Search results for: electricity network performance

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

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

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

Procedia PDF Downloads 382

Authors: A. D. Tayal

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The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

Procedia PDF Downloads 331

Authors: Sasiphan Nitayaprapha

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A commercially-supported social network has become an emerging channel for an organization to communicate with and provide services to customers. The success of the commercially-supported social network depends on the ability of the organization to keep the customers in participating in the network. Drawing from the theories of information adoption, information systems continuance, and web usability, the author develops a model to explore how a commercially-supported social network can encourage customers to continue participating and using the information in the network. The theoretical model will be proved through an online survey of customers using the commercially-supported social networking sites of several high technology companies operating in the same sector. The result will be compared with previous studies to learn about the explanatory power of the research model, and to identify the main factors determining users’ intention to continue using a commercially-supported social network. Theoretical and practical implications, and limitations are discussed.

Keywords: social network, information adoption, information systems continuance, web usability, user satisfaction

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

Procedia PDF Downloads 547

Authors: Thohaku Abdul Hadi, Hadyan Perdana Putra, Nugroho Wicaksono, Adhika Prajna Nandiwardhana, Onang Surya Nugroho, Heri Suryoatmojo, Soedibjo

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Access to electricity is now a basic requirement of mankind. Unfortunately, there are still many places around the world which have no access to electricity, such as small islands, where there could potentially be a factory, a plantation, a residential area, or resorts. Many of these places might have substantial potential for energy generation such us Photovoltaic (PV) and Wind turbine (WT), which can be used to generate electricity independently for themselves. Solar energy and wind power are renewable energy sources which are mostly found in nature and also kinds of alternative energy that are still developing in a rapid speed to help and meet the demand of electricity. PV and Wind has a characteristic of power depend on solar irradiation and wind speed based on geographical these areas. This paper presented a control methodology of hybrid small scale PV/Wind energy system that use a fuzzy logic controller (FLC) to extract the maximum power point tracking (MPPT) in different solar irradiation and wind speed. This paper discusses simulation and analysis of the generation process of hybrid resources in MPP and power conditioning unit (PCU) of Photovoltaic (PV) and Wind Turbine (WT) that is connected to the three-phase low voltage electricity grid system (380V) without battery storage. The capacity of the sources used is 2.2 kWp PV and 2.5 kW PMSG (Permanent Magnet Synchronous Generator) -WT power rating. The Modeling of hybrid PV/Wind, as well as integrated power electronics components in grid connected system, are simulated using MATLAB/Simulink.

Keywords: fuzzy MPPT, grid connected inverter, photovoltaic (PV), PMSG wind turbine

Procedia PDF Downloads 332

Authors: Cristian F.T. Montenegro, Luís F. N. Lourenço, Maurício B. C. Salles, Renato M. Monaro

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More PV systems have been connected to the electrical network each year. As the number of PV systems increases, some issues affecting grid operations have been identified. This paper studied the impacts related to changes in solar irradiance on a distribution/sub-transmission network, considering variations due to moving clouds and daily cycles. Using MATLAB/Simulink software, a solar farm of 30 MWp was built and then implemented to a test network. From simulations, it has been determined that irradiance changes can have a significant impact on the grid by causing voltage fluctuations outside the allowable thresholds. This work discussed some local control strategies and grid reinforcements to mitigate the negative effects of the irradiance changes on the grid.

Keywords: reactive power control, solar irradiance, utility-scale PV systems, voltage fluctuations

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Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: M. K. Benhaoua, A. K. Singh, A. E. H. Benyamina

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In this paper, we propose heuristic for dynamic communications mapping that considers the placement of communications in order to optimize the overall performance. The mapping technique uses a newly proposed Algorithm to place communications between the tasks. The placement we propose of the communications leads to a better optimization of several performance metrics (time and energy consumption). Experimental results show that the proposed mapping approach provides significant performance improvements when compared to those using static routing.

Keywords: Multi-Processor Systems-on-Chip (MPSoCs), Network-on-Chip (NoC), heterogeneous architectures, dynamic mapping heuristics

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Fina O. Faithpraise, Effiong O. Obisung, Azele E. Peter, Chris R. Chatwin

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This study investigates and identifies consumer attitude as a major influence that results in non-technical losses in the Nigerian electricity supply sector. This discovery is revealed by the combination of quantitative and qualitative research to complete a survey. The dataset employed is a simple random sampling of households using electricity (public power supply), and the number of units chosen is based on statistical power analysis. The units were subdivided into two categories (household with and without electrical meters). The hypothesis formulated was tested and analyzed using a chi-square statistical method. The results obtained shows that the critical value for the household with electrical prepared meter (EPM) was (9.488 < 427.4) and those without electrical prepared meter (EPMn) was (9.488 < 436.1) with a p-value of 0.01%. The analysis demonstrated so far established the real-time position, which shows that the wrong attitude towards handling the electricity supplied (not turning off light bulbs and electrical appliances when not in use within the rooms and outdoors within 12 hours of the day) characterized the non-technical losses in the power sector. Therefore the adoption of efficient lighting attitudes in individual households as recommended by the researcher is greatly encouraged. The results from this study should serve as a model for energy efficiency and use for the improvement of electricity consumption as well as a stable economy.

Keywords: attitudinal change, household, non-technical losses, prepared meter

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Authors: Riyadh Alsultani, Ali Majdi

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It is necessary to develop a design methodology that includes the possibility of seismic events occurring in a region, the vulnerability of the civil hydraulic structure, and the effects of the occurrence hazard on society, environment, and economy in order to evaluate the flood and earthquake risks of coastal bridge networks. This paper presents a design approach for the assessment of the risk and sustainability of coastal bridge networks under time-variant flood-earthquake conditions. The social, environmental, and economic indicators of the network are used to measure its sustainability. These consist of anticipated loss, downtime, energy waste, and carbon dioxide emissions. The design process takes into account the possibility of happening in a set of flood and earthquake scenarios that represent the local seismic activity. Based on the performance of each bridge as determined by fragility assessments, network linkages are measured. The network's connections and bridges' damage statuses after an earthquake scenario determine the network's sustainability and danger. The sustainability measures' temporal volatility and the danger of structural degradation are both highlighted. The method is shown using a transportation network in Baghdad, Iraq.

Keywords: sustainability, Coastal bridge networks, flood-earthquake risk, structural design

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Authors: Latif Adnane, Benaatou Wafa, Pla Vicent

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Handover is of great significance to achieve seamless connectivity in wireless networks. This paper gives an impression of the main factors which are being affected by the soft and the hard handovers techniques. To know and understand the handover process in The Universal Mobile Telecommunications System (UMTS) network, different statistics are calculated. This paper focuses on the quality of service (QoS) of soft and hard handover in UMTS network, which includes the analysis of received power, signal to noise radio, throughput, delay traffic, traffic received, delay, total transmit load, end to end delay and upload response time using OPNET simulator.

Keywords: handover, UMTS, mobility, simulation, OPNET modeler

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Authors: Roya Moradifar, Bijan Honarvar, Masoumeh Zabihi

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The personal residential camps of South Pars gas complex are one of the few places where electric energy is used for the bath water heating. The widespread use of these devices is mainly responsible for the high peak of the electricity demand in the residential sector. In an attempt to deal with this issue, to reduce the electricity usage of the hot water, as an option, solar hot water systems have been proposed. However, despite the high incidence of solar radiation on the Assaloyeh about 20 MJ/m²/day, currently, there is no technical assessment quantifying the economic benefits on the region. The present study estimates the economic impacts resulting by the deployment of solar hot water systems in residential camp. Hence, the feasibility study allows assessing the potential of solar water heating as an alternative to reduce the peak on the electricity demand. In order to examine the potential of using solar energy in Bidkhoon residential camp two solar water heater packages as pilots were installed for restaurant and building. Restaurant package was damaged due to maintenance problems, but for the building package, we achieved the result of the solar fraction total 83percent and max energy saving 2895 kWh, the maximum reduction in CO₂ emissions calculated as 1634.5 kg. The results of this study can be used as a support tool to spread the use solar water heaters and create policies for South Pars Gas Complex.

Keywords: electrical energy, hot water, solar, South Pars Gas complex

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Authors: Mohammad Parvaresh, Mahdi Babaee, Bahareh Arghand, Roushanak Fahimi Hanzaee, Davood Nourmohammadi

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Today, the intelligent network of water and wastewater as one of the key steps in realizing the smart city in the world. Use of pressure relief valves in urban water networks in order to reduce the pressure is necessary in Tehran city. But use these pressure relief valves lead to waste water, more power consumption, and environmental pollution because Tehran Province Water and Wastewater Co. use a quarter of industry 's electricity. In this regard, Tehran Province Water and Wastewater Co. identified solutions to reduce direct and indirect costs in energy use in the process of production, transmission and distribution of water because this company has extensive facilities and high capacity to realize green economy and industry. The aim of this study is to analyze the new project in water and wastewater industry to reach sustainable development.

Keywords: Tehran Province Water and Wastewater Company, water network efficiency, sustainable development, International Environmental Law

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Authors: Kumar Manoj, Ramesh Kumar, Kumari Arti, Kumar Prashant

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Routing protocols have a central role in any mobile ad hoc network (MANET). There are many routing protocols that exhibit different performance levels in different scenarios. In this paper we compare AODV, DSDV, DSR and ZRP routing protocol in mobile ad hoc networks to determine the best operational conditions for each protocol. We analyses these routing protocols by extensive simulations in OPNET simulator and show that how pause time and the number of nodes affect their performance. In this study, performance is measured in terms of control traffic received, control traffic sent, data traffic received, data traffic sent, throughput, retransmission attempts.

Keywords: MANET, AODV, DSDV, DSR, ZRP

Procedia PDF Downloads 640

Authors: Usman Abubakar, Xiaoyuan Wang, Sayyed Haleem Shah, Sadiq Ur Rahman, Rabiu Saleh Zakariyya

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High-speed Permanent magnet synchronous machine (HSPMSM) uses in different industrial applications like blowers, compressors as a result of its superb performance. Nevertheless, the over-temperature rise of both winding and PM is one of their substantial problem for a high-power HSPMSM, which affects its lifespan and performance. According to the literature, HSPMSM with a Hybrid cooling configuration has a much lower temperature rise than non-hybrid cooling. This paper presents the design 185kW, 26K rpm with two different cooling configurations, i.e., hybrid cooling configuration (forced air and housing spiral water jacket) and non-hybrid (forced air cooling assisted with winding’s potting material and sleeve’s material) to enhance the heat dissipation of winding and PM respectively. Firstly, the machine’s electromagnetic design is conducted by the finite element method to accurately account for machine losses. Then machine’s cooling configurations are introduced, and their effectiveness is validated by lumped parameter thermal network (LPTN). Investigation shows that using potting, sleeve materials to assist non-hybrid cooling configuration makes the machine’s winding and PM temperature closer to hybrid cooling configuration. Therefore, the machine with non-hybrid cooling is prototyped and tested due to its simplicity, lower energy consumption and can still maintain the lifespan and performance of the HSPMSM.

Keywords: airflow network, axial ventilation, high-speed PMSM, thermal network

Procedia PDF Downloads 201

Authors: Waleed Basuliman

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Anthropometric dimensions are considered one of the important factors when designing human-machine systems. In this study, the estimation of anthropometric dimensions has been improved by using Artificial Neural Network (ANN) model that is able to predict the anthropometric measurements of Saudi males in Riyadh City. A total of 1427 Saudi males aged 6 to 60 years participated in measuring 20 anthropometric dimensions. These anthropometric measurements are considered important for designing the work and life applications in Saudi Arabia. The data were collected during eight months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining 15 dimensions were set to be the measured variables (Model’s outcomes). The hidden layers varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was able to estimate the body dimensions of Saudi male population in Riyadh City. The network's mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found to be 0.0348 and 3.225, respectively. These results were found less, and then better, than the errors found in the literature. Finally, the accuracy of the developed neural network was evaluated by comparing the predicted outcomes with regression model. The ANN model showed higher coefficient of determination (R2) between the predicted and actual dimensions than the regression model.

Keywords: artificial neural network, anthropometric measurements, back-propagation

Procedia PDF Downloads 465

Authors: K. Touafek, A. Khelifa, E. H. Khettaf, A. Embarek

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Hybrid photovoltaic thermal (PV/T) solar system comprises a solar collector which is disposed on photovoltaic solar cells. The disadvantage of a conventional photovoltaic cell is that its performance decreases as the temperature increases. Indeed, part of the solar radiation is converted into electricity and is dissipated as heat, increasing the temperature of the photovoltaic cell with respect to the ambient temperature. The objective of this work is to study experimentally and implement a hybrid prototype to evaluate electrical and thermal performance. In this paper, an experimental study of two new configurations of hybrid collectors is exposed. The results are given and interpreted. The two configurations of absorber studied are a new combination with tubes and galvanized tank, the other is a tubes and sheet.

Keywords: experimental, photovoltaic, solar, temperature

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Authors: Mohamed Othmani, Yassine Khlifi

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This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets.

Keywords: 3d object, optimization, parametrization, polywog wavelets, reconstruction, wavelet networks

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Authors: David A. Wallace, Salakjit J. Nilboworn

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Secure and reliable transmission and distribution of electrical power is crucial in today’s ever-increasing demand for electricity. Traditional methods of protecting the electrical grid have relied on relaying systems receiving voltage and current inputs from inductive instruments transformers (IT). This method has provided robust and stable performance throughout the years. Today with the advent of new non-conventional transformers (NCIT) and sensors, the electrical landscape is changing. These new systems have to ability to provide the same electrical performance as traditional instrument transformers with the added features of data acquisition, communication, smaller footprint, lower cost and resistance to GMD/GIC events.

Keywords: non-conventional instrument transformers, digital substations, smart grids, micro-grids

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Authors: Taiheng Zhang, Hongbin Zhao

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Compressed air energy storage become increasingly vital for solving intermittency problem of some renewable energies. In this study, a new hybrid system on a combination of compressed air energy storage (CAES), solid oxide fuel cell (SOFC), gas turbine (GT), and organic Rankine cycle (ORC) is proposed. In the new system, excess electricity during off-peak time is utilized to compress air. Then, the compressed air is stored in compressed air storage tank. During peak time, the compressed air enters the cathode of SOFC directly instead of combustion chamber of traditional CAES. There is no air compressor consumption of SOFC-GT in peak demand, so SOFC- GT can generate power with high-efficiency. In addition, the waste heat of exhaust from GT is recovered by applying an ORC. Three different organic working fluid (R123, R601, R601a) of ORC are chosen to evaluate system performance. Based on Aspen plus and Engineering Equation Solver (EES) software, energy and exergoeconomic analysis are used to access the viability of the combined system. Besides, the effect of two parameters (fuel flow and ORC turbine inlet pressure) on energy efficiency is studied. The effect of low-price electricity at off-peak hours on thermodynamic criteria (total unit exergy cost of products and total cost rate) is also investigated. Furthermore, for three different organic working fluids, the results of round-trip efficiency, exergy efficiency, and exergoeconomic factors are calculated and compared. Based on thermodynamic performance and exergoeconomic performance of different organic working fluids, the best suitable working fluid will be chosen. In conclusion, this study can provide important guidance for system efficiency improvement and viability.

Keywords: CAES, SOFC, ORC, energy and exergoeconomic analysis, organic working fluids

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Authors: Amro Issam Hamed Attia Ramadan, Marco Zappatore, Pasquale Balena, Antonella Longo

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Considering the energetic crisis that is hitting Europe, it becomes more and more necessary to change the energy policies to depend less on fossil fuels and replace them with energy from renewable sources. This has triggered the urge to use clean energy not only to satisfy energy needs and fulfill the required consumption but also to decrease the danger of climatic changes due to harmful emissions. Many countries have already started creating energetic communities based on renewable energy sources. The first step to understanding energy needs in any place is to perfectly know the consumption. In this work, we aim to estimate electricity consumption for a municipality that makes up part of a rural area located in southern Italy using forecast models that allow for the estimation of electricity consumption for the next ten years, and we then apply the same model to the province where the municipality is located and estimate the future consumption for the same period to examine whether it is possible to start from the municipality level to reach the province level when creating energy communities.

Keywords: ARIMA, electricity consumption, forecasting models, time series

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Authors: Bahareh Asefi, Fereidoun Farzaneh, Ghazaleh Asefi, Chang-Ping Yu

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Increased production of food waste (FW) is a global issue that is receiving more attention due to its environmental and economic impacts. The generation of electricity from food waste, known as energy recovery, is one of the effective solutions in food waste management. Food waste has high energy content which seems ideal to achieve dual benefits in terms of energy recovery and waste stabilization. Microbial fuel cell (MFC) is a promising technology for treating food waste and generate electricity. In this work, we will review energy utilization from different kind of food waste using MFC and factors which affected the process. We have studied the key technology of energy generated from food waste using MFC to enhance the food waste management. The power density and electricity production by each kind of food waste and challenges were identified. This work explored the conversion of FW into energy from different type of food waste, which aim to provide a theoretical analysis for energy utilization of food waste.

Keywords: energy generation, food waste, microbial fuel cell, power density

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Authors: Bashir Abdu Muzakkari, Mohamad Afendee Mohamad, Mohd Fadzil Abdul Kadir

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Wireless Sensor Networks (WSNs) is an aggregation of several tiny, low-cost sensor nodes, spatially distributed to monitor physical or environmental status. WSN is constantly changing because of the rapid technological advancements in sensor elements such as radio, battery and operating systems. The Medium Access Control (MAC) protocols remain very vital in the WSN because of its role in coordinating communication amongst the sensors. Other than battery consumption, packet collision, network lifetime and latency are factors that largely depend on WSN MAC protocol and these factors have been widely treated in recent days. In this paper, we survey some latest proposed WSN Contention-based, Scheduling-based and Hybrid MAC protocols while presenting an examination, correlation of advantages and limitations of each protocol. Concentration is directed towards investigating the treatment of Quality of Service (QoS) performance metrics within these particular protocols. The result shows that majority of the protocols leaned towards energy conservation. We, therefore, believe that other performance metrics of guaranteed QoS such as latency, throughput, packet loss, network and bandwidth availability may play a critical role in the design of future MAC protocols for WSNs.

Keywords: WSN, QoS, energy consumption, MAC protocol

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Authors: Jian Zheng

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Three-dimensional (3D) network structure has been recognized as an effective approach to enhance the mechanical and thermal conductive properties of polymeric composites. However, it has not been applied in energetic materials. In this work, a fluoropolymer based composite with vertically oriented and interconnected 3D graphite network was fabricated for polymer bonded explosives (PBXs). Here, the graphite and graphene oxide platelets were mixed, and self-assembled via rapid freezing and using crystallized ice as the template. The 3D structure was finally obtained by freezing-dry and infiltrating with the polymer. With the increasing of filler fraction and cooling rate, the thermal conductivity of the polymer composite was significantly improved to 2.15 W m⁻¹ K⁻¹ by 1094% than that of pure polymer. Moreover, the mechanical properties, such as tensile strength and elastic modulus, were enhanced by 82% and 310%, respectively, when the highly ordered structure was embedded in the polymer. We attribute the increased thermal and mechanical properties to this 3D network, which is beneficial to the effective heat conduction and force transfer. This study supports a desirable way to fabricate the strong and thermal conductive fluoropolymer composites used for the high-performance polymer bonded explosives (PBXs).

Keywords: mechanical properties, oriented network, graphite polymer composite, thermal conductivity

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Authors: Wei Wang, Yilun Xu

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With the development of knowledge economy era, deepening the mechanism of cooperation and adhering to sharing and win-win cooperation has become new direction of urban development nowadays. In recent years, innovative collaborations between cities are becoming more and more frequent, whose influence on urban network space has aroused many scholars' attention. Taking 46 cities in China as the research object, the paper builds the connectivity of innovative network between cities and the linkages of urban external innovation using patent cooperation data among cities, and explores urban network space in China by the application of GIS, which is a beneficial exploration to the study of social network space in China in the era of information network. The result shows that the urban innovative network space and geographical entity space exist differences, and the linkages of external innovation are not entirely related to the city innovative capacity and the level of economy development. However, urban innovative network space and geographical entity space are similar in hierarchical clustering. They have both formed Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta three metropolitan areas and Beijing-Shenzhen-Shanghai-Hangzhou four core cities, which lead the development of innovative network space in China.

Keywords: innovative collaboration, urban network space, the connectivity of innovative network, the linkages of external innovation

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

Abstract:

Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

Procedia PDF Downloads 145

Authors: Abhishek, Seema Devi, Jyoti Ohri

Abstract:

A mobile ad hoc network (MANET) represents a system of wireless mobile nodes that can self-organize freely and dynamically into arbitrary and temporary network topology. Unlike a wired network, wireless network interface has limited transmission range. Routing is the task of forwarding data packets from source to a given destination. Ad-hoc On Demand Distance Vector (AODV) routing protocol creates a path for a destination only when it required. This paper describes the implementation of AODV routing protocol using MATLAB-based Truetime simulator. In MANET's node movements are not fixed while they are random in nature. Hence intelligent techniques i.e. fuzzy and ANFIS are used to optimize the transmission range. In this paper, we compared the transmission range of AODV, fuzzy AODV and ANFIS AODV. For soft computing AODV, we have taken transmitted power and received threshold as input and transmission range as output. ANFIS gives better results as compared to fuzzy AODV.

Keywords: ANFIS, AODV, fuzzy, MANET, reactive routing protocol, routing protocol, truetime

Procedia PDF Downloads 464

Authors: Saeed Alzahrani, Yaser Miaji

Abstract:

The project involves the design and simulation of a Mobile Cellular Telecommunication Network using the software tool CelPlanner. The design is mainly concerned with Global System for Mobile Communications . The design and simulation of the network is done for a small part of the area allocated for us in the terrain area of Shreveport city .The project is concerned with designing a network that is cost effective and which also efficiently meets the required Grade of Service (GOS) AND Quality of Service (QOS).The expected outcome of this project is the design of a network that gives a good coverage for the area allocated to us with minimum co-channel interference and adjacent channel interference. The Handover and Traffic Handling Capacity should also be taken into consideration and should be good for the given area . The Traffic Handling Capacity of the network in a way decides whether the designed network is good or bad . The design also takes into consideration the topographical and morphological information.

Keywords: mobile communication, GSM, radio base station, network planning

Procedia PDF Downloads 416