Search results for: network behavior
4388 Application of Fuzzy Neural Network for Image Tumor Description
Authors: Nahla Ibraheem Jabbar, Monica Mehrotra
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This paper used a fuzzy kohonen neural network for medical image segmentation. Image segmentation plays a important role in the many of medical imaging applications by automating or facilitating the diagnostic. The paper analyses the tumor by extraction of the features of (area, entropy, means and standard deviation).These measurements gives a description for a tumor.
Keywords: FCM, features extraction, medical image processing, neural network, segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21034387 Modified Functional Link Artificial Neural Network
Authors: Ashok Kumar Goel, Suresh Chandra Saxena, Surekha Bhanot
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In this work, a Modified Functional Link Artificial Neural Network (M-FLANN) is proposed which is simpler than a Multilayer Perceptron (MLP) and improves upon the universal approximation capability of Functional Link Artificial Neural Network (FLANN). MLP and its variants: Direct Linear Feedthrough Artificial Neural Network (DLFANN), FLANN and M-FLANN have been implemented to model a simulated Water Bath System and a Continually Stirred Tank Heater (CSTH). Their convergence speed and generalization ability have been compared. The networks have been tested for their interpolation and extrapolation capability using noise-free and noisy data. The results show that M-FLANN which is computationally cheap, performs better and has greater generalization ability than other networks considered in the work.Keywords: DLFANN, FLANN, M-FLANN, MLP
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17904386 Simplified 3R2C Building Thermal Network Model: A Case Study
Authors: S. M. Mahbobur Rahman
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Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control. Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.
Keywords: ASHRAE case study, clear sky solar radiation model, energy modeling, thermal network model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11974385 Correlation of Viscosity in Nanofluids using Genetic Algorithm-neural Network (GA-NN)
Authors: Hajir Karimi, Fakheri Yousefi, Mahmood Reza Rahimi
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An accurate and proficient artificial neural network (ANN) based genetic algorithm (GA) is developed for predicting of nanofluids viscosity. A genetic algorithm (GA) is used to optimize the neural network parameters for minimizing the error between the predictive viscosity and the experimental one. The experimental viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15 to 343.15 K and volume fraction up to 15% were used from literature. The result of this study reveals that GA-NN model is outperform to the conventional neural nets in predicting the viscosity of nanofluids with mean absolute relative error of 1.22% and 1.77% for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results of this work have also been compared with others models. The findings of this work demonstrate that the GA-NN model is an effective method for prediction viscosity of nanofluids and have better accuracy and simplicity compared with the others models.Keywords: genetic algorithm, nanofluids, neural network, viscosity
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20724384 A Survey of Sentiment Analysis Based on Deep Learning
Authors: Pingping Lin, Xudong Luo, Yifan Fan
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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.Keywords: Natural language processing, sentiment analysis, document analysis, multimodal sentiment analysis, deep learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19784383 Bandwidth Estimation Algorithms for the Dynamic Adaptation of Voice Codec
Authors: Davide Pierattoni, Ivan Macor, Pier Luca Montessoro
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In the recent years multimedia traffic and in particular VoIP services are growing dramatically. We present a new algorithm to control the resource utilization and to optimize the voice codec selection during SIP call setup on behalf of the traffic condition estimated on the network path. The most suitable methodologies and the tools that perform realtime evaluation of the available bandwidth on a network path have been integrated with our proposed algorithm: this selects the best codec for a VoIP call in function of the instantaneous available bandwidth on the path. The algorithm does not require any explicit feedback from the network, and this makes it easily deployable over the Internet. We have also performed intensive tests on real network scenarios with a software prototype, verifying the algorithm efficiency with different network topologies and traffic patterns between two SIP PBXs. The promising results obtained during the experimental validation of the algorithm are now the basis for the extension towards a larger set of multimedia services and the integration of our methodology with existing PBX appliances.Keywords: Integrated voice-data communication, computernetwork performance, resource optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16844382 Application of Feed Forward Neural Networks in Modeling and Control of a Fed-Batch Crystallization Process
Authors: Petia Georgieva, Sebastião Feyo de Azevedo
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This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.
Keywords: Feed forward neural network, process modelling, model predictive control, crystallization process.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18634381 Application of Neural Network and Finite Element for Prediction the Limiting Drawing Ratio in Deep Drawing Process
Authors: H.Mohammadi Majd, M.Jalali Azizpour, A.V. Hoseini
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In this paper back-propagation artificial neural network (BPANN) is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.Keywords: Back-propagation artificial neural network(BPANN), deep drawing, prediction, limiting drawing ratio (LDR).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17194380 Integrating Low and High Level Object Recognition Steps by Probabilistic Networks
Authors: András Barta, István Vajk
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In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.
Keywords: Object recognition, Bayesian network, Wavelets, Document processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16614379 A Survey of Access Control Schemes in Wireless Sensor Networks
Authors: Youssou Faye, Ibrahima Niang, Thomas Noel
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Access control is a critical security service in Wire- less Sensor Networks (WSNs). To prevent malicious nodes from joining the sensor network, access control is required. On one hand, WSN must be able to authorize and grant users the right to access to the network. On the other hand, WSN must organize data collected by sensors in such a way that an unauthorized entity (the adversary) cannot make arbitrary queries. This restricts the network access only to eligible users and sensor nodes, while queries from outsiders will not be answered or forwarded by nodes. In this paper we presentee different access control schemes so as to ?nd out their objectives, provision, communication complexity, limits, etc. Using the node density parameter, we also provide a comparison of these proposed access control algorithms based on the network topology which can be flat or hierarchical.Keywords: Access Control, Authentication, Key Management, Wireless Sensor Networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26464378 Comparison between Beta Wavelets Neural Networks, RBF Neural Networks and Polynomial Approximation for 1D, 2DFunctions Approximation
Authors: Wajdi Bellil, Chokri Ben Amar, Adel M. Alimi
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This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn - R from scattered samples (xi; y = f(xi)) i=1....n, where first, we have little a priori knowledge on the unknown function f: it lives in some infinite dimensional smooth function space and second the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate f as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.
Keywords: Beta wavelets networks, RBF neural network, training algorithms, MSE, 1-D, 2D function approximation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19044377 Predicting Extrusion Process Parameters Using Neural Networks
Authors: Sachin Man Bajimaya, SangChul Park, Gi-Nam Wang
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The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.Keywords: Artificial Neural Network (ANN), Indirect Extrusion, Finite Element Analysis, MES.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23564376 A Signature-Based Secure Authentication Framework for Vehicular Ad Hoc Networks
Authors: J. Jenefa, E. A. Mary Anita
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Vehicular Ad hoc NETwork (VANET) is a kind of Mobile Ad hoc NETwork (MANET). It allows the vehicles to communicate with one another as well as with nearby Road Side Units (RSU) and Regional Trusted Authorities (RTA). Vehicles communicate through On-Board Units (OBU) in which privacy has to be assured which will avoid the misuse of private data. A secure authentication framework for VANETs is proposed in which Public Key Cryptography (PKC) based adaptive pseudonym scheme is used to generate self-generated pseudonyms. Self-generated pseudonyms are used instead of real IDs for privacy preservation and non-repudiation. The ID-Based Signature (IBS) and ID-Based Online/Offline Signature (IBOOS) schemes are used for authentication. IBS is used to authenticate between vehicle and RSU whereas IBOOS provides authentication among vehicles. Security attacks like impersonation attack in the network are resolved and the attacking nodes are rejected from the network, thereby ensuring secure communication among the vehicles in the network. Simulation results shows that the proposed system provides better authentication in VANET environment.
Keywords: Non-repudiation, privacy preservation, public key cryptography, self- generated pseudonym.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14374375 Description and Analysis of Embedded Firewall Techniques
Authors: Ahmed Abou Elfarag, A. Baith M., Hassan H. Alkhishali
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With the turn of this century, many researchers started showing interest in Embedded Firewall (EF) implementations. These are not the usual firewalls that are used as checkpoints at network gateways. They are, rather, applied near those hosts that need protection. Hence by using them, individual or grouped network components can be protected from the inside as well as from external attacks. This paper presents a study of EF-s, looking at their architecture and problems. A comparative study assesses how practical each kind is. It particularly focuses on the architecture, weak points, and portability of each kind. A look at their use by different categories of users is also presented.Keywords: Embedded Firewall (EF), Network Interface Card (NIC), Virtual Machine Software (VMware), Virtual Firewall (VF).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17064374 An Energy Efficient Algorithm for Distributed Mutual Exclusion in Mobile Ad-hoc Networks
Authors: Sayani Sil, Sukanta Das
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This paper reports a distributed mutual exclusion algorithm for mobile Ad-hoc networks. The network is clustered hierarchically. The proposed algorithm considers the clustered network as a logical tree and develops a token passing scheme to get the mutual exclusion. The performance analysis and simulation results show that its message requirement is optimal, and thus the algorithm is energy efficient.Keywords: Critical section, Distributed mutual exclusion, MobileAd-hoc network, Token-based algorithms.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17374373 Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting
Authors: A. Chaouachi, R.M. Kamel, R. Ichikawa, H. Hayashi, K. Nagasaka
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This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.Keywords: Neural network ensemble, Solar power generation, 24 hour forecasting, Comparative study
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32644372 Arterial Stiffness Detection Depending on Neural Network Classification of the Multi- Input Parameters
Authors: Firas Salih, Luban Hameed, Afaf Kamil, Armin Bolz
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Diagnostic and detection of the arterial stiffness is very important; which gives indication of the associated increased risk of cardiovascular diseases. To make a cheap and easy method for general screening technique to avoid the future cardiovascular complexes , due to the rising of the arterial stiffness ; a proposed algorithm depending on photoplethysmogram to be used. The photoplethysmograph signals would be processed in MATLAB. The signal will be filtered, baseline wandering removed, peaks and valleys detected and normalization of the signals should be achieved .The area under the catacrotic phase of the photoplethysmogram pulse curve is calculated using trapezoidal algorithm ; then will used in cooperation with other parameters such as age, height, blood pressure in neural network for arterial stiffness detection. The Neural network were implemented with sensitivity of 80%, accuracy 85% and specificity of 90% were got from the patients data. It is concluded that neural network can detect the arterial STIFFNESS depending on risk factor parameters.Keywords: Arterial stiffness, area under the catacrotic phase of the photoplethysmograph pulse, neural network
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16404371 Application of Neural Network in User Authentication for Smart Home System
Authors: A. Joseph, D.B.L. Bong, D.A.A. Mat
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Security has been an important issue and concern in the smart home systems. Smart home networks consist of a wide range of wired or wireless devices, there is possibility that illegal access to some restricted data or devices may happen. Password-based authentication is widely used to identify authorize users, because this method is cheap, easy and quite accurate. In this paper, a neural network is trained to store the passwords instead of using verification table. This method is useful in solving security problems that happened in some authentication system. The conventional way to train the network using Backpropagation (BPN) requires a long training time. Hence, a faster training algorithm, Resilient Backpropagation (RPROP) is embedded to the MLPs Neural Network to accelerate the training process. For the Data Part, 200 sets of UserID and Passwords were created and encoded into binary as the input. The simulation had been carried out to evaluate the performance for different number of hidden neurons and combination of transfer functions. Mean Square Error (MSE), training time and number of epochs are used to determine the network performance. From the results obtained, using Tansig and Purelin in hidden and output layer and 250 hidden neurons gave the better performance. As a result, a password-based user authentication system for smart home by using neural network had been developed successfully.Keywords: Neural Network, User Authentication, Smart Home, Security
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20304370 Electromagnetic Interference Radiation Prediction and Final Measurement Process Optimization by Neural Network
Authors: Hussam Elias, Ninovic Perez, Holger Hirsch
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The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we present a method to perform the final phase of Electromagnetic Compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the Conventional Neural Network (CNN). The neural network was trained using real EMC measurements which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meet the maximum radiation value.
Keywords: Conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3224369 Slice Bispectrogram Analysis-Based Classification of Environmental Sounds Using Convolutional Neural Network
Authors: Katsumi Hirata
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Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.
Keywords: Bispectrum, convolutional neural network, environmental sound, slice bispectrogram, spectrogram.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6064368 Video Quality assessment Measure with a Neural Network
Authors: H. El Khattabi, A. Tamtaoui, D. Aboutajdine
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In this paper, we present the video quality measure estimation via a neural network. This latter predicts MOS (mean opinion score) by providing height parameters extracted from original and coded videos. The eight parameters that are used are: the average of DFT differences, the standard deviation of DFT differences, the average of DCT differences, the standard deviation of DCT differences, the variance of energy of color, the luminance Y, the chrominance U and the chrominance V. We chose Euclidean Distance to make comparison between the calculated and estimated output.Keywords: video, neural network MLP, subjective quality, DCT, DFT, Retropropagation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18004367 Study of Measures to Secure Video Phone Service Safety through a Preliminary Evaluationof the Information Security of the New IT Service
Authors: DongHoon Shin, Yunmook Nah, HoSeong Kim, Gang Shin Lee, Jae-Il Lee
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The rapid advance of communication technology is evolving the network environment into the broadband convergence network. Likewise, the IT services operated in the individual network are also being quickly converged in the broadband convergence network environment. VoIP and IPTV are two examples of such new services. Efforts are being made to develop the video phone service, which is an advanced form of the voice-oriented VoIP service. However, the new IT services will be subject to stability and reliability vulnerabilities if the relevant security issues are not answered during the convergence of the existing IT services currently being operated in individual networks within the wider broadband network environment. To resolve such problems, this paper attempts to analyze the possible threats and identify the necessary security measures before the deployment of the new IT services. Furthermore, it measures the quality of the encryption algorithm application example to describe the appropriate algorithm in order to present security technology that will have no negative impact on the quality of the video phone service.Keywords: BcN, Security Measures, Video Phone.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14294366 Rheological Behaviors of Crude Oil in the Presence of Water
Authors: Madjid Meriem-Benziane, Hamou Zahloul
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The rheological properties of light crude oil and its mixture with water were investigated experimentally. These rheological properties include steady flow behavior, yield stress, transient flow behavior, and viscoelastic behavior. A RheoStress RS600 rheometer was employed in all of the rheological examination tests. The light crude oil exhibits a Newtonian and for emulsion exhibits a non-Newtonian shear thinning behavior over the examined shear rate range of 0.1–120 s-1. In first time, a series of samples of crude oil from the Algerian Sahara has been tested and the results expressed in terms of τ=f(γ) have demonstrated their Newtonian character for the temperature included in [20°C, 70°C]. In second time and at T=20°C, the oil-water emulsions (30%, 50% and 70%) by volume of water), thermodynamically stable, have demonstrated a non-Newtonian rheological behavior that is to say, Herschel-Bulkley and Bingham types. For each type of crude oil-water emulsion, the rheological parameters are calculated by numerical treatment of results.
Keywords: Crude oil Algerian, Emulsion, Newtonian, Non- Newtonian, viscosity.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 34024365 Remarks Regarding Queuing Model and Packet Loss Probability for the Traffic with Self-Similar Characteristics
Authors: Mihails Kulikovs, Ernests Petersons
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Network management techniques have long been of interest to the networking research community. The queue size plays a critical role for the network performance. The adequate size of the queue maintains Quality of Service (QoS) requirements within limited network capacity for as many users as possible. The appropriate estimation of the queuing model parameters is crucial for both initial size estimation and during the process of resource allocation. The accurate resource allocation model for the management system increases the network utilization. The present paper demonstrates the results of empirical observation of memory allocation for packet-based services.Keywords: Queuing System, Packet Loss Probability, Measurement-Based Admission Control (MBAC), Performanceevaluation, Quality of Service (QoS).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17614364 Farmers’ Awareness and Behavior of Chemical Pesticide Uses in Suan Luang Sub-District Municipality, Ampawa, Samut Songkram, Thailand
Authors: Paiboon Jeamponk, Tikamporn Thipsaeng
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This paper is aimed to investigate farmers’ level of awareness and behavior of chemical pesticide uses, by using a case study of Suan Luang Sub- District Municipality, Ampawa, Samut Songkram Province. Questionnaire was employed in this study with the farmers from 46 households to explore their level of awareness in chemical pesticide uses, while interview and observation were adopted in exploring their behavior of chemical pesticide uses. The findings reflected the farmers’ high level of awareness in chemical pesticide uses in the hazardous effects of the chemical to human and environmental health, while their behavior of chemical pesticide uses explained their awareness paid to the right way of using pesticides, for instance reading the direction on the label, keeping children and animals away from the area of pesticide mixing, covering body with clothes and wearing hat and mask, no smoking, eating or drinking during pesticide spray or standing in windward direction.
Keywords: Awareness, Behavior, Pesticide.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27594363 Research on the Teaching Quality Evaluation of China’s Network Music Education APP
Authors: Guangzhuang Yu, Chun-Chu Liu
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With the advent of the Internet era in recent years, social music education has gradually shifted from the original entity education mode to the mode of entity plus network teaching. No matter for school music education, professional music education or social music education, the teaching quality is the most important evaluation index. Regarding the research on teaching quality evaluation, scholars at home and abroad have contributed a lot of research results on the basis of multiple methods and evaluation subjects. However, to our best knowledge the complete evaluation model for the virtual teaching interaction mode of the emerging network music education Application (APP) has not been established. This research firstly found out the basic dimensions that accord with the teaching quality required by the three parties, constructing the quality evaluation index system; and then, on the basis of expounding the connotation of each index, it determined the weight of each index by using method of fuzzy analytic hierarchy process, providing ideas and methods for scientific, objective and comprehensive evaluation of the teaching quality of network education APP.
Keywords: Network music education APP, teaching quality evaluation, index, connotation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8284362 Condition Monitoring System of Mine Air Compressors Based on Wireless Sensor Network
Authors: Sheng Fu, Yinbo Gao, Hao Lin
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In the current mine air compressors monitoring system, there are some difficulties in the installation and maintenance because of the wired connection. To solve the problem, this paper introduces a new air compressors monitoring system based on ZigBee in which the monitoring parameters are transmitted wirelessly. The collecting devices are designed to form a cluster network to collect vibration, temperature, and pressure of air cylinders and other parameters. All these devices are battery-powered. Besides, the monitoring software in PC is developed using MFC. Experiments show that the designed wireless sensor network works well in the site environmental condition and the system is very convenient to be installed since the wireless connection. This monitoring system will have a wide application prospect in the upgrade of the old monitoring system of the air compressors.
Keywords: Condition monitoring, wireless sensor network, air compressor, ZigBee, data collecting
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13734361 Investigation of Chord Protocol in Peer to Peer-Wireless Mesh Network with Mobility
Authors: P. Prasanna Murali Krishna, M. V. Subramanyam, K. Satya Prasad
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File sharing in networks is generally achieved using Peer-to-Peer (P2P) applications. Structured P2P approaches are widely used in adhoc networks due to its distributed and scalability features. Efficient mechanisms are required to handle the huge amount of data distributed to all peers. The intrinsic characteristics of P2P system makes for easier content distribution when compared to client-server architecture. All the nodes in a P2P network act as both client and server, thus, distributing data takes lesser time when compared to the client-server method. CHORD protocol is a resource routing based where nodes and data items are structured into a 1- dimensional ring. The structured lookup algorithm of Chord is advantageous for distributed P2P networking applications. However, structured approach improves lookup performance in a high bandwidth wired network it could contribute to unnecessary overhead in overlay networks leading to degradation of network performance. In this paper, the performance of existing CHORD protocol on Wireless Mesh Network (WMN) when nodes are static and dynamic is investigated.Keywords: Wireless mesh network (WMN), structured P2P networks, peer to peer resource sharing, CHORD protocol, DHT.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21124360 Neuron Efficiency in Fluid Dynamics and Prediction of Groundwater Reservoirs'' Properties Using Pattern Recognition
Authors: J. K. Adedeji, S. T. Ijatuyi
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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p1), fractured layer (p2), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable gr (gravitational resistance) can be deduced from the elevation and apparent resistivity pa. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.
Keywords: Neural network, gravitational resistance, pattern recognition, non-linear.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7884359 A Study of Efficiency and Prioritize of Eurasian Logistics Network
Authors: Ji-Young Song, Moon-Shuk Song, Hee-Seung Na
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Recently, Northeast Asia has become one of the three largest trade areas, covering approximately 30% of the total trade volume of the world. However, the distribution facilities are saturated due to the increase in the transportation volume within the area and with the European countries. In order to accommodate the increase of the transportation volume, the transportation networking with the major countries in Northeast Asia and Europe is absolutely necessary. The Eurasian Logistics Network will develop into an international passenger transportation network covering the Northeast Asian region and an international freight transportation network connecting across Eurasia Continent. This paper surveys the changes and trend of the distribution network in the Eurasian Region according to the political, economic and environmental changes of the region, analyses the distribution network according to the changes in the transportation policies of the related countries, and provides the direction of the development of composite transportation on the basis of the present conditions of transportation means. The transportation means optimal for the efficiency of transportation system are suggested to be train ferries, sea & rail or sea & rail & sea. It is suggested to develop diversified composite transportation means and routes within the boundary of international cooperation system.Keywords: Eurasian Logistics, Integrated Distribution Transport, Northeast Asia, Transportation Networking
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1657