimum roll speed of 0.0986 m/s for the desired thickness. Of all these parameters, the most significant effect of the strip shape profile was observed with variation of work roll cross angle. However, the rolling force can be a significantly reduced by either increasing the the work roll cross angle or work roll shifting.

Keywords: rolling speed ratio, strip shape, work roll cross angle, work roll shifting

Procedia PDF Downloads 390

Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

Abstract:

PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

Procedia PDF Downloads 122

Authors: Jenny Radeva, Anke-Gundula Roth, Christian Goebbert, Robert Niestroj-Pahl, Lars Daehne, Axel Wolfram, Juergen WIese

Abstract:

The presented investigation studies the correlation between the process parameters of Layer-by-Layer (LbL) coatings and properties of the produced hybrid membranes for water treatment. The coating of alumina ceramic support membrane with polyelectrolyte multilayers on top results in hybrid membranes with increased fouling resistant behavior, high retention (up to 90%) of salt ions and various pharmaceuticals, selectivity to various organic molecules as known from LbL coated polyether sulfone membranes and the possibility of pH response control. Chosen polyelectrolytes were added to the support using the LbL-coating process. Parameters like the type of polyelectrolyte, ionic strength, and pH were varied in order to find the most suitable process conditions and to study how they influence the properties of the final product. The applied LbL-films was investigated in respect to its homogeneity and penetration depth. The analysis of the layer buildup was performed using fluorescence labeled polyelectrolyte molecules and Confocal Laser Scanning Microscopy as well as Scanning and Transmission Electron Microscopy. Furthermore, the influence of the coating parameters on the porosity, surface potential, retention, and permeability of the developed hybrid membranes were estimated. In conclusion, a comparison was drawn between the filtration performance of the uncoated alumina ceramic membrane and modified hybrid membranes.

Keywords: water treatment, membranes, ceramic membranes, hybrid membranes, layer-by-layer modification

Procedia PDF Downloads 162

Authors: Bharat Kumar, Deepak Kumar, Vijay Chaudhry

Abstract:

Zirconium alloys are used as core components of nuclear reactors due to their high wear resistance, good corrosion properties, and good mechanical stability at high temperatures. The present work simulates the contact between the calandria tube and the liquid injection shutdown system (LISS) nozzle. The Calandria tube is the outer covering of the pressure tube. Water flows inside the pressure tube through fuel claddings which produces vibration in the pressure tube along with vibration in the calandria tube. Fretting wear takes place at the point of contact between the calandria tube and the LISS nozzle. Fretting tests were performed under different conditions, such as; varying fretting duration (i.e., 1 to 4 hours), varying frequency (i.e., 5 to 6.5 Hz), and varying amplitude (100 to 400 µm). The formation of the oxide layer was observed during the fretting wear test; as a result, the worn product. The worn surfaces were analyzed with scanning electron microscopy (SEM) to analyze the wear mechanism involved in the fretting test, and Energy dispersive x-ray spectroscopy (EDS) and Raman spectroscopy were used to confirm the presence of an oxide layer on the worn surface. The oxide layer becomes more uniform with fretting duration in case of water submerged condition as compared to dry contact condition. The oxide layer is deeply removed at high amplitude due to the change of wear mechanism from adhesion to abrasion, as confirmed by the presence of micro ploughing and micro cutting. Low amplitude fretting favors the formation of the tribo-oxide layer.

Keywords: tribo-oxide layer, wear, mechanically mixed layer, zircaloy

Procedia PDF Downloads 62

Authors: Ma Yuan, Zhang Mengxi, Akbar Javadi, Chen Longqing

Abstract:

The structure of sand-filled embankment with cover layer is treated with tipping clay modified with lime on the outside of the packing, and the geotextile is placed between the stuffing and the clay. The packing is usually river sand, and the improved clay protects the sand core against rainwater erosion. The sand-filled embankment with cover layer has practical problems such as high filling embankment, construction restriction, and steep slope. The reinforcement can be applied to the sand-filled embankment with cover layer to solve the complicated problems such as irregular settlement caused by poor stability of the embankment. At present, the research on the sand-filled embankment with cover layer mainly focuses on the sand properties, construction technology, and slope stability, and there are few studies in the experimental field, the deformation characteristics and stability of reinforced sand-filled embankment need further study. In addition, experimental research is relatively rare when the cyclic load is considered in tests. A subgrade structure of geogrid-reinforced sand-filled embankment with cover layer was proposed. The mechanical characteristics, the deformation properties, reinforced behavior and the ultimate bearing capacity of the embankment structure under cyclic loading were studied. For this structure, the geogrids in the sand and the tipping soil are through the geotextile which is arranged in sections continuously so that the geogrids can cross horizontally. Then, the Unsaturated/saturated Soil Triaxial Test System of Geotechnical Consulting and Testing Systems (GCTS), USA was modified to form the loading device of this test, and strain collector was used to measuring deformation and earth pressure of the embankment. A series of cyclic loading model tests were conducted on the geogrid-reinforced sand-filled embankment with a cover layer under a different number of reinforcement layers, the length of reinforcement and thickness of the cover layer. The settlement of the embankment, the normal cumulative deformation of the slope and the earth pressure were studied under different conditions. Besides cyclic loading model tests, model experiments of embankment subjected cyclic-static loading was carried out to analyze ultimate bearing capacity with different loading. The experiment results showed that the vertical cumulative settlement under long-term cyclic loading increases with the decrease of the number of reinforcement layers, length of the reinforcement arrangement and thickness of the tipping soil. Meanwhile, these three factors also have an influence on the decrease of the normal deformation of the embankment slope. The earth pressure around the loading point is significantly affected by putting geogrid in a model embankment. After cyclic loading, the decline of ultimate bearing capacity of the reinforced embankment can be effectively reduced, which is contrary to the unreinforced embankment.

Keywords: cyclic load; geogrid; reinforcement behavior; cumulative deformation; earth pressure

Procedia PDF Downloads 99

Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

Abstract:

A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

Procedia PDF Downloads 387

Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

Abstract:

As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the Central Processing Unit (CPU), operational (RAM), and permanent (ROM) memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles

Procedia PDF Downloads 84

Authors: Boukais Meriem, B. Dennai, A. Ould-Abbas

Abstract:

The efficiency of SiGe solar cells might be improved by a wide-band-gap window layer. In this work we were simulated using the one dimensional simulation program called analysis of microelectronic and photonic structures (AMPS-1D). In the simulation, the thickness of silicon window was varied from 80 to 150 nm. The rest of layer’s thicknesses were kept constant, by varying thickness of window layer the simulated device performance was demonstrate in the form of current-voltage (I-V) characteristics and quantum efficiency (QE).

Keywords: SiGe, AMPS-1D, simulation, conversion, efficiency, quantum efficiency

Procedia PDF Downloads 780