Search results for: network transformation
5472 Performance Analysis of Ad-Hoc Network Routing Protocols
Authors: I. Baddari, A. Riahla, M. Mezghich
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Today in the literature, we discover a lot of routing algorithms which some have been the subject of normalization. Two great classes Routing algorithms are defined, the first is the class reactive algorithms and the second that of algorithms proactive. The aim of this work is to make a comparative study between some routing algorithms. Two comparisons are considered. The first will focus on the protocols of the same class and second class on algorithms of different classes (one reactive and the other proactive). Since they are not based on analytical models, the exact evaluation of some aspects of these protocols is challenging. Simulations have to be done in order to study their performances. Our simulation is performed in NS2 (Network Simulator 2). It identified a classification of the different routing algorithms studied in a metrics such as loss of message, the time transmission, mobility, etc.Keywords: ad-hoc network routing protocol, simulation, NS2, delay, packet loss, wideband, mobility
Procedia PDF Downloads 3925471 Attention Based Fully Convolutional Neural Network for Simultaneous Detection and Segmentation of Optic Disc in Retinal Fundus Images
Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara
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Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.Keywords: attention-based fully convolutional network, optic disc detection and segmentation, retinal fundus image, screening of ocular diseases
Procedia PDF Downloads 1325470 Utilization of Secure Wireless Networks as Environment for Learning and Teaching in Higher Education
Authors: Mohammed A. M. Ibrahim
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This paper investigate the utilization of wire and wireless networks to be platform for distributed educational monitoring system. Universities in developing countries suffer from a lot of shortages(staff, equipment, and finical budget) and optimal utilization of the wire and wireless network, so universities can mitigate some of the mentioned problems and avoid the problems that maybe humble the education processes in many universities by using our implementation of the examinations system as a test-bed to utilize the network as a solution to the shortages for academic staff in Taiz University. This paper selects a two areas first one quizzes activities is only a test bed application for wireless network learning environment system to be distributed among students. Second area is the features and the security of wireless, our tested application implemented in a promising area which is the use of WLAN in higher education for leering environment.Keywords: networking wire and wireless technology, wireless network security, distributed computing, algorithm, encryption and decryption
Procedia PDF Downloads 3295469 mKDNAD: A Network Flow Anomaly Detection Method Based On Multi-teacher Knowledge Distillation
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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning
Procedia PDF Downloads 1125468 Measuring Delay Using Software Defined Networks: Limitations, Challenges, and Suggestions for Openflow
Authors: Ahmed Alutaibi, Ganti Sudhakar
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Providing better Quality-of-Service (QoS) to end users has been a challenging problem for researchers and service providers. Building applications relying on best effort network protocols hindered the adoption of guaranteed service parameters and, ultimately, Quality of Service. The introduction of Software Defined Networking (SDN) opened the door for a new paradigm shift towards a more controlled programmable configurable behavior. Openflow has been and still is the main implementation of the SDN vision. To facilitate better QoS for applications, the network must calculate and measure certain parameters. One of those parameters is the delay between the two ends of the connection. Using the power of SDN and the knowledge of application and network behavior, SDN networks can adjust to different conditions and specifications. In this paper, we use the capabilities of SDN to implement multiple algorithms to measure delay end-to-end not only inside the SDN network. The results of applying the algorithms on an emulated environment show that we can get measurements close to the emulated delay. The results also show that depending on the algorithm, load on the network and controller can differ. In addition, the transport layer handshake algorithm performs best among the tested algorithms. Out of the results and implementation, we show the limitations of Openflow and develop suggestions to solve them.Keywords: software defined networking, quality of service, delay measurement, openflow, mininet
Procedia PDF Downloads 1575467 Indoor Temperature Estimation with FIR Filter Using R-C Network Model
Authors: Sung Hyun You, Jeong Hoon Kim, Dae Ki Kim, Choon Ki Ahn
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In this paper, we proposed a new strategy for estimating indoor temperature based on the modified resistance capacitance (R–C) network thermal dynamic model. Using minimum variance finite impulse response (FIR) filter, accurate indoor temperature estimation can be achieved. Our study is clarified by the experimental validation of the proposed indoor temperature estimation method. This experiment scenario environment is composed of a demand response (DR) server and home energy management system (HEMS) in a test bed.Keywords: energy consumption, resistance-capacitance network model, demand response, finite impulse response filter
Procedia PDF Downloads 4385466 Tail-Binding Effect of Kinesin-1 Auto Inhibition Using Elastic Network Model
Authors: Hyun Joon Chang, Jae In Kim, Sungsoo Na
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Kinesin-1 (hereafter called kinesin) is a molecular motor protein that moves cargos toward the end of microtubules using the energy of adenosine triphosphate (ATP) hydrolysis. When kinesin is inactive, its tail autoinhibits the motor chain in order to prevent from reacting with the ATP by cross-linking of the tail domain to the motor domains at two positions. However, the morphological study of kinesin during autoinhibition is yet remained obscured. In this study, we report the effect of the binding site of the tail domain using the normal mode analysis of the elastic network model on kinesin in the tail-free form and tail-bind form. Considering the relationship between the connectivity of conventional network model with respect to the cutoff length and the functionality of the binding site of the tail, we revaluated the network model to observe the key role of the tail domain in its structural aspect. Contingent on the existence of the tail domain, the results suggest the morphological stability of the motor domain. Furthermore, employing the results from normal mode analysis, we have determined the strain energy of the neck linker, an essential portion of the motor domain for ATP hydrolysis. The results of the neck linker also converge to the same indication, i.e. the morphological analysis of the motor domain.Keywords: elastic network model, Kinesin-1, autoinhibition
Procedia PDF Downloads 4465465 Real Time Traffic Performance Study over MPLS VPNs with DiffServ
Authors: Naveed Ghani
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With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2
Procedia PDF Downloads 4195464 Distributed Automation System Based Remote Monitoring of Power Quality Disturbance on LV Network
Authors: Emmanuel D. Buedi, K. O. Boateng, Griffith S. Klogo
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Electrical distribution networks are prone to power quality disturbances originating from the complexity of the distribution network, mode of distribution (overhead or underground) and types of loads used by customers. Data on the types of disturbances present and frequency of occurrence is needed for economic evaluation and hence finding solution to the problem. Utility companies have resorted to using secondary power quality devices such as smart meters to help gather the required data. Even though this approach is easier to adopt, data gathered from these devices may not serve the required purpose, since the installation of these devices in the electrical network usually does not conform to available PQM placement methods. This paper presents a design of a PQM that is capable of integrating into an existing DAS infrastructure to take advantage of available placement methodologies. The monitoring component of the design is implemented and installed to monitor an existing LV network. Data from the monitor is analyzed and presented. A portion of the LV network of the Electricity Company of Ghana is modeled in MATLAB-Simulink and analyzed under various earth fault conditions. The results presented show the ability of the PQM to detect and analyze PQ disturbance such as voltage sag and overvoltage. By adopting a placement methodology and installing these nodes, utilities are assured of accurate and reliable information with respect to the quality of power delivered to consumers.Keywords: power quality, remote monitoring, distributed automation system, economic evaluation, LV network
Procedia PDF Downloads 3425463 Developing Pavement Maintenance Management System (PMMS) for Small Cities, Aswan City Case Study
Authors: Ayman Othman, Tallat Ali
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A pavement maintenance management system (PMMS) was developed for the city of Aswan as a model of a small city to provide the road maintenance department in Aswan city with the capabilities for comprehensive planning of the maintenance activities needed to put the internal pavement network into desired physical condition in view of maintenance budget constraints. The developed system consists of three main stages. First is the inventory & condition survey stage where the internal pavement network of Aswan city was inventoried and its actual conditions were rated in segments of 100 meters length. Second is the analysis stage where pavement condition index (PCI) was calculated and the most appropriate maintenance actions were assigned for each segment. The total maintenance budget was also estimated and a parameter based ranking criteria were developed to prioritize maintenance activities when the available maintenance budget is not sufficient. Finally comes the packaging stage where approved maintenance budget is packed into maintenance projects for field implementation. System results indicate that, the system output maintenance budget is very reasonable and the system output maintenance programs agree to a great extent with the actual maintenance needs of the network. Condition survey of Aswan city road network showed that roughness is the most dominate distress. In general, the road network can be considered in a fairly reasonable condition, however, the developed PMMS needs to be officially adapted to maintain the road network in a desirable condition and to prevent further deterioration.Keywords: pavement, maintenance, management, system, distresses, survey, ranking
Procedia PDF Downloads 2395462 A Memetic Algorithm Approach to Clustering in Mobile Wireless Sensor Networks
Authors: Masood Ahmad, Ataul Aziz Ikram, Ishtiaq Wahid
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Wireless sensor network (WSN) is the interconnection of mobile wireless nodes with limited energy and memory. These networks can be deployed formany critical applications like military operations, rescue management, fire detection and so on. In flat routing structure, every node plays an equal role of sensor and router. The topology may change very frequently due to the mobile nature of nodes in WSNs. The topology maintenance may produce more overhead messages. To avoid topology maintenance overhead messages, an optimized cluster based mobile wireless sensor network using memetic algorithm is proposed in this paper. The nodes in this network are first divided into clusters. The cluster leaders then transmit data to that base station. The network is validated through extensive simulation study. The results show that the proposed technique has superior results compared to existing techniques.Keywords: WSN, routing, cluster based, meme, memetic algorithm
Procedia PDF Downloads 4755461 Foggy Image Restoration Using Neural Network
Authors: Khader S. Al-Aidmat, Venus W. Samawi
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Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration
Procedia PDF Downloads 3755460 Constructing a Physics Guided Machine Learning Neural Network to Predict Tonal Noise Emitted by a Propeller
Authors: Arthur D. Wiedemann, Christopher Fuller, Kyle A. Pascioni
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With the introduction of electric motors, small unmanned aerial vehicle designers have to consider trade-offs between acoustic noise and thrust generated. Currently, there are few low-computational tools available for predicting acoustic noise emitted by a propeller into the far-field. Artificial neural networks offer a highly non-linear and adaptive model for predicting isolated and interactive tonal noise. But neural networks require large data sets, exceeding practical considerations in modeling experimental results. A methodology known as physics guided machine learning has been applied in this study to reduce the required data set to train the network. After building and evaluating several neural networks, the best model is investigated to determine how the network successfully predicts the acoustic waveform. Lastly, a post-network transfer function is developed to remove discontinuity from the predicted waveform. Overall, methodologies from physics guided machine learning show a notable improvement in prediction performance, but additional loss functions are necessary for constructing predictive networks on small datasets.Keywords: aeroacoustics, machine learning, propeller, rotor, neural network, physics guided machine learning
Procedia PDF Downloads 2125459 Managing Organizational Change for a Transformation Project: The Billing and Customer Relationship Management Journey
Authors: Sharifah I. N. A. Syed Azmi, Nazarina Mohd Nasir
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The Billing & Customer Relationship Management (BCRM) project is an important enabler towards realizing customer experience transformation. It involves technological shifts for future scalability, revision of multiple business processes and adoption of change by the users and impacted employees. This massive transition, if not managed properly, may result in the decline of business performance due to productivity drop. Organizational change management is an essential element in BCRM project implementation to ensure the system is well understood and embraced by all stakeholders. In order to move impacted employees from unaware state or denial mode to full-acceptance mindset and committing themselves in using the new system, their involvement in the whole change process starting from the initial stage is imperative. Through the BCRM Change Management Plan, a holistic approach was taken whereby the strategy and program for five key components namely executive sponsorship, continuous communication, process change readiness, organizational readiness and individual readiness were all carefully established. Roles of the project sponsor, change agents, change ambassadors and community of practice (CoP) were clearly defined in gaining high commitment and support across the entire organization. Continuous communication and engagement initiatives throughout project implementation have been carried out to reach all stakeholders. The business readiness was constantly monitored and assessed including effectiveness of end-user training, thorough review of process documentation and completion of roles realignment exercise.Keywords: BCRM, change management, organizational change, transformation project
Procedia PDF Downloads 1295458 A Numerical Solution Based on Operational Matrix of Differentiation of Shifted Second Kind Chebyshev Wavelets for a Stefan Problem
Authors: Rajeev, N. K. Raigar
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In this study, one dimensional phase change problem (a Stefan problem) is considered and a numerical solution of this problem is discussed. First, we use similarity transformation to convert the governing equations into ordinary differential equations with its boundary conditions. The solutions of ordinary differential equation with the associated boundary conditions and interface condition (Stefan condition) are obtained by using a numerical approach based on operational matrix of differentiation of shifted second kind Chebyshev wavelets. The obtained results are compared with existing exact solution which is sufficiently accurate.Keywords: operational matrix of differentiation, similarity transformation, shifted second kind chebyshev wavelets, stefan problem
Procedia PDF Downloads 3945457 Sensor Validation Using Bottleneck Neural Network and Variable Reconstruction
Authors: Somia Bouzid, Messaoud Ramdani
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The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a detection and diagnosis sensor faults method based on a Bottleneck Neural Network (BNN). The BNN approach is used as a statistical process control tool for drinking water distribution (DWD) systems to detect and isolate the sensor faults. Variable reconstruction approach is very useful for sensor fault isolation, this method is validated in simulation on a nonlinear system: actual drinking water distribution system. Several results are presented.Keywords: fault detection, localization, PCA, NLPCA, auto-associative neural network
Procedia PDF Downloads 3775456 Transport Related Air Pollution Modeling Using Artificial Neural Network
Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar
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Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling
Procedia PDF Downloads 5135455 MegaProjects and the Governing Processes That Lead to Success and Failure: A Literature Review
Authors: Fangwei Zhu, Wei Tian, Linzhuo Wang, Miao Yu
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Megaproject has long been a critical issue in project governance, for its low success rate and large impact on society. Although the extant literature on megaproject governance is vast, to our best knowledge, the lacking of a thorough literature review makes it hard for us to gain a holistic view on current scenario of megaproject governance. The study conducts a systematic literature review process to analyze the existing literatures on megaproject governance. The finding indicates that mega project governance needs to be handled at network level and forming a network level governance provides a holistic framework for governing megaproject towards sustainable development of the projects. Theoretical and practical implications, as well as future studies and limitations, were discussed.Keywords: megaproject, governance, literature review, network
Procedia PDF Downloads 1905454 Robust Diagnosis of an Electro-Mechanical Actuators, Bond Graph LFT Approach
Authors: A. Boulanoir, B. Ould Bouamama, A. Debiane, N. Achour
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The paper deals with robust Fault Detection and isolation with respect to parameter uncertainties based on linear fractional transformation form (LFT) Bond graph. The innovative interest of the proposed methodology is the use only one representation for systematic generation of robust analytical redundancy relations and adaptive residual thresholds for sensibility analysis. Furthermore, the parameter uncertainties are introduced graphically in the bond graph model. The methodology applied to the nonlinear industrial Electro-Mechanical Actuators (EMA) used in avionic systems, has determined first the structural monitorability analysis (which component can be monitored) with given instrumentation architecture with any need of complex calculation and secondly robust fault indicators for online supervision.Keywords: bond graph (BG), electro mechanical actuators (EMA), fault detection and isolation (FDI), linear fractional transformation (LFT), mechatronic systems, parameter uncertainties, avionic system
Procedia PDF Downloads 3425453 Predicting the Success of Bank Telemarketing Using Artificial Neural Network
Authors: Mokrane Selma
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The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network
Procedia PDF Downloads 1435452 The Effect of Material Properties and Volumetric Changes in Phase Transformation to the Final Residual Stress of Welding Process
Authors: Djarot B. Darmadi
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The wider growing Finite Element Method (FEM) application is caused by its benefits of cost saving and environment friendly. Also, by using FEM a deep understanding of certain phenomenon can be achieved. This paper observed the role of material properties and volumetric change when Solid State Phase Transformation (SSPT) takes place in residual stress formation due to a welding process of ferritic steels through coupled Thermo-Metallurgy-Mechanical (TMM) analysis. The correctness of FEM residual stress prediction was validated by experiment. From parametric study of the FEM model, it can be concluded that the material properties change tend to over-predicts residual stress in the weld center whilst volumetric change tend to underestimates it. The best final result is the compromise of both by incorporates them in the model which has a better result compared to a model without SSPT.Keywords: residual stress, ferritic steels, SSPT, coupled-TMM
Procedia PDF Downloads 2645451 Forecast of Polyethylene Properties in the Gas Phase Polymerization Aided by Neural Network
Authors: Nasrin Bakhshizadeh, Ashkan Forootan
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A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties.Keywords: polyethylene, polymerization, density, melt index, neural network
Procedia PDF Downloads 1365450 Analysis and Prediction of COVID-19 by Using Recurrent LSTM Neural Network Model in Machine Learning
Authors: Grienggrai Rajchakit
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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning
Procedia PDF Downloads 1495449 Video-On-Demand QoE Evaluation across Different Age-Groups and Its Significance for Network Capacity
Authors: Mujtaba Roshan, John A. Schormans
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Quality of Experience (QoE) drives churn in the broadband networks industry, and good QoE plays a large part in the retention of customers. QoE is known to be affected by the Quality of Service (QoS) factors packet loss probability (PLP), delay and delay jitter caused by the network. Earlier results have shown that the relationship between these QoS factors and QoE is non-linear, and may vary from application to application. We use the network emulator Netem as the basis for experimentation, and evaluate how QoE varies as we change the emulated QoS metrics. Focusing on Video-on-Demand, we discovered that the reported QoE may differ widely for users of different age groups, and that the most demanding age group (the youngest) can require an order of magnitude lower PLP to achieve the same QoE than is required by the most widely studied age group of users. We then used a bottleneck TCP model to evaluate the capacity cost of achieving an order of magnitude decrease in PLP, and found it be (almost always) a 3-fold increase in link capacity that was required.Keywords: network capacity, packet loss probability, quality of experience, quality of service
Procedia PDF Downloads 2685448 Performance Evaluation of DSR and OLSR Routing Protocols in MANET Using Varying Pause Time
Authors: Yassine Meraihi, Dalila Acheli, Rabah Meraihi
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MANET for Mobile Ad hoc NETwork is a collection of wireless mobile nodes that communicates with each other without using any existing infrastructure, access point or centralized administration, due to the higher mobility and limited radio transmission range, routing is an important issue in ad hoc network, so in order to ensure reliable and efficient route between to communicating nodes quickly, an appropriate routing protocol is needed. In this paper, we present the performance analysis of two mobile ad hoc network routing protocols namely DSR and OLSR using NS2.34, the performance is determined on the basis of packet delivery ratio, throughput, average jitter and end to end delay with varying pause time.Keywords: DSR, OLSR, quality of service, routing protocols, MANET
Procedia PDF Downloads 5435447 A Neural Network for the Prediction of Contraction after Burn Injuries
Authors: Ginger Egberts, Marianne Schaaphok, Fred Vermolen, Paul van Zuijlen
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A few years ago, a promising morphoelastic model was developed for the simulation of contraction formation after burn injuries. Contraction can lead to a serious reduction in physical mobility, like a reduction in the range-of-motion of joints. If this is the case in a healing burn wound, then this is referred to as a contracture that needs medical intervention. The morphoelastic model consists of a set of partial differential equations describing both a chemical part and a mechanical part in dermal wound healing. These equations are solved with the numerical finite element method (FEM). In this method, many calculations are required on each of the chosen elements. In general, the more elements, the more accurate the solution. However, the number of elements increases rapidly if simulations are performed in 2D and 3D. In that case, it not only takes longer before a prediction is available, the computation also becomes more expensive. It is therefore important to investigate alternative possibilities to generate the same results, based on the input parameters only. In this study, a surrogate neural network has been designed to mimic the results of the one-dimensional morphoelastic model. The neural network generates predictions quickly, is easy to implement, and there is freedom in the choice of input and output. Because a neural network requires extensive training and a data set, it is ideal that the one-dimensional FEM code generates output quickly. These feed-forward-type neural network results are very promising. Not only can the network give faster predictions, but it also has a performance of over 99%. It reports on the relative surface area of the wound/scar, the total strain energy density, and the evolutions of the densities of the chemicals and mechanics. It is, therefore, interesting to investigate the applicability of a neural network for the two- and three-dimensional morphoelastic model for contraction after burn injuries.Keywords: biomechanics, burns, feasibility, feed-forward NN, morphoelasticity, neural network, relative surface area wound
Procedia PDF Downloads 475446 Conceptual Knowledge Structure Updates after Instructor Provided Structural Feedback: An Exploratory Study Applied with Undergraduate Architectural Engineering Students
Authors: Roy B. Clariana, Ryan L. Solnosky
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Structural feedback is any form of feedback that aims to improve the quality of students’ domain-normative conceptual interrelationships. Research with structural feedback points to the potential mediating role of network graphs as feedback for tuning students’ conceptual understanding; for example, improved content knowledge and motivation were observed for undergraduate students who accessed the instructor’s networks of course content. This exploratory study uses a one-group pretest-posttest design to examine the effects of instructor-provided network feedback during lectures on students’ knowledge structure measured using a concept sorting task at the pretest and posttest. Undergraduate students in an architectural engineering course (n = 32) completed a lesson module and then an end-of-unit quiz on building with wood and wood framing. Three weeks later, as a review, students completed a sorting task that used 26 terms from that lesson, then a week later, the sorting task data were used to create a group-average network, this network along with the instructor’s expert network were added to that week’s lecture slides and were compared and discussed during class time. A week later, students completed the sorting task again. The pre and post-sorting data were rendered into pathfinder networks, and then these students’ networks were compared to five referent networks, specifically the textbook chapter network, the lecture slides network, a network of the end-of-unit quiz, the actual expert network that served as the feedback intervention, and the group-average network. Inspection of means shows that knowledge structure measures improved for all five measures from pre-to-post, becoming more like the lesson content and like the expert. Repeated measures analysis with follow-up paired samples t-tests showed pre-to-post significant increases for both the end-of-unit quiz and the expert network referents. The findings show that instructor presentation of structural feedback as networks improved or ‘tuned’ students’ knowledge structure of the lesson content. This approach only takes a few extra minutes of class time and is fairly simple to implement in ordinary classrooms, and so it has wide potential to support classroom instruction and student learning. Further research is needed to determine how critical it is to present both the group-average network along with the expert network for comparison in order to highlight group-level misconceptions, or is presenting only the expert network sufficient? If a group-level network is necessary, then a simple clicker-like classroom tool could be developed to collect sorting task data during lectures that could then immediately provide the group-average network for class discussion and reflection.Keywords: classroom instruction, engineering education, knowledge structure, pathfinder networks, structural feedback
Procedia PDF Downloads 585445 Advances in Sesame Molecular Breeding: A Comprehensive Review
Authors: Micheale Yifter Weldemichael
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Sesame (Sesamum indicum L.) is among the most important oilseed crops for its high edible oil quality and quantity. Sesame is grown for food, medicinal, pharmaceutical, and industrial uses. Sesame is also cultivated as a main cash crop in Asia and Africa by smallholder farmers. Despite the global exponential increase in sesame cultivation area, its production and productivity remain low, mainly due to biotic and abiotic constraints. Notwithstanding the efforts to solve these problems, a low level of genetic variation and inadequate genomic resources hinder the progress of sesame improvement. The objective of this paper is, therefore, to review recent advances in the area of molecular breeding and transformation to overcome major production constraints and could result in enhanced and sustained sesame production. This paper reviews various researches conducted to date on molecular breeding and genetic transformation in sesame focusing on molecular markers used in assessing the available online database resources, genes responsible for key agronomic traits as well as transgenic technology and genome editing. The review concentrates on quantitative and semi-quantitative studies on molecular breeding for key agronomic traits such as improvement of yield components, oil and oil-related traits, disease and insect/pest resistance, and drought, waterlogging and salt tolerance, as well as sesame genetic transformation and genome editing techniques. Pitfalls and limitations of existing studies and methodologies used so far are identified and some priorities for future research directions in sesame genetic improvement are identified in this review.Keywords: abiotic stress, biotic stress, improvement, molecular breeding, oil, sesame, shattering
Procedia PDF Downloads 195444 Evaluation of Collect Tree Protocol for Structural Health Monitoring System Using Wireless Sensor Networks
Authors: Amira Zrelli, Tahar Ezzedine
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Routing protocol may enhance the lifetime of sensor network, it has a highly importance, especially in wireless sensor network (WSN). Therefore, routing protocol has a big effect in these networks, thus the choice of routing protocol must be studied before setting up our network. In this work, we implement the routing protocol collect tree protocol (CTP) which is one of the hierarchic protocols used in structural health monitoring (SHM). Therefore, to evaluate the performance of this protocol, we choice to work with Contiki system and Cooja simulator. By throughput and RSSI evaluation of each node, we will deduce about the utility of CTP in structural monitoring system.Keywords: CTP, WSN, SHM, routing protocol
Procedia PDF Downloads 2855443 A Multi Agent Based Protection Scheme for Smart Distribution Network in Presence of Distributed Energy Resources
Authors: M. R. Ebrahimi, B. Mahdaviani
Abstract:
Conventional electric distribution systems are radial in nature, supplied at one end through a main source. These networks generally have a simple protection system usually implemented using fuses, re-closers, and over-current relays. Recently, great attention has been paid to applying Distributed energy resources (DERs) throughout electric distribution systems. Presence of such generation in a network leads to losing coordination of protection devices. Therefore, it is desired to develop an algorithm which is capable of protecting distribution systems that include DER. On the other hand smart grid brings opportunities to the power system. Fast advancement in communication and measurement techniques accelerates the development of multi agent system (MAS). So in this paper, a new approach for the protection of distribution networks in the presence of DERs is presented base on MAS. The proposed scheme has been implemented on a sample 27-bus distribution network.Keywords: distributed energy resource, distribution network, protection, smart grid, multi agent system
Procedia PDF Downloads 601