Search results for: network structure

Authors: Mahdi Jalili

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Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

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Authors: Hossain Samani, Mohammad Ehteram

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In this paper, we consider finite volume method (FVM) in water hammer. We will simulate these techniques on a looped network with complex boundary conditions. After comparing methods, we see the FVM method as the best method. We compare the results of FVM with experimental data. Finite volume using staggered grid is applied for solving water hammer equations.

Keywords: hydraulic transient, water hammer, interpolation, non-liner interpolation

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Authors: Diogo Prosdocimi, Don Ross, Matthew Townshend

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Road authorities are mandated within limited budgets to both deliver improved access to basic services and facilitate economic growth. This responsibility is further complicated if maintenance backlogs and funding shortfalls exist, as evident in many countries including South Africa. These conditions require authorities to make difficult prioritisation decisions, with the effect that Road Asset Management Systems with a one-dimensional focus on traffic volumes may overlook the maintenance of low-volume roads that provide isolated communities with vital access to basic services. Given these challenges, this paper overlays the full South African road network with geo-referenced information for population, primary and secondary schools, and healthcare facilities to identify the network of connective roads between communities and basic service centres. This connective network is then rationalised according to the Gross Value Added and number of jobs per mesozone, administrative and functional road classifications, speed limit, and road length, location, and name to estimate the Basic Access Road Network. A two-step floating catchment area (2SFCA) method, capturing a weighted assessment of drive-time to service centres and the ratio of people within a catchment area to teachers and healthcare workers, is subsequently applied to generate a Multivariate Road Index. This Index is used to assign higher maintenance priority to roads within the Basic Access Road Network that provide more people with better access to services. The relatively limited incidence of Basic Access Roads indicates that authorities could maintain the entire estimated network without exhausting the available road budget before practical economic considerations get any purchase. Despite this fact, a final case study modelling exercise is performed for the Namakwa District Municipality to demonstrate the extent to which optimal relocation of schools and healthcare facilities could minimise the Basic Access Road Network and thereby release budget for investment in roads that best promote GDP growth.

Keywords: basic access roads, multivariate road index, road prioritisation, two-step floating catchment area method

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Authors: Mohamed Youssry, Dominique Guyomard, Bernard Lestriez

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In order to enhance the energy and power densities of electrodes for energy storage systems, the formulation and processing of electrode slurries proved to be a critical issue in determining the electrode performance. In this study, we introduce novel approach to formulate carbon black slurries based on microemulsion and lyotropic liquid crystalline phases (namely, lamellar phase) composed of non-ionic surfactant (Triton X100), decanol and water. Simultaneous measurements of electrical properties of slurries under shear flow (rheology) have been conducted to elucidate the microstructure evolution with the surfactant concentration and decanol/water ratio at rest, as well as, the structural transition under steady-shear which has been confirmed by rheo-microscopy. Interestingly, the carbon black slurries at low decanol/water ratio are weak-gel (flowable) with higher electrical conductivity than those at higher ratio which behave strong-gel viscoelastic response. In addition, the slurries show recoverable electrical behaviour under shear flow in tandem with the viscosity trend. It is likely that oil-in-water microemulsion enhances slurries’ stability without affecting on the percolating network of carbon black. On the other hand, the oil-in-water analogous and bilayer structure of lamellar phase cause the slurries less conductive as a consequence of losing the network percolation. These findings are encouraging to formulate microemulsion-based electrodes for energy storage system (lithium-ion batteries).

Keywords: electrode slurries, microemulsion, microstructure transition, rheo-electrical properties

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Authors: Akshay Satishrao Kawtikwar

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The effect of blast masses on structures is an essential aspect that need to be considered. This type of assault could be very horrifying, who where we take it into consideration in the course of the design system. While designing a building, now not only the wind and seismic masses however also the consequences of the blast have to be take into consideration. Blast load is the burden implemented to a structure form a blast wave that comes straight away after an explosion. A blast in or close to a constructing can reason catastrophic harm to the interior and exterior of the building, inner structural framework, wall collapsing, and so on. The most important feature of blast resistant construction is the ability to absorb blast energy without causing catastrophic failure of the structure as a whole. Construction materials in blastprotective structures must have ductility as well as strength.

Keywords: blast resistant design, blast load, explosion, ETABS

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

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Authors: Seyed Mahdi Jameii

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Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

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Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang

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Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.

Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

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Authors: D. Dorota

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This paper presents a method to generate and schedule task in the architecture of embedded systems based on the simulated annealing. This method takes into account the attribute of divisibility of tasks. A proposal represents the process in the form of trees. Despite the fact that the architecture of Network-on-Chip (NoC) is an interesting alternative to a bus architecture based on multi-processors systems, it requires a lot of work that ensures the optimization of communication. This paper proposes an effective approach to generate dedicated NoC topology solving communication problems. Network NoC is generated taking into account the energy consumption and resource issues. Ultimately generated is minimal, dedicated NoC topology. The proposed solution is assumed to be a simple router design and the minimum number of lines.

Keywords: Network-on-Chip, NoC-based embedded systems, scheduling task in embedded systems, simulated annealing

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Authors: Zhifei Hu, Feng Xia

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In recent years, Graph neural network has been widely used in knowledge graph recommendation. The existing recommendation methods based on graph neural network extract information from knowledge graph through entity and relation, which may not be efficient in the way of information extraction. In order to better propose useful entity information for the current recommendation task in the knowledge graph, we propose an end-to-end Neural network Model based on multi-stream graph attentional Mechanism (MSGAT), which can effectively integrate the knowledge graph into the recommendation system by evaluating the importance of entities from both users and items. Specifically, we use the attention mechanism from the user's perspective to distil the domain nodes information of the predicted item in the knowledge graph, to enhance the user's information on items, and generate the feature representation of the predicted item. Due to user history, click items can reflect the user's interest distribution, we propose a multi-stream attention mechanism, based on the user's preference for entities and relationships, and the similarity between items to be predicted and entities, aggregate user history click item's neighborhood entity information in the knowledge graph and generate the user's feature representation. We evaluate our model on three real recommendation datasets: Movielens-1M (ML-1M), LFM-1B 2015 (LFM-1B), and Amazon-Book (AZ-book). Experimental results show that compared with the most advanced models, our proposed model can better capture the entity information in the knowledge graph, which proves the validity and accuracy of the model.

Keywords: graph attention network, knowledge graph, recommendation, information propagation

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Authors: Naim Karasekreter, Ugur Fidan, Fatih Basciftci

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Wireless sensor networks are networks in which more than one sensor node is organized among themselves. The working principle is based on the transfer of the sensed data over the other nodes in the network to the central station. Wireless sensor networks concentrate on routing algorithms, energy efficiency and clustering algorithms. In the clustering method, the nodes in the network are divided into clusters using different parameters and the most suitable cluster head is selected from among them. The data to be sent to the center is sent per cluster, and the cluster head is transmitted to the center. With this method, the network traffic is reduced and the energy efficiency of the nodes is increased. In this study, clustering algorithms were examined in terms of clustering performances and cluster head selection characteristics to try to identify weak and strong sides. This work is supported by the Project 17.Kariyer.123 of Afyon Kocatepe University BAP Commission.

Keywords: wireless sensor networks (WSN), clustering algorithm, cluster head, clustering

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Authors: Dina Ibrahim Abouelamaiem, Ana Jorge Sobrido, Magdalena Titirici, Paul R. Shearing, Daniel J. L. Brett

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Global warming and scarcity of fossil fuels have had a radical impact on the world economy and ecosystem. The urgent need for alternative energy sources has hence elicited an extensive research for exploiting efficient and sustainable means of energy conversion and storage. Among various electrochemical systems, supercapacitors attracted significant attention in the last decade due to their high power supply, long cycle life compared to batteries and simple mechanism. Recently, the performance of these devices has drastically improved, as tuning of nanomaterials provided efficient charge and storage mechanisms. Carbon materials, in various forms, are believed to pioneer the next generation of supercapacitors due to their attractive properties that include high electronic conductivities, high surface areas and easy processing and functionalization. Cellulose has eco-friendly attributes that are feasible to replace man-made fibers. The carbonization of cellulose yields carbons, including activated carbon and graphite fibers. Activated carbons successively are the most exploited candidates for supercapacitor electrode materials that can be complemented with pseudocapacitive materials to achieve high energy and power densities. In this work, the optimum functionalization conditions of cellulose have been investigated for supercapacitor electrode materials. The precursor was treated with potassium hydroxide (KOH) at different KOH/cellulose ratios prior to the carbonization process in an inert nitrogen atmosphere at 850 °C. The chalky products were washed, dried and characterized with different techniques including transmission electron microscopy (TEM), x-ray tomography and nitrogen adsorption-desorption isotherms. The morphological characteristics and their effect on the electrochemical performances were investigated in two and three-electrode systems. The KOH/cellulose ratios of 0.5:1 and 1:1 exhibited the highest performances with their unique hierarchal porous network structure, high surface areas and low cell resistances. Both samples acquired the best results in three-electrode systems and coin cells with specific gravimetric capacitances as high as 187 F g-1 and 20 F g-1 at a current density of 1 A g-1 and retention rates of 72% and 70%, respectively. This is attributed to the morphology of the samples that constituted of a well-balanced micro-, meso- and macro-porosity network structure. This study reveals that the electrochemical performance doesn’t solely depend on high surface areas but also an optimum pore size distribution, specifically at low current densities. The micro- and meso-pore contribution to the final pore structure was found to dominate at low KOH loadings, reaching ‘equilibrium’ with macropores at the optimum KOH loading, after which macropores dictate the porous network. The wide range of pore sizes is detrimental for the mobility and penetration of electrolyte ions in the porous structures. These findings highlight the influence of various morphological factors on the double-layer capacitances and high performance rates. In addition, they open a platform for the investigation of the optimized conditions for double-layer capacitance that can be coupled with pseudocapacitive materials to yield higher energy densities and capacities.

Keywords: carbon, electrochemical performance, electrodes, KOH/cellulose optimized ratio, morphology, supercapacitor

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Authors: A. Clementking, C. Jothi Venkateswaran

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Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: T. Vikneshvaran, A. Aminudin, U. Alyaa Hashim, Waziralilah N. Fathiah, D. Shakirah Shukor

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This paper presents the experimental study conducted on a structure of three-floor height building model. Most vibrations are undesirable and can cause damages to the buildings, machines and people all around us. The vibration wave from earthquakes, construction and winds have high potential to bring damage to the buildings. Excessive vibrations can result in structural and machinery failures. This failure is related to the human life and environment around it. The effect of vibration which causes failure and damage to the high rise buildings can be controlled in real life by implementing tuned mass damper (TMD) into the structure of the buildings. This research aims to study the effect and performance improvement achieved by applying TMD into the building structure. A structure model of three degrees of freedom (3DOF) is designed to demonstrate the performance of TMD to the designed model. The model designed is the physical representation of actual building structure in real life. It is constructed at a reduced scale and will be used for the experiment. Thus, the result obtained will be more accurate to compared with the real life effect. Based on the result from experimental study, by applying TMD to the structure model, the forces of vibration and the displacement mode of the building reduced. Thus, the reduced in vibration of the building helps to maintain the good condition of the building.

Keywords: degrees-of-freedom, displacement mode, natural frequency, tuned mass damper

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Authors: S. H. Bae, D. W. Cho, W. B. Jeong, J. R. Cho

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Fatigue fracture of an aluminum welded structure is a phenomenon frequently occurring from pores in a weld. In order to grasp the state of the welded structure in operation in real time, the acceleration signal of the structure is measured. At this time, the vibration characteristic of the signal according to the fatigue load is an important parameter of the state diagnosis. This paper was an experimental study on the variation of vibration characteristics of welded beams with vibration aging (especially bending vibration). First simple beams were produced according to welding conditions. Each beam was vibrated and measured beam's PSD (power spectral density) according to the degree of aging. Also, modal testing was conducted to compare the transfer functions of welded beams. Testing result shows that the natural frequencies of the beam changed with the vibration aging due to the change of stiffness in welding part and its stiffness was estimated by the finite element method.

Keywords: modal testing, natural frequency, vibration aging, welded structure

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Authors: Junhang Luo

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With the transformation of spatial structure and the deepening of urban development, the demand for a better life and the concerns for social resources equities of residents are increasing. As an important social resource, park plays an essential role in building environmentally sustainable cities. Thus, it is important to examine park accessibility for low-income and how it works in relative deprivation, so as to provide all residents with equitable services. Using the network and buffer methods of GIS, this paper analyzes urban park accessibility for low-income residents in Chongqing, China. And then conduct a satisfaction evaluation of park resource accessibility with low-incomes through questionnaire surveys from deprivation dimensions. Results show that the level of park accessibility in Chongqing varies significantly and the degree of relative deprivation is relatively high. Public transportation convenience improves and the number of community park increases contribute positively to improving park accessibility and alleviating the relative deprivation of public resources. Combined with the innovation pattern of social governance in China, it suggests that urban park accessibility needs to be jointly governed and optimized by multiple social resources from the government to the public, and the service efficiency needs the index system and planning standards according to local conditions to improve quality and promote equity. At the same time, building a perfect park system and complete legislation assurance system will also play a positive role in ensuring that all residents can enjoy the urban public space more fairly, especially low-income groups.

Keywords: urban park, accessibility, relative deprivation, GIS network analysis, chongqing

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Authors: ShuHui Yang, ChingChing Lu

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Measuring event-related potentials (ERPs) has been fundamental to our understanding of how people process language. One specific ERP component, a P600, has been hypothesized to be associated with syntactic reanalysis processes. We, however, propose that the P600 is not restricted to reanalysis processes, but is the index of the structural pattern processing. To investigate the structural pattern processing, we utilized the effects of stimulus degradation in structural priming. To put it another way, there was no P600 effect if the structure of the prime was the same with the structure of the target. Otherwise, there would be a P600 effect if the structure were different between the prime and the target. In the experiment, twenty-two participants were presented with four sentences of Tang poetry. All of the first two sentences, being prime, were conducted with SVO+VP. The last two sentences, being the target, were divided into three types. Type one of the targets was SVO+VP. Type two of the targets was SVO+VPVP. Type three of the targets was VP+VP. The result showed that both of the targets, SVO+VPVP and VP+VP, elicited positive-going brainwave, a P600 effect, at 600~900ms time window. Furthermore, the P600 component was lager for the target’ VP+VP’ than the target’ SVO+VPVP’. That meant the more dissimilar the structure was, the lager the P600 effect we got. These results indicate that P600 was the index of the structure processing, and it would affect the P600 effect intensity with the degrees of structural heterogeneity.

Keywords: ERPs, P600, structural pattern, structural priming, Tang poetry

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Authors: M. Heidari, A. Safekordi, A. Zamaniyan, E. Ganji Babakhani, M. Amanipour

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Perovskite-type membrane Ba_0.5Sr_0.5Ce_0.9Y_0.1O_3-δ (BSCY) was successfully synthesized by liquid citrate method. The hydrogen permeation and stability of BSCY perovskite-type membranes were studied at high temperatures. The phase structure of the powder was characterized by X-ray diffraction (XRD). Scanning electron microscopy (SEM) was used to characterize microstructures of the membrane sintered under various conditions. SEM results showed that increasing in sintering temperature, formed dense membrane with clear grains. XRD results for BSCY membrane that sintered in 1150 °C indicated single phase perovskite structure with orthorhombic configuration, and SEM results showed dense structure with clear grain size which is suitable for permeation tests. Partial substitution of Sr with Ba in SCY structure improved the hydrogen permeation flux through the membrane due to the larger ionic radius of Ba²⁺. BSCY membrane shows high hydrogen permeation flux of 1.6 ml/min.cm² at 900 °C and partial pressure of 0.6.

Keywords: hydrogen separation, perovskite, proton conducting membrane.

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Authors: Naveed Ghani, Samreen Javed

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In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.

Keywords: network worms, malware infection propagating malicious code, virus, security, VPN

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: D. G. Badagha, C. D. Modhera, S. A. Vasanwala

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There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.

Keywords: artificial neural network, high performance concrete, rebound hammer, strength prediction

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

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

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

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Authors: Xu Chen, Zhao Caiqi

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In addition to the advantages of light weight, resistant corrosion and ease of processing, aluminum is also applied to the long-span spatial structures. However, the elastic modulus of aluminum is lower than that of the steel. This paper combines the high performance aluminum honeycomb panel with the aluminum latticed shell, forming a new panel-and-rod composite shell structure. Through comparative analysis between the static and dynamic performance, the conclusion that the structure of composite shell is noticeably superior to the structure combined before.

Keywords: combination of aluminum honeycomb panel, rod latticed shell, dynamic performence, response spectrum analysis, seismic properties

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Fariba Ebrahimi, Mehdi Ghorbani

Abstract:

Comprehensive water management considers economic, environmental, technical and social sustainability of water resources for future generations. Integrated water management implies cooperative approach and involves all stakeholders and also introduces issues to managers and decision makers. Solving these issues needs integrated and system approach according to the recognition of actors or key persons in necessary to apply cooperative management of water resources. Therefore, social network analysis can be used to demonstrate the most effective actors for environmental base decisions. The linkage of diverse sets of actors and knowledge systems across management levels and institutional boundaries often poses one of the greatest challenges in adaptive water management. Bridging stakeholder can facilitate interactions among actors in management settings by lowering the transaction costs of collaboration. This research examines how network connections between group members affect in co- management. Cohesive network structures allow groups to more effectively achieve their goals and objectives Strong; centralized leadership is a better predictor of working group success in achieving goals and objectives. Finally, geometric position of each actor was illustrated in the network. The results of the research based on between centrality index have a key and bridging actor in recognition of cooperative management of water resources in Darbandsar village and also will help managers and planners of water in the case of recognition to organization and implementation of sustainable management of water resources and water security.

Keywords: co-management, water management, social network, bridging stakeholder, darbandsar village

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Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

Abstract:

Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios.

Keywords: hybrid MAC protocol, data integrity, lightweight encryption, neighbor based key sharing, sensor node dataprocessing, Z-MAC

Procedia PDF Downloads 136

Authors: O. Eren, L. Zakar

Abstract:

Recycling steel building components is key to the sustainability of a structure’s end-of-life, as it is the most economical solution. In this paper the effects of usage of recycled steel material in tall buildings aspects are investigated.

Keywords: building, recycled material, steel, structure

Procedia PDF Downloads 375

Authors: Wang Haining, Zhang Hong

Abstract:

In China, rapid urbanization needs more and more buildings constructed for the growing population in cities. With the help of the methodology which contains investigation, contrastive analysis, design based on component with BIM and experiment before real construction, this research based on mobile light structure system, trying to the sustainable problems partly in present China by systematic study. The system cannot replace the permanent heavy structure completely. So the goal is the improvement of the whole building system by the addition of light structure. This house system uses modularized envelopes and standardized connections, which are pre-fabricated and assembled in factories and transported like containers. Aluminum is used as the structural material in this system, and inorganic thermal insulation material used in the envelope, which have high fireproof properties. The relationship between manufactory and construction of the system is progressive hierarchy. They exist as First Industrial, Second Industrial, Third Industrial and Site Assembly Stage. It could maximize the land usage capacity by fully exploit the area where normal permanent architecture can't take advantage of. Not only the building system itself especially the thermal isolated materials used and active solar photovoltaic system equipped can save energy, but also the way of product development is sustainable.

Keywords: aluminum house, light Structure, rapid assembly, repeat construction

Procedia PDF Downloads 484