Search results for: electrical distribution networks

Authors: Robert Lenzie

Abstract:

Transcranial electrical stimulation (tES) is significant in the research literature. However, the effects of tES on brain activity are still poorly understood at the surface level, the Brodmann Area level, and the impact on neural networks. Using a method like electroencephalography (EEG) in conjunction with tES might make it possible to comprehend the brain response and mechanisms behind published observed alterations in more depth. Using a method to directly see the effect of tES on EEG may offer high temporal resolution data on the brain activity changes/modulations brought on by tES that correlate to various processing stages within the brain. This paper provides unpublished information on a cutting-edge methodology that may reveal details about the dynamics of how the human brain works beyond what is now achievable with existing methods.

Keywords: tACS, frequency, EEG, optimal

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Jainendra Singh, Zaheeruddin

Abstract:

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

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

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Authors: Tea Poklepović, Zdravka Aljinović, Branka Marasović

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Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector auto-regressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is neural networks using Nelson-Siegel estimation of yield curves.

Keywords: Nelson-Siegel Model, neural networks, Svensson Model, vector autoregressive model, yield curve

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Authors: Norazlin Mohamad, Saiful Adli Bukry, Zarina Zahari, Haidzir Manaf, Hanafi Sawalludin

Abstract:

Background: Deficit in neuromuscular recruitment and decrease force distribution were the common problems among ankle instability patients due to altered joint kinematics that lead to recurrent ankle injuries. Rigid Tape and KT Tape had widely been used as therapeutic and performance enhancement tools in ankle stability. However the difference effect between this two tapes is still controversial. Objective: To investigate the different effect between Rigid Tape and KT Tape on force distribution and muscle activation among ankle instability patients while standing. Study design: Crossover trial. Participants: 27 patients, age between 18 to 30 years old participated in this study. All the subjects were applied with KT Tape & Rigid Tape on their affected ankle with 3 days of interval for each intervention. The subjects were tested with their barefoot (without tape) first to act as a baseline before proceeding with KT Tape, and then with Rigid Tape. Result: There were no significant difference on force distribution at forefoot and back-foot for both tapes while standing. However the mean data shows that Rigid Tape has the highest force distribution at back-foot rather than forefoot when compared with KT Tape that had more force distribution at forefoot while standing. Regarding muscle activation (Peroneus Longus), results showed significant difference between Rigid Tape and KT Tape (p= 0.048). However, there was no significant difference on Tibialis Anterior muscle activation between both tapes while standing. Conclusion: The results indicated that Peroneus longus muscle was more active when applied Rigid Tape rather than KT Tape in ankle instability patients while standing.

Keywords: ankle instability, kinematic, muscle activation, force distribution, Rigid Tape, KT tape

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Authors: Tomoaki Hashimoto

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In recent decades, probabilistic constrained optimal control problems have attracted much attention in many research field. Although probabilistic constraints are generally intractable in an optimization problem, several tractable methods haven been proposed to handle probabilistic constraints. In most methods, probabilistic constraints are reduced to deterministic constraints that are tractable in an optimization problem. However, there is a gap between the transformed deterministic constraints in case of known and unknown probability distribution. This paper examines the conservativeness of probabilistic constrained optimization method with the unknown probability distribution. The objective of this paper is to provide a quantitative assessment of the conservatism for tractable constraints in probabilistic constrained optimization with the unknown probability distribution.

Keywords: optimal control, stochastic systems, discrete time systems, probabilistic constraints

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Authors: Abdel Hadi Ebraheim

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This paper introduces a new extension of the Inverse Pareto distribution in the framework of Marshal-Olkin (1997) family of distributions. This model is capable of modeling various shapes of aging and failure data. The statistical properties of the new model are discussed. Several methods are used to estimate the parameters involved. Explicit expressions are derived for different types of moments of value in reliability analysis are obtained. Besides, the order statistics of samples from the new proposed model have been studied. Finally, the usefulness of the new model for modeling reliability data is illustrated using two real data sets with simulation study.

Keywords: pareto distribution, marshal-Olkin, reliability, hazard functions, moments, estimation

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Authors: Esubalew Yehualaw Melaku, Tigistu Haile Eritro

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In this study, two basic geophysical methods are applied for mapping the groundwater aquifer system in the Lisana area along the Guder River, northeast of Hosanna town, near the western margin of the Central Main Ethiopian Rift. The main target of the study is to map the potential aquifer zone and investigate the groundwater potential for current and future development of the resource in the Gode area. The geophysical methods employed in this study include, Vertical Electrical Sounding (VES) and magnetic survey techniques. Electrical sounding was used to examine and map the depth to the potential aquifer zone of the groundwater and its distribution over the area. On the other hand, a magnetic survey was used to delineate contact between lithologic units and geological structures. The 2D magnetic modeling and the geoelectric sections are used for the identification of weak zones, which control the groundwater flow and storage system. The geophysical survey comprises of twelve VES readings collected by using a Schlumberger array along six profile lines and more than four hundred (400) magnetic readings at about 10m station intervals along four profiles and 20m along three random profiles. The study result revealed that the potential aquifer in the area is obtained at a depth range from 45m to 92m. This is the response of the highly weathered/ fractured ignimbrite and pumice layer with sandy soil, which is the main water-bearing horizon. Overall, in the neighborhood of four VES points, VES- 2, VES- 3, VES-10, and VES-11, shows good water-bearing zones in the study area.

Keywords: vertical electrical sounding, magnetic survey, aquifer, groundwater potential

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Authors: Mohamed Aymen Slim, Afef Abdelkrim, Mohamed Benrejeb

Abstract:

The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches.

Keywords: Electro Myo Graphic (EMG) signals, experimental approach, handwriting process, Radial Basis Functions (RBF) neural networks, velocity modeling

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Authors: Oladapo Tolulope Ibitoye

Abstract:

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

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

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Authors: Arbnor Pajaziti, Hasan Cana

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In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results.

Keywords: robotic arm, neural network, genetic algorithm, optimization

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Authors: Mohamed Amhal, Jose Sayritupac

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Photovoltaic (PV) systems have risen as one of the modern renewable energy sources that are used in wide ranges to produce electricity and deliver it to the electrical grid. In parallel, monitoring systems have been deployed as a key element to track the energy production and to forecast the total production for the next days. The reliability of the PV energy production has become a crucial point in the analysis of PV systems. A deeper understanding of each phenomenon that causes a gain or a loss of energy is needed to better design, operate and maintain the PV systems. This work analyzes the current losses distribution in PV systems starting from the available solar energy, going through the DC side and AC side, to the delivery point. Most of the phenomena linked to energy losses and gains are considered and modeled, based on real time monitoring data and datasheets of the PV system components. An analysis of the order of magnitude of each loss is compared to the current literature and commercial software. To date, the analysis of PV systems performance based on a breakdown structure of energy losses and gains is not covered enough in the literature, except in some software where the concept is very common. The cutting-edge of the current analysis is the implementation of software tools for energy losses estimation in PV systems based on several energy losses definitions and estimation technics. The developed tools have been validated and tested on some PV plants in France, which are operating for years. Among the major findings of the current study: First, PV plants in France show very low rates of soiling and aging. Second, the distribution of other losses is comparable to the literature. Third, all losses reported are correlated to operational and environmental conditions. For future work, an extended analysis on further PV plants in France and abroad will be performed.

Keywords: energy gains, energy losses, losses distribution, monitoring, photovoltaic, photovoltaic systems

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Authors: Longqing Li

Abstract:

The paper addresses the inefficiency of the classical model in measuring the Value-at-Risk (VaR) using a normal distribution or a Student’s t distribution. Specifically, the paper focuses on the one day ahead Value-at-Risk (VaR) of major stock market’s daily returns in US, UK, China and Hong Kong in the most recent ten years under 95% confidence level. To improve the predictable power and search for the best performing model, the paper proposes using two leading alternatives, Extreme Value Theory (EVT) and a family of GARCH models, and compares the relative performance. The main contribution could be summarized in two aspects. First, the paper extends the GARCH family model by incorporating EGARCH and TGARCH to shed light on the difference between each in estimating one day ahead Value-at-Risk (VaR). Second, to account for the non-normality in the distribution of financial markets, the paper applies Generalized Error Distribution (GED), instead of the normal distribution, to govern the innovation term. A dynamic back-testing procedure is employed to assess the performance of each model, a family of GARCH and the conditional EVT. The conclusion is that Exponential GARCH yields the best estimate in out-of-sample one day ahead Value-at-Risk (VaR) forecasting. Moreover, the discrepancy of performance between the GARCH and the conditional EVT is indistinguishable.

Keywords: Value-at-Risk, Extreme Value Theory, conditional EVT, backtesting

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Authors: Reza Dinarvand, Mahdi Sadeghian, Somaye Sadeghian

Abstract:

Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.

Keywords: artificial neural network, clayey soil, imperialist competition algorithm, lateral bearing capacity, short pile

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Authors: Liang Bai, Yandong Xiao, Haorang Wang, Songyang Lao

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The directedness of links is crucial to determine the controllability in complex networks. Even the edge directions can determine the controllability of complex networks. Obviously, for a given network, we wish to design its edge directions that make this network approach the optimal controllability. In this work, we firstly introduce two methods to enhance network by assigning edge directions. However, these two methods could not completely mitigate the negative effects of inaccessibility and dilations. Thus, to approach the optimal network controllability, the edge directions must mitigate the negative effects of inaccessibility and dilations as much as possible. Finally, we propose the edge direction for optimal controllability. The optimal method has been found to be successfully useful on real-world and synthetic networks.

Keywords: complex network, dynamics, network control, optimization

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Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

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In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: Dong-Hyun Ha, Young-Min Ko, Chang-Bin Ha, Hyoung-Kyu Song

Abstract:

In Heterogeneous network (HetNet) can provide high quality of a service in a wireless communication system by composition of small cell networks. The composition of small cell networks improves cell coverage and capacity to the mobile users.Recently, various techniques using small cell networks have been researched in the wireless communication system. In this paper, the cooperative scheme obtaining high reliability is proposed in the small cell networks. The proposed scheme suggests a cooperative small cell system and the new signal transmission technique in the proposed system model. The new signal transmission technique applies a cyclic delay diversity (CDD) scheme based on the multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system to obtain improved performance. The improved performance of the proposed scheme is confirmed by the simulation results.

Keywords: adaptive transmission, cooperative communication, diversity gain, OFDM

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Authors: M. Shadmand

Abstract:

The curing process of insulation system for electrical machines plays a determinative role for its durability and reliability. Polar structure of insulating resin molecules and used filler of insulation system can be taken as an occasion to leverage it to enhance overall characteristics of insulation system, mechanically and electrically. The curing process regime for insulating system plays an important role for its mechanical and electrical characteristics by arranging the polymerization of chain structure for resin. In this research, the effect of electrical field application on in-curing insulating system for Global Vacuum Pressurized Impregnation (GVPI) system for traction motor was considered by performing the dissipation factor, polarization and de-polarization current (PDC) and voltage endurance (aging) measurements on sample test objects. Outcome results depicted obvious improvement in mechanical strength of the insulation system as well as higher electrical characteristics with routing and long-time (aging) electrical tests. Coming together, polarization of insulation system during curing process would enhance the machine life time. 

Keywords: insulation system, GVPI, PDC, aging

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Authors: Elias Nemer, Greg Vines

Abstract:

Time-frequency transformation and spectral representations of audio signals are commonly used in various machine learning applications. Training networks on frequency features such as the Mel-Spectrogram or Cochleogram have been proven more effective and convenient than training on-time samples. In practical realizations, these features are created on a different processor and/or pre-computed and stored on disk, requiring additional efforts and making it difficult to experiment with different features. In this paper, we provide a PyTorch framework for creating various spectral features as well as time-frequency transformation and time-domain filter-banks using the built-in trainable conv1d() layer. This allows computing these features on the fly as part of a larger network and enabling easier experimentation with various combinations and parameters. Our work extends the work in the literature developed for that end: First, by adding more of these features and also by allowing the possibility of either starting from initialized kernels or training them from random values. The code is written as a template of classes and scripts that users may integrate into their own PyTorch classes or simply use as is and add more layers for various applications.

Keywords: neural networks Mel-Spectrogram, chromagram, cochleogram, discrete Fourrier transform, PyTorch conv1d()

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Authors: Ramin Rostamkhani, Thurasamy Ramayah

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One of the most important topics of today’s industrial organizations is the challenging issue of supply chain management. In this field, scientists and researchers have published numerous practical articles and models, especially in the last decade. In this research, to our best knowledge, the discussion of data modeling of supply chain management components using well-known statistical distribution functions has been considered. The world of science owns mathematics, and showing the behavior of supply chain data based on the characteristics of statistical distribution functions is innovative research that has not been published anywhere until the moment of doing this research. In an analytical process, describing different aspects of functions including probability density, cumulative distribution, reliability, and failure function can reach the suitable statistical distribution function for each of the components of the supply chain management. It can be applied to predict the behavior data of the relevant component in the future. Providing a model to adapt the best statistical distribution function in the supply chain management components will be a big revolution in the field of the behavior of the supply chain management elements in today's industrial organizations. Demonstrating the final results of the proposed model by introducing the process capability indices before and after implementing it alongside verifying the approach through the relevant assessment as an acceptable verification is a final step. The introduced approach can save the required time and cost to achieve the organizational goals. Moreover, it can increase added value in the organization.

Keywords: analyzing, process capability indices, statistical distribution functions, supply chain management components

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Authors: Hanmei Peng, Yiqun Wang, Mao Tan, Zhuocen Dai, Yongxin Su

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Efficient energy management in multi-microgrid distribution systems holds significant importance for enhancing the economic benefits of regional power grids. To better balance conflicts among various stakeholders, a two-stage game-based collaborative optimization approach is proposed in this paper, effectively addressing the realistic scenario involving both competition and collaboration among stakeholders. The first stage, aimed at maximizing individual benefits, involves constructing a non-cooperative tariff game model for the distribution network and surplus microgrid. In the second stage, considering power flow and physical line capacity constraints we establish a cooperative P2P game model for the multi-microgrid distribution system, and the optimization involves employing the Lagrange method of multipliers to handle complex constraints. Simulation results demonstrate that the proposed approach can effectively improve the system economics while harmonizing individual and collective rationality.

Keywords: cooperative game, collaborative optimization, multi-microgrid distribution system, non-cooperative game

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Authors: José Anderson Rodrigues de Souza, Teles de Sales Bezerra, Saulo Aislan da Silva Eleuterio, Jeronimo Silva Rocha

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Mobile devices are increasingly occupying sectors of society and one of its most important features is mobility. However, the use of mobile devices is subject to the lifetime of the batteries. Thus, the use of energy batteries has become an important issue in the study of wireless network technologies. In this context, new solutions that enable aggregate energy efficiency not only through energy saving, and principally they are evaluated from a more realistic model of energy discharge, if easy adaptation to existing protocols. This paper presents a study on the energy needed and the lifetime for Wireless Sensor Networks (WSN) using a heterogeneous network and applying the LEACH protocol.

Keywords: wireless sensor networks, energy efficiency, heterogeneous, LEACH protocol

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Authors: Md. Rashidul Hasan, Atikur Rahman Baizid

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The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically.

Keywords: Bayes estimator, maximum likelihood estimator (MLE), modified linear exponential (MLINEX) loss function, Squared Error (SE) loss function, non-linear exponential (NLINEX) loss function

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Authors: Y. Ates, A. R. Boynuegri, M. Uzunoglu, A. Karakas

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The recent interest in alternative and renewable energy systems results in increased installed capacity ratio of such systems in total energy production of the world. Specifically, wind energy conversion systems (WECS) draw significant attention among possible alternative energy options, recently. On the contrary of the positive points of penetrating WECS in all over the world in terms of environment protection, energy independence of the countries, etc., there are significant problems to be solved for the grid connection of large scale WECS. The reactive power regulation, voltage variation suppression, etc. can be presented as major issues to be considered in this regard. Thus, this paper evaluates the application of a Static VAr Compensator (SVC) unit for the reactive power regulation and operation continuity of WECS during a fault condition. The system is modeled employing the IEEE 13 node test system. Thus, it is possible to evaluate the system performance with an overall grid simulation model close to real grid systems. The overall simulation model is developed in MATLAB/Simulink/SimPowerSystems® environments and the obtained results effectively match the target of the provided study.

Keywords: IEEE 13 bus distribution system, reactive power regulation, static VAr compensator, wind energy conversion system

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Authors: Issam Derkaoui, Mohammed Khenfouch, Bakang M. Mothudi, Malik Maaza, Izeddine Zorkani, Anouar Jorio

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Transition metal oxides nanoparticles with different morphologies have attracted a lot of attention recently owning to their distinctive geometries, and demonstrated promising electrical properties for various applications. In this paper, we discuss the time and annealing effects on the structural and electrical properties of vanadium oxides nanoparticles (VO-NPs) prepared by microwave method. In this sense, transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman Spectroscopy, Ultraviolet-visible absorbance spectra (Uv-Vis) and electrical conductivity were investigated. Hence, the annealing state and the time are two crucial parameters for the improvement of the optoelectronic properties. The use of these nanostructures is promising way for the development of technological applications especially for energy storage devices.

Keywords: Vanadium oxide, Microwave, Electrical conductivity, Optoelectronic properties

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Authors: David Hasurungan

Abstract:

This paper objective is to analyze the impact from photovoltaic system integration to power distribution network. The case study in Badak Liquefied Natural Gas (LNG) plant is presented in this paper. Badak LNG electricity network is operated in islanded mode. The total power generation in Badak LNG plant is significantly affected to feed gas supply. Meanwhile, to support the Government regulation, Badak LNG continuously implemented the grid-connected photovoltaic system in existing power distribution network. The impact between train operational mode change in Badak LNG plant and the growth of photovoltaic system is also encompassed in analysis. The analysis and calculation are performed using software Power Factory 15.1.

Keywords: power quality, distribution network, grid-connected photovoltaic system, power management system

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Authors: P. K. Guha Thakurta, Shouvik Roy, Bhawana Raj

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A congestion control scheme in mobile networks is proposed in this paper through a verification based model. The model proposed in this work is represented through performance metric like buffer Occupancy, latency and packet loss rate. Based on pre-defined values, each of the metric is introduced in terms of three different states. A Markov chain based model for the proposed work is introduced to monitor the occurrence of the corresponding state transitions. Thus, the estimation of the network status is obtained in terms of performance metric. In addition, the improved performance of our proposed model over existing works is shown with experimental results.

Keywords: congestion, mobile networks, buffer, delay, call drop, markov chain

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Authors: M. Abdollahi, S. N. Moghaddas Tafreshi

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Expanded polystyrene (EPS) geofoam is often used in below ground applications in geotechnical engineering. A most recent configuration system implemented in roadways to protect lifelines such as buried pipes, electrical cables and culvert systems could be consisted of two EPS geofoam blocks, “posts” placed on each side of the structure, an EPS block capping, “beam” put atop two posts, and soil cover on the beam. In this configuration, a rectangular void space will be built atop the lifeline. EPS blocks will stand all the imposed vertical forces due to their strength and deformability, thus the lifeline will experience no vertical stress. The present paper describes the results of a numerical study on the post and beam configuration subjected to the static loading. Three-dimensional finite element analysis using ABAQUS software is carried out to investigate the effect of different parameters such as beam thickness, soil thickness over the beam, post height to width ratio, EPS density, and free span between two posts, on the stress distribution and the deflection of the beam. The results show favorable performance of EPS geofoam for protecting sensitive infrastructures.

Keywords: beam, EPS block, numerical analysis, post, stress distribution

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Authors: Jiin-Yuh Jang, Yu-Feng Gan

Abstract:

In order to improve the comprehensive mechanical properties of the steel, the cooling rate, and the temperature distribution must be controlled in the cooling process. A three-dimensional numerical model for the prediction of the heat transfer coefficient distribution of H-beam in the controlled cooling process was performed in order to obtain the uniform temperature distribution and minimize the maximum stress and the maximum deformation after the controlled cooling. An algorithm developed with a simplified conjugated-gradient method was used as an optimizer to optimize the heat transfer coefficient distribution. The numerical results showed that, for the case of air cooling 5 seconds followed by water cooling 6 seconds with uniform the heat transfer coefficient, the cooling rate is 15.5 (℃/s), the maximum temperature difference is 85℃, the maximum the stress is 125 MPa, and the maximum deformation is 1.280 mm. After optimize the heat transfer coefficient distribution in control cooling process with the same cooling time, the cooling rate is increased to 20.5 (℃/s), the maximum temperature difference is decreased to 52℃, the maximum stress is decreased to 82MPa and the maximum deformation is decreased to 1.167mm.

Keywords: controlled cooling, H-Beam, optimization, thermal stress

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Authors: Yasser F. Hassan

Abstract:

The reduction or removal of noise in a color image is an essential part of image processing, whether the final information is used for human perception or for an automatic inspection and analysis. This paper describes the modeling system based on the rough neural network model to adaptive cellular automata for various image processing tasks and noise remover. In this paper, we consider the problem of object processing in colored image using rough neural networks to help deriving the rules which will be used in cellular automata for noise image. The proposed method is compared with some classical and recent methods. The results demonstrate that the new model is capable of being trained to perform many different tasks, and that the quality of these results is comparable or better than established specialized algorithms.

Keywords: rough sets, rough neural networks, cellular automata, image processing

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