Search results for: dynamic network analysis

Authors: Al-Amin, Huanjun Jiang, Anayat Ali

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Reinforced concrete frames (RC), especially Ordinary RC frames, are prone to structural failures/collapse during seismic events, leading to a large proportion of debris from the structures, which obstructs adjacent areas, including streets. These blocked areas severely impede post-earthquake resilience. This study uses computational simulation (FEM) to investigate the amount of debris generated by the seismic collapse of an ordinary reinforced concrete moment frame building and its effects on the adjacent pedestrian and road network. A three-story ordinary reinforced concrete frame building, primarily designed for gravity load and earthquake resistance, was selected for analysis. Sixteen different ground motions were applied and scaled up until the total collapse of the tested building to evaluate the failure mode under various seismic events. Four types of collapse direction were identified through the analysis, namely aligned (positive and negative) and skewed (positive and negative), with aligned collapse being more predominant than skewed cases. The amount and distribution of debris around the collapsed building were assessed to investigate the interaction between collapsed buildings and adjacent street networks. An interaction was established between a building that collapsed in an aligned direction and the adjacent pedestrian walkway and narrow street located in an unplanned old city. The FEM model was validated against an existing shaking table test. The presented results can be utilized to simulate the interdependency between the debris generated from the collapse of seismic-prone buildings and the resilience of street networks. These findings provide insights for better disaster planning and resilient infrastructure development in earthquake-prone regions.

Keywords: building collapse, earthquake-induced debris, ORC moment resisting frame, street network

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Authors: Tarek M. Alguhane, Ayman H. Khalil, Nour M. Fayed, Ayman M. Ismail

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Early, pre-code, reinforced concrete structures present undetermined resistance to earthquakes. This situation is particularly unacceptable in the case of essential structures, such as healthcare structures and pilgrims' houses. Among these, existing old RC building in Madinah is seismically evaluated with and without infill wall and their dynamic characteristics are compared with measured values in the field using ambient vibration measurements (AVM). After, updating the mathematical models for this building with the experimental results, three dimensional pushover analysis (Nonlinear static analysis) was carried out using SAP 2000 software incorporating inelastic material properties for concrete, infill and steel. The purpose of this analysis is to evaluate the expected performance of structural systems by estimating, strength and deformation demands in design, and comparing these demands to available capacities at the performance levels of interest. The results are summarized and discussed.

Keywords: seismic assessment, pushover analysis ambient vibration, modal update

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Authors: Nelson F. F. Ebecken, Gilberto C. Pereira, Lucio P. de Andrade

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Bays, estuaries and coastal ecosystems are some of the most used and threatened natural systems globally. Its deterioration is due to intense and increasing human activities. This paper aims to monitor the socio-ecological in Brazil, model and simulate it through a multilayer network representing a DPSIR structure (Drivers, Pressures, States-Impacts-Responses) considering the concept of Management based on Ecosystems to support decision-making under the National/State/Municipal Coastal Management policy. This approach considers several interferences and can represent a significant advance in several scientific aspects. The main objective of this paper is the coupling of three different types of complex networks, the first being an ecological network, the second a social network, and the third a network of economic activities, in order to model the marine ecosystem. Multilayer networks comprise two or more "layers", which may represent different types of interactions, different communities, different points in time, and so on. The dependency between layers results from processes that affect the various layers. For example, the dispersion of individuals between two patches affects the network structure of both samples. A multilayer network consists of (i) a set of physical nodes representing entities (e.g., species, people, companies); (ii) a set of layers, which may include multiple layering aspects (e.g., time dependency and multiple types of relationships); (iii) a set of state nodes, each of which corresponds to the manifestation of a given physical node in a layer-specific; and (iv) a set of edges (weighted or not) to connect the state nodes among themselves. The edge set includes the intralayer edges familiar and interlayer ones, which connect state nodes between layers. The applied methodology in an existent case uses the Flow cytometry process and the modeling of ecological relationships (trophic and non-trophic) following fuzzy theory concepts and graph visualization. The identification of subnetworks in the fuzzy graphs is carried out using a specific computational method. This methodology allows considering the influence of different factors and helps their contributions to the decision-making process.

Keywords: marine ecosystems, complex systems, multilayer network, ecosystems management

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Authors: B. S.Akhmetov, S. T. Akhmetova, D. N. Nadeyev, V. Yu. Yegorov, V. V. Smogoonov

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Estimated probabilities of errors of the first and second kind for nonideal biometrics-neural transducers 256 outputs, the construction of nomograms based error probability of 'own' and 'alien' from the mathematical expectation and standard deviation of the normalized measures Hamming.

Keywords: modeling, errors, probability, biometrics, neural network, authentication

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Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche

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This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.

Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization

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Authors: Rene Jimenez, Sonia E. Ruiz, Miguel A. Orellana

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On 09/19/2017, a Mw = 7.1 intraslab earthquake occurred in Mexico causing the collapse of about 40 buildings. Many of these were 5- or 6-story buildings with soft first story; so, it is desirable to perform a structural fragility analysis of typical structures representative of those buildings and to propose a reliable structural solution. Here, a typical 5-story building constituted by regular R/C moment-resisting frames in the first story and confined masonry walls in the upper levels, similar to the collapsed structures on the 09/19/2017 Mexico earthquake, is analyzed. Three different structural solutions of the 5-story building are considered: S1) it is designed in accordance with the Mexico City Building Code-2004; S2) then, the column dimensions of the first story corresponding to S1 are reduced, and S3) viscous dampers are added at the first story of solution S2. A number of dynamic incremental analyses are performed for each structural solution, using a 3D structural model. The hysteretic behavior model of the masonry was calibrated with experiments performed at the Laboratory of Structures at UNAM. Ten seismic ground motions are used to excite the structures; they correspond to ground motions recorded in intermediate soil of Mexico City with a dominant period around 1s, where the structures are located. The fragility curves of the buildings are obtained for different values of the maximum inter-story drift demands. Results show that solutions S1 and S3 give place to similar probabilities of exceedance of a given value of inter-story drift for the same seismic intensity, and that solution S2 presents a higher probability of exceedance for the same seismic intensity and inter-story drift demand. Therefore, it is concluded that solution S3 (which corresponds to the building with soft first story and energy dissipation devices) can be a reliable solution from the structural point of view.

Keywords: demand hazard analysis, fragility curves, incremental dynamic analyzes, soft-first story, structural capacity

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Authors: Dinara F. Gaynutdinova, Vladimir Ya Modorsky, Nikolay A. Shevelev

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The problem under research is that of unpredictable modes occurring in two-stage centrifugal hydraulic pump as a result of hydraulic processes caused by vibrations of structural components. Numerical, analytical and experimental approaches are considered. A hypothesis was developed that the problem of unpredictable pressure decrease at the second stage of centrifugal pumps is caused by cavitation effects occurring upon vibration. The problem has been studied experimentally and theoretically as of today. The theoretical study was conducted numerically and analytically. Hydroelastic processes in dynamic “liquid – deformed structure” system were numerically modelled and analysed. Using ANSYS CFX program engineering analysis complex and computing capacity of a supercomputer the cavitation parameters were established to depend on vibration parameters. An influence domain of amplitudes and vibration frequencies on concentration of cavitation bubbles was formulated. The obtained numerical solution was verified using CFM program package developed in PNRPU. The package is based on a differential equation system in hyperbolic and elliptic partial derivatives. The system is solved by using one of finite-difference method options – the particle-in-cell method. The method defines the problem solution algorithm. The obtained numerical solution was verified analytically by model problem calculations with the use of known analytical solutions of in-pipe piston movement and cantilever rod end face impact. An infrastructure consisting of an experimental fast hydro-dynamic processes research installation and a supercomputer connected by a high-speed network, was created to verify the obtained numerical solutions. Physical experiments included measurement, record, processing and analysis of data for fast processes research by using National Instrument signals measurement system and Lab View software. The model chamber end face oscillated during physical experiments and, thus, loaded the hydraulic volume. The loading frequency varied from 0 to 5 kHz. The length of the operating chamber varied from 0.4 to 1.0 m. Additional loads weighed from 2 to 10 kg. The liquid column varied from 0.4 to 1 m high. Liquid pressure history was registered. The experiment showed dependence of forced system oscillation amplitude on loading frequency at various values: operating chamber geometrical dimensions, liquid column height and structure weight. Maximum pressure oscillation (in the basic variant) amplitudes were discovered at loading frequencies of approximately 1,5 kHz. These results match the analytical and numerical solutions in ANSYS and CFM.

Keywords: computing experiment, hydroelasticity, physical experiment, vibration

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Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

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Authors: Andrew Laghos

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The Internet is a big part of our everyday lives. People can now access the internet from a variety of places including home, college, and work. Many airports, hotels, restaurants and cafeterias, provide free wireless internet to their visitors. Using technologies like computers, tablets, and mobile phones, we spend a lot of our time online getting entertained, getting informed, and communicating with each other. This study deals with the latter part, namely, human communication through the Internet. People can communicate with each other using social media, social network sites (SNS), e-mail, messengers, chatrooms, and so on. By connecting with each other they form virtual communities. Regarding SNS, types of connections that can be studied include friendships and cliques. Analyzing these connections is important to help us understand online user behavior. The method of Social Network Analysis (SNA) was used on a case study, and results revealed the existence of some useful patterns of interactivity between the participants. The study ends with implications of the results and ideas for future research.

Keywords: human communication, internet communities, online user behavior, psychology

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Authors: Ankan Roy, Niharika, Samir Kumar Patra

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Anomalous nexus of complex topological assemblies and spatiotemporal epigenetic choreography at chromosomal territory may forms the most sophisticated regulatory layer of gene expression in cancer. Colon cancer is one of the leading malignant neoplasms of the lower gastrointestinal tract worldwide. There is still a paucity of information about the complex molecular mechanisms of colonic cancerogenesis. Bioinformatics prediction and analysis helps to identify essential genes and significant pathways for monitoring and conquering this deadly disease. The present study investigates and explores potential hub genes as biomarkers and effective therapeutic targets for colon cancer treatment. Colon cancer patient sample containing gene expression profile datasets, such as GSE44076, GSE20916, and GSE37364 were downloaded from Gene Expression Omnibus (GEO) database and thoroughly screened using the GEO2R tool and Funrich software to find out common 2 differentially expressed genes (DEGs). Other approaches, including Gene Ontology (GO) and KEGG pathway analysis, Protein-Protein Interaction (PPI) network construction and hub gene investigation, Overall Survival (OS) analysis, gene correlation analysis, methylation pattern analysis, and hub gene-Transcription factors regulatory network construction, were performed and validated using various bioinformatics tool. Initially, we identified 166 DEGs, including 68 up-regulated and 98 down-regulated genes. Up-regulated genes are mainly associated with the Cytokine-cytokine receptor interaction, IL17 signaling pathway, ECM-receptor interaction, Focal adhesion and PI3K-Akt pathway. Downregulated genes are enriched in metabolic pathways, retinol metabolism, Steroid hormone biosynthesis, and bile secretion. From the protein-protein interaction network, thirty hub genes with high connectivity are selected using the MCODE and cytoHubba plugin. Survival analysis, expression validation, correlation analysis, and methylation pattern analysis were further verified using TCGA data. Finally, we predicted COL1A1, COL1A2, COL4A1, SPP1, SPARC, and THBS2 as potential master regulators in colonic cancerogenesis. Moreover, our experimental data highlights that disruption of lipid raft and RAS/MAPK signaling cascade affects this gene hub at mRNA level. We identified COL1A1, COL1A2, COL4A1, SPP1, SPARC, and THBS2 as determinant hub genes in colon cancer progression. They can be considered as biomarkers for diagnosis and promising therapeutic targets in colon cancer treatment. Additionally, our experimental data advertise that signaling pathway act as connecting link between membrane hub and gene hub.

Keywords: hub genes, colon cancer, DNA methylation, epigenetic engineering, bioinformatic predictions

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Authors: Yu Wang

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Nowadays, Internet enables its users to share the information online and to interact with others. Facing with numerous information, these Internet users are confused and begin to rely on the opinion leaders’ recommendations. The online opinion leaders are the individuals who have professional knowledge, who utilize the online channels to spread word-of-mouth information and who can affect the attitudes or even the behavior of their followers to some degree. Because utilizing the online opinion leaders is seen as an important approach to affect the potential consumers, how to identify them has become one of the hottest topics in the related field. Hence, in this article, the concepts and characteristics are introduced, and the researches related to identifying opinion leaders are collected and divided into three categories. Finally, the implications for future studies are provided.

Keywords: online opinion leaders, user attributes analysis, text mining analysis, network structure analysis

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Authors: Brahim Brahmi, Mohammed Hamza Laraki, Mohammad Habibur Rahman, Islam M. Rasedul, M. Assad Uz-Zaman

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The humanoid robot, specifically the NAO robot must be able to provide a highly dynamic performance on the soccer field. Maintaining the balance of the humanoid robot during the required motion is considered as one of a challenging problems especially when the robot is subject to external disturbances, as contact with other robots. In this paper, a dynamic controller is proposed in order to ensure a robust walking (stabilization) and to improve the dynamic balance of the robot during its contact with the environment (external disturbances). The generation of the trajectory of the center of mass (CoM) is done by a model predictive controller (MPC) conjoined with zero moment point (ZMP) technique. Taking into account the properties of the rotational dynamics of the whole-body system, a modified previous control mixed with feedback control is employed to manage the angular momentum and the CoM’s acceleration, respectively. This latter is dedicated to provide a robust gait of the robot in the presence of the external disturbances. Simulation results are presented to show the feasibility of the proposed strategy.

Keywords: preview control, Nao robot, model predictive control

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Authors: Dale Dzemydiene, Aurelija Burinskiene, Arunas Miliauskas, Kristina Ciziuniene

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Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Keywords: multi-objective, analysis, data flow, freight delivery, methodology

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Authors: A. Gharib, M. Moradi

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The technical analysis of using solar energy and electricity for water pumping in the Khuzestan province in Iran is investigated. For this purpose, the ecological conditions such as the weather data, air clearness and sunshine hours are analyzed. The nature of groundwater in the region was examined in terms of depth, static and dynamic head, water pumping rate. Three configurations for solar water pumping system were studied in this thesis; AC solar water pumping with a storage battery, AC solar water pumping with a storage tank, and DC direct solar water pumping.

Keywords: technical and economic feasibility, solar energy, photovoltaic systems, solar water pumping system

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Authors: Shadi Fathi, Moura Mehravar, Mujib Rahman

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The railwaytransportation network in the UK is over 100 years old and is known as one of the oldest mass transit systems in the world. This aged track network requires frequent closure for maintenance. One of the main reasons for closure is inadequate drainage due to the leakage in the buried drainage pipes. The leaking water can cause localised subgrade weakness, which subsequently can lead to major ground/substructure failure.Different condition assessment methods are available to assess the railway substructure. However, the existing condition assessment methods are not able to detect any local ground weakness/damageand provide details of the damage (e.g. size and location). To tackle this issue, a hybrid back-analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to predict the substructurelayers’ moduli and identify any soil weaknesses. At first, afinite element (FE) model of a railway track section under Falling Weight Deflection (FWD) testing was developed and validated against field trial. Then a drainage pipe and various scenarios of the local defect/ soil weakness around the buried pipe with various geometriesand physical properties were modelled. The impact of the soil local weaknesson the track surface deflection wasalso studied. The FE simulations results were used to generate a database for ANN training, and then a GA wasemployed as an optimisation tool to optimise and back-calculate layers’ moduli and soil weakness moduli (ANN’s input). The hybrid ANN-GA back-analysis technique is a computationally efficient method with no dependency on seed modulus values. The modelcan estimate substructures’ layer moduli and the presence of any localised foundation weakness.

Keywords: finite element (FE) model, drainage defect, falling weight deflectometer (FWD), hybrid ANN-GA

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Authors: Radha Tomar, Satish K. Jain, Manish Panchal, P. S. Rathore

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In the presented work, inset fed microstrip patch antenna (IFMPA) based two port MIMO Antenna system has been proposed, which is suitable for wireless local area network (WLAN) applications. IFMPA has been designed, optimized for 2.4 GHz and applied for MIMO formation. The optimized parameters of the proposed IFMPA have been used for fabrication of antenna and two port MIMO in a laboratory. Fabrication of the designed MIMO antenna has been done and tested experimentally for performance parameters like Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), Directive Gain (DG), Channel Capacity Loss (CCL), Multiplexing Efficiency (ME) etc and results are compared with simulated parameters extracted with simulated S parameters to validate the results. The simulated and experimentally measured plots and numerical values of these MIMO performance parameters resembles very much with each other. This shows the success of MIMO antenna design methodology.

Keywords: multiple-input and multiple-output, wireless local area network, vector network analyzer, envelope correlation coefficient

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Authors: Muhammad Tariq Chaudhary

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Bridges constitute a vital link in a transportation system and their functionality after an earthquake is critical in reducing disruption to social and economic activities of the society. Bridges supported on pile foundations are commonly used in many earthquake-prone regions. In order to properly design or investigate the performance of such structures, it is imperative that the effect of soil-foundation-structure interaction be properly taken into account. This study focused on the influence of soil and seismic ground motion variability on the dynamic impedance of pile-group foundations typically used for medium-span (about 30 m) urban viaduct bridges. Soil profiles corresponding to various AASHTO soil classes were selected from actual data of such bridges and / or from the literature. The selected soil profiles were subjected to 1-D wave propagation analysis to determine effective values of soil shear modulus and damping ratio for a suite of properly selected actual seismic ground motions varying in PGA from 0.01g to 0.64g, and having variable velocity and frequency content. The effective values of the soil parameters were then employed to determine the dynamic impedance of pile groups in horizontal, vertical and rocking modes in various soil profiles. Pile diameter was kept constant for bridges in various soil profiles while pile length and number of piles were changed based on AASHTO design requirements for various soil profiles and earthquake ground motions. Conclusions were drawn regarding variability in effective soil shear modulus, soil damping, shear wave velocity and pile group impedance for various soil profiles and ground motions and its implications for design and evaluation of pile-supported bridges. It was found that even though the effective soil parameters underwent drastic variation with increasing PGA, the pile group impedance was not affected much in properly designed pile foundations due to the corresponding increase in pile length or increase in a number of piles or both when subjected to increasing PGA or founded in weaker soil profiles.

Keywords: bridge, pile foundation, dynamic foundation impedance, soil profile, shear wave velocity, seismic ground motion, seismic wave propagation

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Authors: Giselle Maggie-Fer Castañeda Lozano

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The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.

Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Badar Muneer, Waseem Shabir, Faisal Karim Shaikh

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Substrate Integrated waveguide based 6-element array of Planar Inverted F antenna (PIFA) has been presented and analyzed parametrically in this paper. The antenna is fed with coupled transverse slots on a plate laminated waveguide cavity to ensure wide bandwidth and simplicity of feeding network. The two-layer structure has one layer dedicated for feeding network and the top layer dedicated for radiating elements. It has been demonstrated that the presented feeding technique for feeding such class of array antennas can be far simple in structure and miniaturized in size when it comes to designing large phased array antenna systems. A good return loss and standing wave ratio of 2:1 has been achieved while maintaining properties of typical PIFA.

Keywords: feeding network, laminated waveguide, PIFA, transverse slots

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Authors: M. Y. Ismail, M. Inam

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This paper presents the design and analysis of Liquid Crystal (LC) based tunable reflect array antenna with different design configurations within X-band frequency range. The effect of LC volume used for unit cell element on frequency tunability and reflection loss performance has been investigated. Moreover different slot embedded patch element configurations have been proposed for LC based tunable reflect array antenna design with enhanced performance. The detailed fabrication and measurement procedure for different LC based unit cells has been presented. The waveguide scattering parameter measured results demonstrated that by using the circular slot embedded patch elements, the frequency tunability and dynamic phase range can be increased from 180 MHz to 200 MHz and 120° to 124° respectively. Furthermore the circular slot embedded patch element can be designed at 10 GHz resonant frequency with a patch volume of 2.71 mm3 as compared to 3.47 mm3 required for rectangular patch without slot.

Keywords: liquid crystal, tunable reflect array, frequency tunability, dynamic phase range

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Authors: Ashish Kumar, Sanjeev Kumar Suman

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This study investigated the influence of Cloisite-15A nanoclay on the physical, performance, and mechanical properties of bitumen binder. Cloisite-15A was blended in the bitumen in variegated percentages from 1% to 9% with increment of 2%. The blended bitumen was characterized using penetration, softening point, and dynamic viscosity using rotational viscometer, and compared with unmodified bitumen equally penetration grade 60/70. The rheological parameters were investigated using Dynamic Shear Rheometer (DSR), and mechanical properties were investigated by using Marshall Stability test. The results indicated an increase in softening point, dynamic viscosity and decrease in binder penetration. Rheological properties of bitumen increase complex modulus, decrease phase angle and improve rutting resistances as well. There was significant improvement in Marshall Stability, rather marginal improvement in flow value. The best improvement in the modified binder was obtained with 5% Cloisite-15A nanoclay.

Keywords: Cloisite-15A, complex shear modulus, phase angle, rutting resistance

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Authors: Baeza S. Roberto

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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

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Authors: Bashir Abdu Muzakkari, Aisha Umar Sulaiman, Mohamed Afendee Muhamad, Sanah Abdullahi Muaz

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The field of the Internet of Things (IoT) is rapidly expanding and has the potential to completely change how we work, live, and interact with the world. The Internet of Things (IoT) is the term used to describe a network of networked physical objects, including machinery, vehicles, and buildings, which are equipped with electronics, software, sensors, and network connectivity. This review paper aims to provide a comprehensive overview of the current state of IoT, including its definition, key components, development history, and current applications. The paper will also discuss the challenges and opportunities presented by IoT, as well as its potential impact on various industries, such as healthcare, agriculture, and transportation. In addition, this paper will highlight the ethical and security concerns associated with IoT and the need for effective solutions to address these challenges. The paper concludes by highlighting the prospects of IoT and the directions for future research in this field.

Keywords: internet of things, IoT, sensors, network

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Authors: B. Moueddene, B. Labbaci, L. Missoum, R. Abdeldjebar

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Quality estimation of the experimental simulation of boundary conditions is one of the problems encountered while performing an experimental program. In fact, it is not easy to estimate directly the effective influence of these simulations on the results of experimental investigation. The aim of this is article to evaluate the effect of boundary conditions uncertainties on structure response, using the change of the dynamics characteristics. The experimental models used and the correlation by the Frequency Domain Assurance Criterion (FDAC) allowed an interpretation of the change in the dynamic characteristics. The application of this strategy to stratified composite structures (glass/ polyester) has given satisfactory results.

Keywords: vibration, composite, endommagement, correlation

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Authors: Akif Kutlu, Merve Ermis, Nihal Eratlı, Mehmet H. Omurtag

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The objective of this study is to investigate the forced vibration analysis of a planar curved beam lying on elastic foundation by using the mixed finite element method. The finite element formulation is based on the Timoshenko beam theory. In order to solve the problems in frequency domain, the element matrices of two nodded curvilinear elements are transformed into Laplace space. The results are transformed back to the time domain by the well-known numerical Modified Durbin’s transformation algorithm. First, the presented finite element formulation is verified through the forced vibration analysis of a planar curved Timoshenko beam resting on Winkler foundation and the finite element results are compared with the results available in the literature. Then, the forced vibration analysis of a planar curved beam resting on Winkler-Pasternak foundation is conducted.

Keywords: curved beam, dynamic analysis, elastic foundation, finite element method

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Authors: A. M. Andriano, M. Lombardi, A. Lopolito, M. Prosperi, A. Stasi, E. Iannuzzi

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In according to the scientific debate on the definition of Social Innovation (SI), the present paper identifies SI as new ideas (products, services, and models) that simultaneously meet social needs and create new social relationships or collaborations. This concept offers important tools to unravel the difficult condition for the agricultural sector in marginalized areas, characterized by the abandonment of activities, low level of farmer education, and low generational renewal, hampering new territorial strategies addressed at and integrated and sustainable development. Models of SI in agriculture, starting from bottom up approach or from the community, are considered to represent the driving force of an ecological and digital revolution. A system based on SI may be able to grasp and satisfy individual and social needs and to promote new forms of entrepreneurship. In this context, Vazapp ('Go Hoeing') is an emerging SI model in southern Italy that promotes solutions for satisfying needs of farmers and facilitates their relationships (creation of network). The Vazapp’s initiative, considered in this study, is the Contadinners ('Farmer’s dinners'), a dinner held at farmer’s house where stakeholders living in the surrounding area know each other and are able to build a network for possible future professional collaborations. The aim of the paper is to identify the evolution of farmers’ relationships, both quantitatively and qualitatively, because of the Contadinner’s initiative organized by Vazapp. To this end, the study adopts the Social Network Analysis (SNA) methodology by using UCINET (Version 6.667) software to analyze the relational structure. Data collection was realized through a questionnaire distributed to 387 participants in the twenty 'Contadinners', held from February 2016 to June 2018. The response rate to the survey was about 50% of farmers. The elaboration data was focused on different aspects, such as: a) the measurement of relational reciprocity among the farmers using the symmetrize method of answers; b) the measurement of the answer reliability using the dichotomize method; c) the description of evolution of social capital using the cohesion method; d) the clustering of the Contadinners' participants in followers and not-followers of Vazapp to evaluate its impact on the local social capital. The results concern the effectiveness of this initiative in generating trustworthy relationships within the rural area of southern Italy, typically affected by individualism and mistrust. The number of relationships represents the quantitative indicator to define the dimension of the network development; while the typologies of relationships (from simple friendship to formal collaborations, for branding new cooperation initiatives) represents the qualitative indicator that offers a diversified perspective of the network impact. From the analysis carried out, Vazapp’s initiative represents surely a virtuous SI model to catalyze the relationships within the rural areas and to develop entrepreneurship based on the real needs of the community.

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Procedia PDF Downloads 110

Authors: Behrooz Daneshmand

Abstract:

Software Defined Networking (SDN) is designed to meet the future needs of 5G mobile networks. The SDN architecture offers a new solution that involves separating the control plane from the data plane, which is usually paired together. Network functions traditionally performed on specific hardware can now be abstracted and virtualized on any device, and a centralized software-based administration approach is based on a central controller, facilitating the development of modern applications and services. These plan standards clear the way for a more adaptable, speedier, and more energetic network beneath computer program control compared with a conventional network. We accept SDN gives modern inquire about openings to security, and it can significantly affect network security research in numerous diverse ways. Subsequently, the SDN architecture engages systems to effectively screen activity and analyze threats to facilitate security approach modification and security benefit insertion. The segregation of the data planes and control and, be that as it may, opens security challenges, such as man-in-the-middle attacks (MIMA), denial of service (DoS) attacks, and immersion attacks. In this paper, we analyze security threats to each layer of SDN - application layer - southbound interfaces/northbound interfaces - controller layer and data layer. From a security point of see, the components that make up the SDN architecture have a few vulnerabilities, which may be abused by aggressors to perform noxious activities and hence influence the network and its administrations. Software-defined network assaults are shockingly a reality these days. In a nutshell, this paper highlights architectural weaknesses and develops attack vectors at each layer, which leads to conclusions about further progress in identifying the consequences of attacks and proposing mitigation strategies.

Keywords: software-defined networking, security, SDN, 5G/IMT-2020

Procedia PDF Downloads 98

Authors: Diego Aguirre, Olac Fuentes

Abstract:

In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques.

Keywords: deep learning, image classification, supervised learning, weight initialization

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

Abstract:

Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

Procedia PDF Downloads 303