Search results for: network load

Authors: Chia-Feng Lu, Yuh-Jen Wang, Yu-Te Wu, Sui-Hing Yan

Abstract:

This study aimed at investigating whether the functional brain networks constructed using the initial EEG (obtained when patients first visited hospital) can be correlated with the progression of cognitive decline calculated as the changes of mini-mental state examination (MMSE) scores between the latest and initial examinations. We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions, and the network analysis based on graph theory to investigate the organization of functional networks in aMCI. Our finding suggested that higher integrated functional network with sufficient connection strengths, dense connection between local regions, and high network efficiency in processing information at the initial stage may result in a better prognosis of the subsequent cognitive functions for aMCI. In conclusion, the functional connectivity can be a useful biomarker to assist in prediction of cognitive declines in aMCI.

Keywords: Cognitive decline, functional connectivity, MCI, MMSE.

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Authors: M. Shariati, N. Tahouni, A. Khoshgard, M.H. Panjeshahi

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Owing to extensive use of hydrogen in refining or petrochemical units, it is essential to manage hydrogen network in order to make the most efficient utilization of hydrogen. On the other hand, hydrogen is an important byproduct not properly used through petrochemical complexes and mostly sent to the fuel system. A few works have been reported in literature to improve hydrogen network for petrochemical complexes. In this study a comprehensive analysis is carried out on petrochemical units using a modified automated targeting technique which is applied to determine the minimum hydrogen consumption. Having applied the modified targeting method in two petrochemical cases, the results showed a significant reduction in required fresh hydrogen.

Keywords: Automated targeting, Hydrogen network, Petrochemical, Process integration.

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Authors: S. Berjozkina, A. Sauhats, V. Bargels, E. Vanzovichs

Abstract:

There are various solutions for improving existing overhead line systems with the general purpose of increasing their limited capacity. The capacity reserve of the existing overhead lines is an important problem that must be considered from different aspects. The paper contains a comparative analysis of the mechanical and thermal limitations of an existing overhead line based on certain calculation conditions characterizing the examined variants. The methodology of the proposed estimation of the permissible conductor temperature and maximum load current is described in detail. The transmission line model consists of specific information of an existing overhead line of the Latvian power network. The main purpose of the simulation tasks is to find an additional capacity reserve by using accurate mathematical models. The results of the obtained data are presented.

Keywords: capacity of an overhead line, mechanical conditions, permissible conductor temperature, thermal conditions.

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Authors: Hakim S. Sultan Aljibori

Abstract:

Effect of geometry on crushing behavior, energy absorption and failure mode of woven roving jute fiber/epoxy laminated composite tubes were experimentally studied. Investigations were carried out on three different geometrical types of composite tubes (circular, square and radial corrugated) subjected to axial compressive loading. It was observed in axial crushing study that the load bearing capability is significantly influenced by corrugation geometry. The influence of geometries of specimens was supported by the plotted load – displacement curves of the tests.

Keywords: Crushing behavior, jute fiber, composite tubes andSpecific energy absorption

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Authors: Emmanuel D. Buedi, K. O. Boateng, Griffith S. Klogo

Abstract:

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.

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Authors: Hamid R. S. Mojaveri, Seyed S. Mousavi, Mojtaba Heydar, Ahmad Aminian

Abstract:

The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured.

Keywords: Artificial Neural Networks (ANN), bullwhip effect, demand forecasting, Support Vector Machine (SVM).

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Authors: Tao Peng, Jing Zhao, Yanqing Xu, Jing Cai

Abstract:

Due to missing/ambiguous boundaries between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes, ultrasound prostate segmentation is challenging. To handle these issues, this paper develops a hybrid method for ultrasound prostate segmentation by combining an optimized closed principal curve-based method and the evolutionary neural network; the former can fit curves with great curvature and generate a contour composed of line segments connected by sorted vertices, and the latter is used to express an appropriate map function (represented by parameters of evolutionary neural network) for generating the smooth prostate contour to match the ground truth contour. Both qualitative and quantitative experimental results showed that our proposed method obtains accurate and robust performances.

Keywords: Ultrasound prostate segmentation, optimized closed polygonal segment method, evolutionary neural network, smooth mathematical model.

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Authors: S. Jadid, S. Jalilzadeh

Abstract:

In this paper, an improved technique for contingency ranking using artificial neural network (ANN) is presented. The proposed approach is based on multi-layer perceptrons trained by backpropagation to contingency analysis. Severity indices in dynamic stability assessment are presented. These indices are based on the concept of coherency and three dot products of the system variables. It is well known that some indices work better than others for a particular power system. This paper along with test results using several different systems, demonstrates that combination of indices with ANN provides better ranking than a single index. The presented results are obtained through the use of power system simulation (PSS/E) and MATLAB 6.5 software.

Keywords: composite indices, transient stability, neural network.

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Authors: Bhavneet Sidhu, Hardeep Singh

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Cellular networks provide voice and data services to the users with mobility. To deliver services to the mobile users, the cellular network is capable of tracking the locations of the users, and allowing user movement during the conversations. These capabilities are achieved by the location management. Location management in mobile communication systems is concerned with those network functions necessary to allow the users to be reached wherever they are in the network coverage area. In a cellular network, a service coverage area is divided into smaller areas of hexagonal shape, referred to as cells. The cellular concept was introduced to reuse the radio frequency. Continued expansion of cellular networks, coupled with an increasingly restricted mobile spectrum, has established the reduction of communication overhead as a highly important issue. Much of this traffic is used in determining the precise location of individual users when relaying calls, with the field of location management aiming to reduce this overhead through prediction of user location. This paper describes and compares various location management schemes in the cellular networks.

Keywords: Cellular Networks, Location Area, MobilityManagement, Paging.

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Authors: M. Sedighizadeh, M. Rezaei, V. Najmi

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The Proton Exchange Membrane Fuel Cell (PEMFC) control system has an important effect on operation of cell. Traditional controllers couldn-t lead to acceptable responses because of time- change, long- hysteresis, uncertainty, strong- coupling and nonlinear characteristics of PEMFCs, so an intelligent or adaptive controller is needed. In this paper a neural network predictive controller have been designed to control the voltage of at the presence of fluctuations of temperature. The results of implementation of this designed NN Predictive controller on a dynamic electrochemical model of a small size 5 KW, PEM fuel cell have been simulated by MATLAB/SIMULINK.

Keywords: PEMFC, Neural Network, Predictive Control..

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Authors: Wenji Zhu, Yigang He

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This paper presents a new method of analog fault diagnosis based on back-propagation neural networks (BPNNs) using wavelet decomposition and fractal dimension as preprocessors. The proposed method has the capability to detect and identify faulty components in an analog electronic circuit with tolerance by analyzing its impulse response. Using wavelet decomposition to preprocess the impulse response drastically de-noises the inputs to the neural network. The second preprocessing by fractal dimension can extract unique features, which are the fed to a neural network as inputs for further classification. A comparison of our work with [1] and [6], which also employs back-propagation (BP) neural networks, reveals that our system requires a much smaller network and performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.

Keywords: Analog circuits, fault diagnosis, tolerance, wavelettransform, fractal dimension, box dimension.

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Authors: Jolly Shah, S.S.Rattan, B.C.Nakra

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Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major issue. This paper involves the forward and inverse kinematics of 2-DOF robotic manipulator with revolute joints. In this study the Denavit- Hartenberg (D-H) model is used to model robot links and joints. Also forward and inverse kinematics solution has been achieved using Artificial Neural Networks for 2-DOF robotic manipulator. It shows that by using artificial neural network the solution we get is faster, acceptable and has zero error.

Keywords: Artificial Neural Network, Forward Kinematics, Inverse Kinematics, Robotic Manipulator

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Authors: R. Ashok Bakkiyaraj, N. Kumarappan

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This paper investigates the suitability of Latin Hypercube sampling (LHS) for composite electric power system reliability analysis. Each sample generated in LHS is mapped into an equivalent system state and used for evaluating the annualized system and load point indices. DC loadflow based state evaluation model is solved for each sampled contingency state. The indices evaluated are loss of load probability, loss of load expectation, expected demand not served and expected energy not supplied. The application of the LHS is illustrated through case studies carried out using RBTS and IEEE-RTS test systems. Results obtained are compared with non-sequential Monte Carlo simulation and state enumeration analytical approaches. An error analysis is also carried out to check the LHS method’s ability to capture the distributions of the reliability indices. It is found that LHS approach estimates indices nearer to actual value and gives tighter bounds of indices than non-sequential Monte Carlo simulation.

Keywords: Composite power system, Latin Hypercube sampling, Monte Carlo simulation, Reliability evaluation, Variance analysis.

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Authors: Bachir Chemani, Rachid Halfaoui

Abstract:

The aim of study was to evaluate pressure distribution characteristics of the elastic textile bandages using two instrumental techniques: a prototype Instrument and a load Transference. The prototype instrument which simulates shape of real leg has pressure sensors which measure bandage pressure. Using this instrument, the results show that elastic textile bandages presents different pressure distribution characteristics and none produces a uniform distribution around lower limb.

The load transference test procedure is used to determine whether a relationship exists between elastic textile bandage structure and pressure distribution characteristics. The test procedure assesses degree of load, directly transferred through a textile when loads series are applied to bandaging surface. A range of weave fabrics was produced using needle weaving machine and a sewing technique. A textile bandage was developed with optimal characteristics far superior pressure distribution than other bandages. From results, we find that theoretical pressure is not consistent exactly with practical pressure. It is important in this study to make a practical application for specialized nurses in order to verify the results and draw useful conclusions for predicting the use of this type of elastic band.

Keywords: Textile, cotton, pressure, venous ulcers, elastic.

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Authors: Khaled M. EL-Naggar, Khaled A. AL-Rumaih

Abstract:

This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.

Keywords: Forecasting, Least error squares, Least absolute Value, Genetic algorithms

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Authors: Muhammad R Ahmed, Xu Huang, Dharmendra Sharma

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Developments in communication technologies especially in wireless have enabled the progress of low-cost and lowpower wireless sensor networks (WSNs). The features of such WSN are holding minimal energy, weak computational capabilities, wireless communication and an open-medium nature where sensors are deployed. WSN is underpinned by application driven such as military applications, the health sector, etc. Due to the intrinsic nature of the network and application scenario, WSNs are vulnerable to many attacks externally and internally. In this paper we have focused on the types of internal attacks of WSNs based on OSI model and discussed some security requirements, characterizers and challenges of WSNs, by which to contribute to the WSN-s security research.

Keywords: Wireless sensor network, internal attacks, security, OSI model.

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Authors: H. Rezvani, A. Hekmati

Abstract:

Nowadays, the use of renewable energy sources has been increasingly great because of the cost increase and public demand for clean energy sources. One of the fastest growing sources is wind energy. In this paper, Wind Diesel Hybrid System (WDHS) comprising a Diesel Generator (DG), a Wind Turbine Generator (WTG), the Consumer Load, a Battery-based Energy Storage System (BESS), and a Dump Load (DL) is used. Voltage is controlled by Diesel Generator; the frequency is controlled by BESS and DL. The BESS elimination is an efficient way to reduce maintenance cost and increase the dynamic response. Simulation results with graphs for the frequency of Power System, active power, and the battery power are presented for load changes. The controlling parameters are optimized by using Imperialist Competitive Algorithm (ICA). The simulation results for the BESS/no BESS cases are compared. Results show that in no BESS case, the frequency control is more optimal than the BESS case by using ICA. 

Keywords: Renewable Energy, Wind Diesel System, Induction Generator, Energy Storage, Imperialist Competitive Algorithm.

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Authors: Venus Marza, Amin Seyyedi, Luiz Fernando Capretz

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Software estimation accuracy is among the greatest challenges for software developers. This study aimed at building and evaluating a neuro-fuzzy model to estimate software projects development time. The forty-one modules developed from ten programs were used as dataset. Our proposed approach is compared with fuzzy logic and neural network model and Results show that the value of MMRE (Mean of Magnitude of Relative Error) applying neuro-fuzzy was substantially lower than MMRE applying fuzzy logic and neural network.

Keywords: Artificial Neural Network, Fuzzy Logic, Neuro-Fuzzy, Software Estimation

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Authors: Waleed S. Alwaneen

Abstract:

In many countries, cow dung is used as farm manure and for biogas production. Several bacterial strains associated with cow dung such as Campylobacter, Salmonella sp. and Escherichia coli cause serious human diseases. The objective of the present study was to investigate the use of insect larvae including fruit beetle, waxworms and tiger worms to improve the breakdown of agricultural wastes and reduce their pathogen loads. Fresh cow faeces were collected from a cattle farm and distributed into plastic boxes (100 g/box). Each box was provided with 10 larvae of fruit beetle, Waxworms and Tiger worms, respectively. There were 3 replicates in each treatment including the control. Bacteria were isolated weekly from both control and cow faeces to which larvae were added to determine the bacterial populations. Results revealed that the bacterial load was higher in the cow faeces treated with fruit beetles than in the control, while the bacterial load was lower in the cow faeces treated with waxworms and tiger worms than in the control. The activities of the fruit beetle larvae led to the cow faeces being liquefied which provided a more conducive growing media for bacteria. Therefore, higher bacterial load in the cow faeces treated with fruit beetle might be attributed to the liquefaction of cow faeces.

Keywords: Fruit beetle, waxworms, tiger worms, waste conditioning, composting.

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Authors: Ashwini V. Chavan, Sukhanand S. Bhosale

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Challenges of weak soil subgrade are often resolved either by stabilization or reinforcing it. However, it is also practiced to reinforce the granular fill to improve the load-settlement behavior of it over weak soil strata. The inclusion of reinforcement in the engineered granular fill provided a new impetus for the development of enhanced Soil-Structure Interaction (SSI) models, also known as mechanical foundation models or lumped parameter models. Several researchers have been working in this direction to understand the mechanism of granular fill-reinforcement interaction and the response of weak soil under the application of load. These models have been developed by extending available SSI models such as the Winkler Model, Pasternak Model, Hetenyi Model, Kerr Model etc., and are helpful to visualize the load-settlement behavior of a physical system through 1-D and 2-D analysis considering beam and plate resting on the foundation, respectively. Based on the literature survey, these models are categorized as ‘Reinforced Pasternak Model,’ ‘Double Beam Model,’ ‘Reinforced Timoshenko Beam Model,’ and ‘Reinforced Kerr Model’. The present work reviews the past 30+ years of research in the field of SSI models for reinforced foundation systems, presenting the conceptual development of these models systematically and discussing their limitations. A flow-chart showing procedure for compution of deformation and mobilized tension is also incorporated in the paper. Special efforts are taken to tabulate the parameters and their significance in the load-settlement analysis, which may be helpful in future studies for the comparison and enhancement of results and findings of physical models. 

Keywords: geosynthetics, mathematical modeling, reinforced foundation, soil-structure interaction, ground improvement, soft soil

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Authors: M.R. Mustafa, M.H. Isa, R.B. Rezaur

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Prediction of highly non linear behavior of suspended sediment flow in rivers has prime importance in the field of water resources engineering. In this study the predictive performance of two Artificial Neural Networks (ANNs) namely, the Radial Basis Function (RBF) Network and the Multi Layer Feed Forward (MLFF) Network have been compared. Time series data of daily suspended sediment discharge and water discharge at Pari River was used for training and testing the networks. A number of statistical parameters i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the models. Both the models produced satisfactory results and showed a good agreement between the predicted and observed data. The RBF network model provided slightly better results than the MLFF network model in predicting suspended sediment discharge.

Keywords: ANN, discharge, modeling, prediction, suspendedsediment,

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Authors: Somenath Mukherjee, Rajdeep Ray, Raj Kumar Samanta, Mofazzal H. Khondekar, Gautam Sanyal

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In next generation wireless networks (i.e., 4G and beyond), one of the main objectives is to ensure highest level of customer satisfaction in terms of data transfer speed, decrease in cost and delay, non-rejection and no drop of calls, availability of ‘always-on’ connectivity and services, continuity of connected services, hastle-free roaming in addition to the convenience of use of network services from anywhere and anytime. To take care of these requirements effectively, internet service providers (ISPs) and network planners have to go for major capacity enhancement of network resources and at the same time these resources are to be used effectively and efficiently to reduce cost and to increase revenue. In this work, the effective bandwidth available in a Mobile Switching Center (MSC) of a wireless network providing multi-class multimedia services is analyzed. Bandwidth requirement of the users for a customized Quality of Service (QoS) is estimated. The findings of the QoS estimation are applied for the capacity planning and admission control of the multi-class traffic flows coming into the MSC.

Keywords: Next generation wireless network, mobile switching center, multi-class traffic, quality of service, admission control, effective bandwidth.

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Authors: Sri Hartati, 1Agus Harjoko, Brad G. Nickerson

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A prototype of an anomaly detection system was developed to automate process of recognizing an anomaly of roentgen image by utilizing fuzzy histogram hyperbolization image enhancement and back propagation artificial neural network. The system consists of image acquisition, pre-processor, feature extractor, response selector and output. Fuzzy Histogram Hyperbolization is chosen to improve the quality of the roentgen image. The fuzzy histogram hyperbolization steps consist of fuzzyfication, modification of values of membership functions and defuzzyfication. Image features are extracted after the the quality of the image is improved. The extracted image features are input to the artificial neural network for detecting anomaly. The number of nodes in the proposed ANN layers was made small. Experimental results indicate that the fuzzy histogram hyperbolization method can be used to improve the quality of the image. The system is capable to detect the anomaly in the roentgen image.

Keywords: Image processing, artificial neural network, anomaly detection.

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Authors: Otávio J.U. Flores, Fernando A. Andrade, Dachamir Hotza, Hazim A. Al-Qureshi

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In the forming of ceramic materials the plasticity concept is commonly used. This term is related to a particular mechanical behavior when clay is mixed with water. A plastic ceramic material shows a permanent strain without rupture when a compressive load produces a shear stress that exceeds the material-s yield strength. For a plastic ceramic body it observes a measurable elastic behavior before the yield strength and when the applied load is removed. In this work, a mathematical model was developed from applied concepts of the plasticity theory by using the stress/strain diagram under compression.

Keywords: Plasticity, clay, modeling, coefficient of friction.

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Authors: Laurent Pitteloud, Jörg Meier

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Piled raft foundations represent an efficient and reliable technique for transferring high vertical and horizontal loads to the subsoil. Piled raft foundations were success­fully implemented for several high-rise buildings world­wide over the last decades. For the structural design of this foundation type the stiffnesses of both the piles and the raft have to be deter­mined for the static (e.g. dead load, live load) and the dynamic load cases (e.g. earthquake). In this context the question often arises, to which proportion wind loads are to be considered as dynamic loads. Usually a piled raft foundation has to be monitored in order to verify the design hypotheses. As an additional benefit, the analysis of this monitoring data may lead to a better under­standing of the behaviour of this foundation type for future projects in similar subsoil conditions. In case the measurement frequency is high enough, one may also draw conclusions on the effect of wind loading on the piled raft foundation. For a 41-storey office building in Basel, Switzerland, the preliminary design showed that a piled raft foundation was the best solution to satisfy both design requirements, as well as economic aspects. A high-frequency monitoring of the foundation including pile loads, vertical stresses under the raft, as well as pore water pressures was performed over 5 years. In windy situations the analysis of the measure­ments shows that the pile load increment due to wind consists of a static and a cyclic load term. As piles and raft react with different stiffnesses under static and dynamic loading, these measure­ments are useful for the correct definition of stiffnesses of future piled raft foundations. This paper outlines the design strategy and the numerical modelling of the aforementioned piled raft foundation. The measurement results are presented and analysed. Based on the findings, comments and conclusions on the definition of pile and raft stiffnesses for vertical and wind loading are proposed.

Keywords: Dynamic loading, high-frequency monitoring, piled raft foundations, wind loading.

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Authors: Reinhold Decker, Christian Holsing, Sascha Lerke

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This paper presents a new growing neural network for cluster analysis and market segmentation, which optimizes the size and structure of clusters by iteratively checking them for multivariate normality. We combine the recently published SGNN approach [8] with the basic principle underlying the Gaussian-means algorithm [13] and the Mardia test for multivariate normality [18, 19]. The new approach distinguishes from existing ones by its holistic design and its great autonomy regarding the clustering process as a whole. Its performance is demonstrated by means of synthetic 2D data and by real lifestyle survey data usable for market segmentation.

Keywords: Artificial neural network, clustering, multivariatenormality, market segmentation, self-organization

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Authors: Sameer Kumar, Jariah Mohd. Jan

Abstract:

Earlier studies in kinship networks have primarily focused on observing the social relationships existing between family relatives. In this study, we pre-identified hubs in the network to investigate if they could play a catalyst role in the transfer of physical information. We conducted a case study of a ceremony performed in one of the families of a small Hindu community – the Uttar Rarhi Kayasthas. Individuals (n = 168) who resided in 11 geographically dispersed regions were contacted through our hub-based representation. We found that using this representation, over 98% of the individuals were successfully contacted within the stipulated period. The network also demonstrated a small-world property, with an average geodesic distance of 3.56.

Keywords: Social Networks, Kinship Networks, Social Network Analysis, Geospatial Communication, Hubs

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Authors: Soichi Ogishima, Takeshi Hase, So Nakagawa, Yasuhiro Suzuki, Hiroshi Tanaka

Abstract:

To understand life as biological system, evolutionary understanding is indispensable. Protein interactions data are rapidly accumulating and are suitable for system-level evolutionary analysis. We have analyzed yeast protein interaction network by both mathematical and biological approaches. In this poster presentation, we inferred the evolutionary birth periods of yeast proteins by reconstructing phylogenetic profile. It has been thought that hub proteins that have high connection degree are evolutionary old. But our analysis showed that hub proteins are entirely evolutionary new. We also examined evolutionary processes of protein complexes. It showed that member proteins of complexes were tend to have appeared in the same evolutionary period. Our results suggested that protein interaction network evolved by modules that form the functional unit. We also reconstructed standardized phylogenetic trees and calculated evolutionary rates of yeast proteins. It showed that there is no obvious correlation between evolutionary rates and connection degrees of yeast proteins.

Keywords: Protein interaction network, evolution, modularity, evolutionary rate, connection degrees.

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Authors: Huihai Wu, Xiaohui Liu

Abstract:

Using Dynamic Bayesian Networks (DBN) to model genetic regulatory networks from gene expression data is one of the major paradigms for inferring the interactions among genes. Averaging a collection of models for predicting network is desired, rather than relying on a single high scoring model. In this paper, two kinds of model searching approaches are compared, which are Greedy hill-climbing Search with Restarts (GSR) and Markov Chain Monte Carlo (MCMC) methods. The GSR is preferred in many papers, but there is no such comparison study about which one is better for DBN models. Different types of experiments have been carried out to try to give a benchmark test to these approaches. Our experimental results demonstrated that on average the MCMC methods outperform the GSR in accuracy of predicted network, and having the comparable performance in time efficiency. By proposing the different variations of MCMC and employing simulated annealing strategy, the MCMC methods become more efficient and stable. Apart from comparisons between these approaches, another objective of this study is to investigate the feasibility of using DBN modeling approaches for inferring gene networks from few snapshots of high dimensional gene profiles. Through synthetic data experiments as well as systematic data experiments, the experimental results revealed how the performances of these approaches can be influenced as the target gene network varies in the network size, data size, as well as system complexity.

Keywords: Genetic regulatory network, Dynamic Bayesian network, GSR, MCMC.

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Authors: Nasrin Bakhshizadeh, Ashkan Forootan

Abstract:

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.

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