Search results for: network devices

Authors: Melissa Dickinson

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In the context of intense global competitiveness and urban transformations, today’s cities are faced with enormous challenges. There is increasing pressure among cities and regions to respond promptly and efficiently to fierce market progressions, to offer a competitive advantage, higher flexibility, and to be pro-active in creating future markets. Consequently, competition among cities and regions within the dynamics of a worldwide spatial economic system is growing fiercer, amplifying the importance of intangible capital in shaping the competitive and dynamic economic performance of organisations and firms. Accordingly, this study addresses how intangible capital influences urban economic development within an urban environment. Despite substantial research on the economic, and strategic determinants of urban economic development this multidimensional phenomenon remains to be one of the greatest challenges for economic geographers. The research provides a unique contribution, exploring intangible capital through the lenses of entrepreneurial capital and social-network capital. Drawing on business surveys and in-depth interviews with key stakeholders in the case of the three UK Core Cities Birmingham, Bristol and Cardiff. This paper critically considers how entrepreneurial capital and social-network capital is a crucial source of competitiveness and urban economic development. This paper deals with questions concerning the complexity of operationalizing ‘network capital’ in different urban settings and the challenges that reside in characterising its effects. The paper will highlight the role of institutions in facilitating urban economic development. Particular emphasis will be placed on exploring the roles formal and informal institutions have in delivering, supporting and nurturing entrepreneurial capital and social-network capital, to facilitate urban economic development. Discussions will then consider how institutions moderate and contribute to the economic development of urban areas, to provide implications in terms of future policy formulation in the context of large and medium sized cities.

Keywords: urban economic development, network capital, entrepreneurialism, institutions

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Authors: Zerzouri Nora

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In this paper, author presented the generalities of a photovoltaic system study and simulation. Author inserted the DC-DC converter to raise the voltage level and improve the operation of the PV panel by continuing the operating point at maximum power by using the Perturb and Observe technique (P&O). The connection to the network is made by inserting a three-phase voltage inverter allowing synchronization with the network the inverter is controlled by a PWM control. The simulation results allow the author to visualize the operation of the different components of the system, as well as the behavior of the system during the variation of meteorological values.

Keywords: photovoltaic generator PV, boost converter, P&O MPPT, PWM inverter, three phase grid

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Authors: Jennifer E Simpson

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Background and purpose: The ubiquitous use of the Internet and social media by children and young people has had a dual effect. The first is to open a world of possibilities and promise that is characterized by the ability to consume and create content, connect with friends, explore and experiment. The second relates to risks such as unsolicited requests, sexual exploitation, cyberbullying and commercial exploitation. This duality poses significant difficulties for a generation of foster carers and social workers who have no childhood experience to draw on in terms of growing up using the Internet, social media and digital devices. This presentation is concerned with the findings of a small qualitative study about the use of digital devices and the Internet by care-experienced young people to stay in touch with their families and the way this was managed by foster carers and social workers using specific parental mediation strategies. The findings highlight that restrictive strategies were used by foster carers and endorsed by social workers. An argument is made for an approach that develops a series of balanced solutions that move foster carers from such restrictive approaches to those that are grounded in co-use and are interpretive in nature. Methods: Using a purposive sampling strategy, 12 triads consisting of care-experienced young people (aged 13-18 years), their foster carers and allocated social workers were recruited. All respondents undertook a semi-structured interview, with the young people detailing what social media apps and other devices they used to contact their families via an Ecomap. The foster carers and social workers shared details of the methods and approaches they used to manage digital devices and the Internet in general. Data analysis was performed using a Framework analytic method to explore the various attitudes, as well as complementary and contradictory perspectives of the young people, their foster carers and allocated social workers. Findings: The majority of foster carers made use of parental mediation strategies that erred on the side of typologies that included setting rules and regulations (restrictive), ad-hoc checking of a young person’s behavior and device (monitoring), and software used to limit or block access to inappropriate websites (technical). It was noted that minimal use was made by foster carers of parental mediation strategies that included talking about content (active/interpretive) or sharing Internet activities (co-use). Amongst the majority of the social workers, they also had a strong preference for restrictive approaches. Conclusions and implications: Trepidations on the part of both foster carers and social workers about the use of digital devices and the Internet meant that the parental strategies used were weighted more towards restriction, with little use made of approaches such as co-use and interpretative. This lack of balance calls for solutions that are grounded in co-use and an interpretive approach, both of which can be achieved through training and support, as well as wider policy change.

Keywords: parental mediation strategies, risk, children in state care, online safety

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Authors: Prajamitra Bhuyan

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Transport operators have a range of intervention options available to improve or enhance their networks. But often such interventions are made in the absence of sound evidence on what outcomes may result. Cycling superhighways were promoted as a sustainable and healthy travel mode which aims to cut traffic congestion. The estimation of the impacts of the cycle superhighways on congestion is complicated due to the non-random assignment of such intervention over the transport network. In this paper, we analyse the causal effect of cycle superhighways utilising pre-innervation and post-intervention information on traffic and road characteristics along with socio-economic factors. We propose a modeling framework based on the propensity score and outcome regression model. The method is also extended to doubly robust set-up. Simulation results show the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse a real dataset on the London transport network, and the result would help effective decision making to improve network performance.

Keywords: average treatment effect, confounder, difference-in-difference, intelligent transportation system, potential outcome

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Authors: Amin Hamrahi, Niloofar Moghaddam

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Denial of Service is for denial-of-service attack, a type of attack on a network that is designed to bring the network to its knees by flooding it with useless traffic. Denial of Service (DoS) attacks have become a major threat to current computer networks. Many recent DoS attacks were launched via a large number of distributed attacking hosts in the Internet. These attacks are called distributed denial of service (DDoS) attacks. To have a better understanding on DoS attacks, this article provides an overview on existing DoS and DDoS attacks and major defense technologies in the Internet.

Keywords: denial of service, distributed denial of service, traffic, flooding

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Authors: Rajesh Agarwal

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Carrier mobility at the organic/insulator interface is essential to the performance of organic thin film transistors (OTFT). The present work describes estimation of field dependent mobility (FDM) parameters and the threshold voltage of an OTFT using a simple, easy to fabricate two terminal asymmetric capacitive test structure using admittance measurements. Conventionally, transfer characteristics are used to estimate the threshold voltage in an OTFT with field independent mobility (FIDM). Yet, this technique breaks down to give accurate results for devices with high contact resistance and having field dependent mobility. In this work, a new technique is presented for characterization of long channel organic capacitor (LCOC). The proposed technique helps in the accurate estimation of mobility enhancement factor (γ), the threshold voltage (V_th) and band mobility (µ₀) using capacitance-voltage (C-V) measurement in OTFT. This technique also helps to get rid of making short channel OTFT or metal-insulator-metal (MIM) structures for making C-V measurements. To understand the behavior of devices and ease of analysis, transmission line compact model is developed. The 2-D numerical simulation was carried out to illustrate the correctness of the model. Results show that proposed technique estimates device parameters accurately even in the presence of contact resistance and field dependent mobility. Pentacene/Poly (4-vinyl phenol) based top contact bottom-gate OTFT’s are fabricated to illustrate the operation and advantages of the proposed technique. Small signal of frequency varying from 1 kHz to 5 kHz and gate potential ranging from +40 V to -40 V have been applied to the devices for measurement.

Keywords: capacitance, mobility, organic, thin film transistor

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Authors: Nateepat Srivarom, Weng Jingnong, Serm Chinnarat

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Precise Point Positioning (PPP) technique is use to improve accuracy by using precise satellite orbit and clock correction data, but this technique is complicated methods and high costs. Currently, there are several online processing service providers which offer simplified calculation. In the first part of this research, we compare the efficiency and precision of four software. There are three popular online processing service providers: Australian Online GPS Processing Service (AUSPOS), CSRS-Precise Point Positioning and CenterPoint RTX post processing by Trimble and 1 offline software, RTKLIB, which collected data from 10 the International GNSS Service (IGS) stations for 10 days. The results indicated that AUSPOS has the least distance root mean square (DRMS) value of 0.0029 which is good enough to be calculated for monitoring the movement of tectonic plates. The second, we use AUSPOS to process the data of geodetic network of Thailand. In December 26, 2004, the earthquake occurred a 9.3 MW at the north of Sumatra that highly affected all nearby countries, including Thailand. Earthquake effects have led to errors of the coordinate system of Thailand. The Royal Thai Survey Department (RTSD) is primarily responsible for monitoring of the crustal movement of the country. The difference of the geodetic network movement is not the same network and relatively large. This result is needed for survey to continue to improve GPS coordinates system in every year. Therefore, in this research we chose the AUSPOS to calculate the magnitude and direction of movement, to improve coordinates adjustment of the geodetic network consisting of 19 pins in Thailand during October 2013 to November 2017. Finally, results are displayed on the simulation map by using the ArcMap program with the Inverse Distance Weighting (IDW) method. The pin with the maximum movement is pin no. 3239 (Tak) in the northern part of Thailand. This pin moved in the south-western direction to 11.04 cm. Meanwhile, the directional movement of the other pins in the south gradually changed from south-west to south-east, i.e., in the direction noticed before the earthquake. The magnitude of the movement is in the range of 4 - 7 cm, implying small impact of the earthquake. However, the GPS network should be continuously surveyed in order to secure accuracy of the geodetic network of Thailand.

Keywords: precise point positioning, online processing service, geodetic network, inverse distance weighting

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Authors: Zakir Husain, Neem Sagar, Neeraj Gupta

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Due to the increased penetration of renewable energy resources in the distribution system, the system is no longer passive in nature. In this paper, a steady state analysis of the distribution system has been done with the inclusion of wind generation. The modeling of wind turbine generator system and wind generator has been made to obtain the average active and the reactive power injection into the system. The study has been conducted on a IEEE-33 bus system with two wind generators. The present research work is useful not only to utilities but also to customers.

Keywords: distributed generation, distribution network, radial network, wind turbine generating system

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Authors: Jainendra Singh, Zaheeruddin

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A typical wireless sensor networks (WSNs) consists of several tiny and low-power sensors which use radio frequency to perform distributed sensing tasks. The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While routing protocols are striving to save energy by acting on sensor nodes, recent studies show that network lifetime can be enhanced by further involving sink mobility. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we propose an energy- efficient homogenous clustering (EHC) technique. In this technique, the decision of each sensor is based on their residual energy and an estimate of how many of its neighboring cluster heads (CHs) will benefit from it being a CH. We, also explore the routing algorithm in clustered WSNs. We show that the proposed schemes significantly outperform current approaches in terms of packet delay, hop count and energy consumption of WSNs.

Keywords: wireless sensor network, energy efficiency, clustering, routing

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Authors: Behnaz Moradijamei, Michael Higgins

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Identifying community structure is a critical task in analyzing social media data sets often modeled by networks. Statistical models such as the stochastic block model have proven to explain the structure of communities in real-world network data. In this work, we develop a goodness-of-fit test to examine community structure's existence by using a distinguishing property in networks: cyclic structures are more prevalent within communities than across them. To better understand how communities are shaped by the cyclic structure of the network rather than just the number of edges, we introduce a novel method for deciding on the existence of communities. We utilize these structures by using renewal non-backtracking random walk (RNBRW) to the existing goodness-of-fit test. RNBRW is an important variant of random walk in which the walk is prohibited from returning back to a node in exactly two steps and terminates and restarts once it completes a cycle. We investigate the use of RNBRW to improve the performance of existing goodness-of-fit tests for community detection algorithms based on the spectral properties of the adjacency matrix. Our proposed test on community structure is based on the probability distribution of eigenvalues of the normalized retracing probability matrix derived by RNBRW. We attempt to make the best use of asymptotic results on such a distribution when there is no community structure, i.e., asymptotic distribution under the null hypothesis. Moreover, we provide a theoretical foundation for our statistic by obtaining the true mean and a tight lower bound for RNBRW edge weights variance.

Keywords: hypothesis testing, RNBRW, network inference, community structure

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Authors: Manverpreet Kaur, Amarpreet Singh

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The demand of Vehicular ad hoc networks is increasing day by day, due to offering the various applications and marvelous benefits to VANET users. Clustering in VANETs is most important to overcome the connectivity problems of VANETs. In this paper, we proposed a new clustering technique Enhanced cluster based connectivity maintenance in vehicular ad hoc network. Our objective is to form long living clusters. The proposed approach is grouping the vehicles, on the basis of the longest list of neighbors to form clusters. The cluster formation and cluster head selection process done by the RSU that may results it reduces the chances of overhead on to the network. The cluster head selection procedure is the vehicle which has closest speed to average speed will elect as a cluster Head by the RSU and if two vehicles have same speed which is closest to average speed then they will be calculate by one of the new parameter i.e. distance to their respective destination. The vehicle which has largest distance to their destination will be choosing as a cluster Head by the RSU. Our simulation outcomes show that our technique performs better than the existing technique.

Keywords: VANETs, clustering, connectivity, cluster head, intelligent transportation system (ITS)

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Authors: Nesrine Toubaline, Djamel Bennouar, Ali Mahdoum

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3D technology can lead to a significant reduction in power and average hop-count in Networks on Chip (NoCs). It offers short and fast vertical links which copes with the long wire problem in 2D NoCs. This work proposes heuristic-based method to optimize number and placement of vertical links to achieve specified performance goals. Experiments show that significant improvement can be achieved by using a specific number of vertical interconnect.

Keywords: interconnect optimization, monolithic inter-tier vias, network on chip, system on chip, through silicon vias, three dimensional integration circuits

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Authors: Prateeksha Mahamallik, Anjali Pal

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In the present study, Co(II)-adsolubilized surfactant modified alumina (SMA) was prepared, and methylene blue (MB) degradation was carried out on Co-SMA surface by visible light photo-Fenton process. The entire reaction proceeded on solid surface as MB was embedded on Co-SMA surface. The reaction followed zero order kinetics. Response surface methodology (RSM) and artificial neural network (ANN) were used for modeling the decolorization of MB by photo-Fenton process as a function of dose of Co-SMA (10, 20 and 30 g/L), initial concentration of MB (10, 20 and 30 mg/L), concentration of H2O2 (174.4, 348.8 and 523.2 mM) and reaction time (30, 45 and 60 min). The prediction capabilities of both the methodologies (RSM and ANN) were compared on the basis of correlation coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), relative percent deviation (RPD). Due to lower value of RMSE (1.27), SEP (2.06) and RPD (1.17) and higher value of R2 (0.9966), ANN was proved to be more accurate than RSM in order to predict decolorization efficiency.

Keywords: adsolubilization, artificial neural network, methylene blue, photo-fenton process, response surface methodology

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Authors: Mirza Mujtaba Baig

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Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

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Authors: Basant Singh Sikarwar, Mukesh Roy, Ayush Goyal, Priya Ranjan

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This work combines two bodies of knowledge which includes biomedical basis of blood stain formation and fluid communities’ wisdom that such formation of blood stain depends heavily on physical properties. Moreover biomedical research tells that different patterns in stains of blood are robust indicator of blood donor’s health or lack thereof. Based on these valuable insights an innovative screening tool is proposed which can act as an aide in the diagnosis of diseases such Anemia, Hyperlipidaemia, Tuberculosis, Blood cancer, Leukemia, Malaria etc., with enhanced confidence in the proposed analysis. To realize this powerful technique, simple, robust and low-cost micro-fluidic devices, a micro-capillary viscometer and a pendant drop tensiometer are designed and proposed to be fabricated to measure the viscosity, surface tension and wettability of various blood samples. Once prognosis and diagnosis data has been generated, automated linear and nonlinear classifiers have been applied into the automated reasoning and presentation of results. A support vector machine (SVM) classifies data on a linear fashion. Discriminant analysis and nonlinear embedding’s are coupled with nonlinear manifold detection in data and detected decisions are made accordingly. In this way, physical properties can be used, using linear and non-linear classification techniques, for screening of various diseases in humans and cattle. Experiments are carried out to validate the physical properties measurement devices. This framework can be further developed towards a real life portable disease screening cum diagnostics tool. Small-scale production of screening cum diagnostic devices is proposed to carry out independent test.

Keywords: blood, physical properties, diagnostic, nonlinear, classifier, device, surface tension, viscosity, wettability

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Authors: O. Guellati, A. Harat, F. Djefaflia, N. Habib, A. Nait-Merzoug, J. El Haskouri, D. Momodu, N. Manyala, D. Bégin, M. Guerioune

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We report a facile and low-cost free-template synthesis method was used to synthesize mesoporous smart multifunctional nanohybrids based on Graphene/PANI nanofibers micro/nanostructures with very interesting physic-chemical properties and faradic electrochemical behavior of these products was investigated. These nanohybrid products have been characterized quantitatively and qualitatively using different techniques, such as XRD / FTIR, Raman, XPS spectroscopy, Field Emission SEM and High-Resolution TEM microscopy, BET textural analysis, electrochemical measurements (CV, CD, EIS). Moreover, the electrochemical measurements performed in a 6 M KOH aqueous electrolyte depicted excellent electrochemical performance ascribed to the optimized composition of hydroxides et PANI nanofibers. An exceptionally notable specific capacitance between 800  and 2000 F. g-1 was obtained at 5  mV. s-1 scan rate for these synthesized products depends on the optimized growth conditions. We found much better nanohybrids by reinforcing hydroxides or conduction polymer nanofibers with carbonaceous nanomaterials depicting their potential as suitable materials for energy storage devices.

Keywords: nanohybrid materials, conducting polymers, carbonaceous nanomaterials, supercapacitors, energy storage

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Authors: Nassim Labdaoui, Fabienne Nouvel, Stéphane Dutertre

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The Internet of Things (IoT) is emerging as a crucial communication technology for the future. Many solutions have been proposed, and among them, licensed operators have put forward LTE-M and NB-IoT. However, implementing these technologies requires a good understanding of the device energy requirements, which can vary depending on the coverage conditions. In this paper, we investigate the power consumption of LTE-M and NB-IoT devices using Ublox SARA-R422S modules based on relevant standards from two French operators. The measurements were conducted under different coverage conditions, and we also present an empirical consumption model based on the different states of the radio modem as per the RRC protocol specifications. Our findings indicate that these technologies can achieve a 5 years operational battery life under certain conditions. Moreover, we conclude that the size of transmitted data does not have a significant impact on the total power consumption of the device under favorable coverage conditions. However, it can quickly influence the battery life of the device under harsh coverage conditions. Overall, this paper offers insights into the power consumption of LTE-M and NBIoT devices and provides useful information for those considering the use of these technologies.

Keywords: internet of things, LTE-M, NB-IoT, MQTT, cellular IoT, power consumption

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Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

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Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: E. M. Hassan, A. L. Kalamkarov

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Two micromechanical models for 3D smart composite with embedded periodic or nearly periodic network of generally orthotropic reinforcements and actuators are developed and applied to cubic structures with unidirectional orientation of constituents. Analytical formulas for the effective piezothermoelastic coefficients are derived using the Asymptotic Homogenization Method (AHM). Finite Element Analysis (FEA) is subsequently developed and used to examine the aforementioned periodic 3D network reinforced smart structures. The deformation responses from the FE simulations are used to extract effective coefficients. The results from both techniques are compared. This work considers piezoelectric materials that respond linearly to changes in electric field, electric displacement, mechanical stress and strain and thermal effects. This combination of electric fields and thermo-mechanical response in smart composite structures is characterized by piezoelectric and thermal expansion coefficients. The problem is represented by unit-cell and the models are developed using the AHM and the FEA to determine the effective piezoelectric and thermal expansion coefficients. Each unit cell contains a number of orthotropic inclusions in the form of structural reinforcements and actuators. Using matrix representation of the coupled response of the unit cell, the effective piezoelectric and thermal expansion coefficients are calculated and compared with results of the asymptotic homogenization method. A very good agreement is shown between these two approaches.

Keywords: asymptotic homogenization method, finite element analysis, effective piezothermoelastic coefficients, 3D smart network composite structures

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Authors: Beier Zhu, Rui Zhang, Qi Song

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Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

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Authors: Alpesh Adeshara, Rajendrasinh Jadeja, Praghnesh Bhatt

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Implementation of advanced technologies requires sophisticated instruments that deal with the operation, control, restoration and protection of rapidly growing power system network under normal and abnormal conditions. Presently, the applications of Phasor Measurement Unit (PMU) are widely found in real time operation, monitoring, controlling and analysis of power system network as it eliminates the various limitations of Supervisory Control and Data Acquisition System (SCADA) conventionally used in power system. The use of PMU data is very rapidly increasing its importance for online and offline analysis. Wide Area Measurement System (WAMS) is developed as new technology by use of multiple PMUs in power system. The present paper proposes a model of MATLAB based PMU using Discrete Fourier Transform (DFT) algorithm and evaluation of its operation under different contingencies. In this paper, PMU based two bus system having WAMS network is presented as a case study.

Keywords: GPS global positioning system, PMU phasor measurement system, WAMS wide area monitoring system, DFT, PDC

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Authors: Hamd Rezaeifar, Hamid Reza Sahriari

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Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data.

Keywords: accident, data mining, neural network, GIS

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Armando Cartenì

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Within the design of sustainable transportation engineering, the problem of reducing particulate matter (PM) concentrations in railways metro system was not much discussed. It is well known that PM levels in railways metro system are mainly produced by mechanical friction at the rail-wheel-brake interactions and by the PM re-suspension caused by the turbulence generated by the train passage, which causes dangerous problems for passenger health. Starting from these considerations, the aim of this research was twofold: i) to investigate the particulate matter concentrations in a ‘traditional’ railways metro system; ii) to investigate the particulate matter concentrations of a ‘high quality’ metro system equipped with design devices useful for reducing PM concentrations: platform screen doors, rubber-tyred and an advanced ventilation system. Two measurement surveys were performed: one in the ‘traditional’ metro system of Naples (Italy) and onother in the ‘high quality’ rubber-tyred metro system of Turin (Italy). Experimental results regarding the ‘traditional’ metro system of Naples, show that the average PM10 concentrations measured in the underground station platforms are very high and range between 172 and 262 µg/m3 whilst the average PM2,5 concentrations range between 45 and 60 µg/m3, with dangerous problems for passenger health. By contrast the measurements results regarding the ‘high quality’ metro system of Turin show that: i) the average PM10 (PM2.5) concentrations measured in the underground station platform is 22.7 µg/m3 (16.0 µg/m3) with a standard deviation of 9.6 µg/m3 (7.6 µg/m3); ii) the indoor concentrations (both for PM10 and for PM2.5) are statistically lower from those measured in outdoors (with a ratio equal to 0.9-0.8), meaning that the indoor air quality is greater than those in urban ambient; iii) that PM concentrations in underground stations are correlated to the trains passage; iv) the inside trains concentrations (both for PM10 and for PM2.5) are statistically lower from those measured at station platform (with a ratio equal to 0.7-0.8), meaning that inside trains the use of air conditioning system could promote a greater circulation that clean the air. The comparison among the two case studies allow to conclude that the metro system designed with PM reduction devices allow to reduce PM concentration up to 11 times against a ‘traditional’ one. From these results, it is possible to conclude that PM concentrations measured in a ‘high quality’ metro system are significantly lower than the ones measured in a ‘traditional’ railway metro systems. This result allows possessing the bases for the design of useful devices for retrofitting metro systems all around the world.

Keywords: air quality, pollutant emission, quality in public transport, underground railway, external cost reduction, transportation planning

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Authors: Abdulrahman Yarali, Machica McClain

Abstract:

In the context of the increasing use of Cyber-Physical Systems (CPS) across critical infrastructure sectors, this paper addresses a crucial and emerging topic: the integration of Identity and Access Management (IAM) with Internet of Things (IoT) devices in Medical Cyber-Physical Systems (MCPS). It underscores the significance of robust IAM solutions in the expanding interconnection of IoT devices in healthcare settings, leveraging AI, ML, DL, Zero Trust Architecture (ZTA), biometric authentication advancements, and blockchain technologies. The paper advocates for the potential benefits of transitioning from traditional, static IAM frameworks to dynamic, adaptive solutions that can effectively counter sophisticated cyber threats, ensure the integrity and reliability of CPS, and significantly bolster the overall security posture. The paper calls for strategic planning, collaboration, and continuous innovation to harness these benefits. By emphasizing the importance of securing CPS against evolving threats, this research contributes to the ongoing discourse on cybersecurity and advocates for a collaborative approach to foster innovation and enhance the resilience of critical infrastructure in the digital era.

Keywords: CPS, IAM, IoT, AI, ML, authentication, models, policies, healthcare

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Authors: Vahid Bairami Rad

Abstract:

The intelligentization of agricultural fields can control the temperature, humidity, and variables affecting the growth of agricultural products online and on a mobile phone or computer. Smarting agricultural fields and gardens is one of the best and best ways to optimize agricultural equipment and has a 100 percent direct effect on the growth of plants and agricultural products and farms. Smart farms are the topic that we are going to discuss today, the Internet of Things and artificial intelligence. Agriculture is becoming smarter every day. From large industrial operations to individuals growing organic produce locally, technology is at the forefront of reducing costs, improving results and ensuring optimal delivery to market. A key element to having a smart agriculture is the use of useful data. Modern farmers have more tools to collect intelligent data than in previous years. Data related to soil chemistry also allows people to make informed decisions about fertilizing farmland. Moisture meter sensors and accurate irrigation controllers have made the irrigation processes to be optimized and at the same time reduce the cost of water consumption. Drones can apply pesticides precisely on the desired point. Automated harvesting machines navigate crop fields based on position and capacity sensors. The list goes on. Almost any process related to agriculture can use sensors that collect data to optimize existing processes and make informed decisions. The Internet of Things (IoT) is at the center of this great transformation. Internet of Things hardware has grown and developed rapidly to provide low-cost sensors for people's needs. These sensors are embedded in IoT devices with a battery and can be evaluated over the years and have access to a low-power and cost-effective mobile network. IoT device management platforms have also evolved rapidly and can now be used securely and manage existing devices at scale. IoT cloud services also provide a set of application enablement services that can be easily used by developers and allow them to build application business logic. Focus on yourself. These development processes have created powerful and new applications in the field of Internet of Things, and these programs can be used in various industries such as agriculture and building smart farms. But the question is, what makes today's farms truly smart farms? Let us put this question in another way. When will the technologies associated with smart farms reach the point where the range of intelligence they provide can exceed the intelligence of experienced and professional farmers?

Keywords: food security, IoT automation, wireless communication, hybrid lifestyle, arduino Uno

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Authors: Yaser Miaji, Mohammed Aloryani

Abstract:

The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

Abstract:

The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: environmental internet of things, EIoT, machine learning, anomaly detection, environment monitoring

Procedia PDF Downloads 151

Authors: Ji-yang Yu, Dan Huang, Guo-ping Feng, Xin Li, Lu-yuan Wang

Abstract:

The navigation satellite network, especially the Beidou MEO Constellation, can relay data effectively with wide coverage and is applied in navigation, detection, and position widely. But the constellation has not been completed, and the amount of satellites on-board is not enough to cover the earth, which makes the data-relay disrupted or delayed in the transition process. The data-relay function needs to tolerant the delay or disruption in some extension, which make the Beidou MEO Constellation a delay/disruption-tolerant network (DTN). The traditional DTN designs mainly employ the relay table as the basic of data path schedule computing. But in practical application, especially in critical condition, such as the war-time or the infliction heavy losses on the constellation, parts of the nodes may become invalid, then the traditional DTN design could be useless. Furthermore, when transmitting the critical message in the navigation system, the maximum priority strategy is used, but the nodes still inquiry the relay table to design the path, which makes the delay more than minutes. Under this circumstances, it needs a function which could compute the optimum data path on-board in real-time according to the constellation states. The on-board critical message transmission design for navigation satellite delay/disruption-tolerant network (DTN) is proposed, according to the characteristics of navigation satellite network. With the real-time computation of parameters in the network link, the least-delay transition path is deduced to retransmit the critical message in urgent conditions. First, the DTN model for constellation is established based on the time-varying matrix (TVM) instead of the time-varying graph (TVG); then, the least transition delay data path is deduced with the parameters of the current node; at last, the critical message transits to the next best node. For the on-board real-time computing, the time delay and misjudges of constellation states in ground stations are eliminated, and the residual information channel for each node can be used flexibly. Compare with the minute’s delay of traditional DTN; the proposed transmits the critical message in seconds, which improves the re-transition efficiency. The hardware is implemented in FPGA based on the proposed model, and the tests prove the validity.

Keywords: critical message, DTN, navigation satellite, on-board, real-time

Procedia PDF Downloads 343