Search results for: Artificial regeneration

Authors: Vishnu Pratap Singh Kirar

Abstract:

The main aim of a communication system is to achieve maximum performance. In Cognitive Radio any user or transceiver has ability to sense best suitable channel, while channel is not in use. It means an unlicensed user can share the spectrum of a licensed user without any interference. Though, the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision making capacity of CRN without affecting bandwidth, cost and signal rate.

Keywords: Artificial Neural Network, Cognitive Radio, Cognitive Radio Networks, Back Propagation, Spectrum Sensing.

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Authors: Petar Halachev

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Forecasting the values of the indicators, which characterize the effectiveness of performance of organizations is of great importance for their successful development. Such forecasting is necessary in order to assess the current state and to foresee future developments, so that measures to improve the organization-s activity could be undertaken in time. The article presents an overview of the applied mathematical and statistical methods for developing forecasts. Special attention is paid to artificial neural networks as a forecasting tool. Their strengths and weaknesses are analyzed and a synopsis is made of the application of artificial neural networks in the field of forecasting of the values of different education efficiency indicators. A method of evaluation of the activity of universities using the Balanced Scorecard is proposed and Key Performance Indicators for assessment of e-learning are selected. Resulting indicators for the evaluation of efficiency of the activity are proposed. An artificial neural network is constructed and applied in the forecasting of the values of indicators for e-learning efficiency on the basis of the KPI values.

Keywords: artificial neural network, balanced scorecard, e-learning

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Authors: U. Idachaba, F. Z. Wang, A. Qi, N. Helian

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Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence technique for clustering. It produces higher quality clusters compared to other population-based algorithms but with poor energy efficiency, cluster quality consistency and typically slower in convergence speed. Inspired by energy saving foraging behavior of natural honey bees this paper presents a Quality and Quantity Aware Artificial Bee Colony (Q2ABC) algorithm to improve quality of cluster identification, energy efficiency and convergence speed of the original ABC. To evaluate the performance of Q2ABC algorithm, experiments were conducted on a suite of ten benchmark UCI datasets. The results demonstrate Q2ABC outperformed ABC and K-means algorithm in the quality of clusters delivered.

Keywords: Artificial bee colony algorithm, clustering.

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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

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Authors: Ezinne M. Igwe

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Statements of facts have been made about Nollywood, a segment of the Nigerian film industry that has in recent times become phenomenal due largely to its quantity of production and specific production style. In the face of recent transformations reshaping the industry, matters have been arising which have not been given due academic attention from an industry player perspective. While re-addressing such issues like structure, policy and informality, this study benefits from a new perspective – that of a community member adopting participant observation to research into a familiar culture. With data drawn from an extensive ethnographic study of the industry, this paper examines these matters with an emphasis on structure and the industry’s overall political economy. Drawing from discourses on the new and old Nollywood labels and other current matters arising within the industry such as the MOPICON bill redraft, corporate financing and possibilities of regeneration, this paper examines structure and power struggle within Nollywood. These are championing regenerative processes that bring about formalization, professionalism and the quest for a transnational presence, which have only been superficially evaluated. Focused essentially on Nollywood’s political economy, this study critically analyses the transforming face of an informal industry, the consistent quest for structure, quality and standard, and issues of corporate sponsorship as possible trends of regeneration. It evaluates them as indicators of regeneration, questioning the possibilities of their sustenance in an industry experiencing increased interactions with the formal economy and an influx of young professionals. With findings that make sustained regeneration both certain (due to increased formal economy interaction) and uncertain (due to the dysfunctionality of the society and its political system), it concludes that the transforming face of the industry suggests impending gentrification of the industry.

Keywords: Formalization, MOPICON, Nollywood, Structure.

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Authors: Youngsuck Ji, Youngjoon Han, Hernsoo Hahn

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In this paper, we proposed the robust mobile object detection method for light effect in the night street image block based updating reference background model using block state analysis. Experiment image is acquired sequence color video from steady camera. When suddenly appeared artificial illumination, reference background model update this information such as street light, sign light. Generally natural illumination is change by temporal, but artificial illumination is suddenly appearance. So in this paper for exactly detect artificial illumination have 2 state process. First process is compare difference between current image and reference background by block based, it can know changed blocks. Second process is difference between current image-s edge map and reference background image-s edge map, it possible to estimate illumination at any block. This information is possible to exactly detect object, artificial illumination and it was generating reference background more clearly. Block is classified by block-state analysis. Block-state has a 4 state (i.e. transient, stationary, background, artificial illumination). Fig. 1 is show characteristic of block-state respectively [1]. Experimental results show that the presented approach works well in the presence of illumination variance.

Keywords: Block-state, Edge component, Reference backgroundi, Artificial illumination.

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Authors: İ. Kıyak, G. Gökmen

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Using of natural lighting has come into prominence in constructed buildings, especially in last ten years, under scope of energy efficiency. Natural lighting methods are one of the methods that aim to take advantage of day light in maximum level and decrease using of artificial lighting. Increasing of day light amount in buildings by using suitable methods will give optimum result in terms of comfort and energy saving when the daylight-artificial light integration is ensured with a suitable control system. Using of natural light in places that require lighting will ensure energy saving in great extent. With this study, it is aimed to save energy used for purpose of lighting. Under this scope, lighting of a scanning laboratory of a hospital was realized by using a lighting automation containing natural and artificial lighting. In natural lighting, light pipes were used and in artificial lighting, dimmable power LED modules were used. Necessity of lighting was followed with motion sensors. The lighting automation containing natural and artificial light was ensured with fuzzy logic control. At the scanning laboratory where this application was realized, energy saving in lighting was obtained.

Keywords: Daylight transfer, fuzzy logic controller, light pipe, Power LED.

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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The gases generated in oil filled transformers can be used for qualitative determination of incipient faults. The Dissolved Gas Analysis has been widely used by utilities throughout the world as the primarily diagnostic tool for transformer maintenance. In this paper, various Artificial Intelligence Techniques that have been used by the researchers in the past have been reviewed, some conclusions have been drawn and a sequential hybrid system has been proposed. The synergy of ANN and FIS can be a good solution for reliable results for predicting faults because one should not rely on a single technology when dealing with real–life applications.

Keywords: Dissolved Gas Analysis, Artificial IntelligenceTechniques, Incipient Faults, Transformer Fault Diagnosis, andHybrid Systems.

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Authors: K. Kalaiselvan, A. Elango, N. M. Nagarajan

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Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.

Keywords: Laser Beam Welding (LBW), Artificial Neural Networks (ANN), Optimization, Titanium and Aluminium sheets.

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Authors: Birol Yildiz, Abdullah Yalama, Metin Coskun

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Many studies have shown that Artificial Neural Networks (ANN) have been widely used for forecasting financial markets, because of many financial and economic variables are nonlinear, and an ANN can model flexible linear or non-linear relationship among variables. The purpose of the study was to employ an ANN models to predict the direction of the Istanbul Stock Exchange National 100 Indices (ISE National-100). As a result of this study, the model forecast the direction of the ISE National-100 to an accuracy of 74, 51%.

Keywords: Artificial Neural Networks, Istanbul StockExchange, Non-linear Modeling.

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Authors: Ali Khaleel Kareem, Shian Gao, Ahmed Qasim Ahmed

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A numerical investigation of unsteady mixed convection heat transfer in a 3D moving top wall enclosure, which has a central rotating cylinder and uses either artificial roughness on the bottom hot plate or smooth bottom hot plate to study the heat transfer enhancement, is completed for fixed circular cylinder, and anticlockwise and clockwise rotational speeds, -1 ≤ Ω ≤ 1, at Reynolds number of 5000. The top lid-driven wall was cooled, while the other remaining walls that completed obstructed cubic were kept insulated and motionless. A standard k-ε model of Unsteady Reynolds-Averaged Navier-Stokes (URANS) method is involved to deal with turbulent flow. It has been clearly noted that artificial roughness can strongly control the thermal fields and fluid flow patterns. Ultimately, the heat transfer rate has been dramatically increased by involving artificial roughness on the heated bottom wall in the presence of rotating cylinder.

Keywords: Artificial roughness, Lid-driven cavity, Mixed convection heat transfer, Rotating cylinder, URANS method.

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Authors: T. Vigneswari, M. A. Maluk Mohamed

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Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Heterogeneous Earliest First Min- Min Artificial Bee Colony (CHMM-ABC), to optimally schedule jobs for the available resources. The proposed model utilizes a novel Heterogeneous Earliest Finish Time (HEFT) Heuristic Algorithm along with Min-Min algorithm to identify the initial food source. Simulation results show the performance improvement of the proposed algorithm over other swarm intelligence techniques.

Keywords: Grid Computing, Grid Scheduling, Heterogeneous Earliest Finish Time (HEFT), Artificial Bee colony (ABC) Algorithm, Resource Management.

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Authors: Vahid Zeighami, Mohsen Ghasemi, Reza Akbari

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In this work, a Multi-Level Artificial Bee Colony (called MLABC) for optimizing numerical test functions is presented. In MLABC, two species are used. The first species employs n colonies where each of them optimizes the complete solution vector. The cooperation between these colonies is carried out by exchanging information through a leader colony, which contains a set of elite bees. The second species uses a cooperative approach in which the complete solution vector is divided to k sub-vectors, and each of these sub-vectors is optimized by a colony. The cooperation between these colonies is carried out by compiling sub-vectors into the complete solution vector. Finally, the cooperation between two species is obtained by exchanging information. The proposed algorithm is tested on a set of well-known test functions. The results show that MLABC algorithm provides efficiency and robustness to solve numerical functions.

Keywords: Artificial bee colony, cooperative artificial bee colony, multilevel cooperation.

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Authors: N. Hatraf, N. Moummi

Abstract:

The interest in air conditioning using renewable energies is increasing. The thermal energy produced from the solar energy can be converted to useful cooling and heating through the thermochemical or thermophysical processes by using thermally activated energy conversion systems. The ambient air contains so much water that very high dehumidification rates are required. For a continuous dehumidification of the process air, the water adsorbed on the desiccant material has to be removed, which is done by allowing hot air to flow through the desiccant material (regeneration). A solid desiccant cooling system transfers moisture from the inlet air to the silica gel by using two processes: Absorption process and the regeneration process. The main aim of this paper is to study how the dehumidification rate, the generation temperature and many other factors influence the efficiency of a solid desiccant system by using TRNSYS software. The results show that the desiccant system could be used to decrease the humidity rate of the entering air.

Keywords: Dehumidification, efficiency, humidity, TRNSYS.

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Authors: Bin Cai, Shilong Wang, Haibo Hu

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The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. This paper presents a hybrid artificial immune system for the JSSP with the objective of minimizing makespan. The proposed approach combines the artificial immune system, which has a powerful global exploration capability, with the local search method, which can exploit the optimal antibody. The antibody coding scheme is based on the operation based representation. The decoding procedure limits the search space to the set of full active schedules. In each generation, a local search heuristic based on the neighborhood structure proposed by Nowicki and Smutnicki is applied to improve the solutions. The approach is tested on 43 benchmark problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Keywords: Artificial immune system, Job shop scheduling problem, Local search, Metaheuristic algorithm

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Authors: Surinder Deswal, Mahesh Pal

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An Artificial Neural Network based modeling technique has been used to study the influence of different combinations of meteorological parameters on evaporation from a reservoir. The data set used is taken from an earlier reported study. Several input combination were tried so as to find out the importance of different input parameters in predicting the evaporation. The prediction accuracy of Artificial Neural Network has also been compared with the accuracy of linear regression for predicting evaporation. The comparison demonstrated superior performance of Artificial Neural Network over linear regression approach. The findings of the study also revealed the requirement of all input parameters considered together, instead of individual parameters taken one at a time as reported in earlier studies, in predicting the evaporation. The highest correlation coefficient (0.960) along with lowest root mean square error (0.865) was obtained with the input combination of air temperature, wind speed, sunshine hours and mean relative humidity. A graph between the actual and predicted values of evaporation suggests that most of the values lie within a scatter of ±15% with all input parameters. The findings of this study suggest the usefulness of ANN technique in predicting the evaporation losses from reservoirs.

Keywords: Artificial neural network, evaporation losses, multiple linear regression, modeling.

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Authors: Kyoung Hoon Kim

Abstract:

ORC (Organic Rankine Cycle) has potential of reducing consumption of fossil fuels and has many favorable characteristics to exploit low-temperature heat sources. In this work thermodynamic performance of ORC with regeneration is comparatively assessed for various working fluids. Special attention is paid to the effects of system parameters such as the turbine inlet pressure on the characteristics of the system such as net work production, heat input, volumetric flow rate per 1 MW of net work and quality of the working fluid at turbine exit as well as thermal efficiency. Results show that for a given source the thermal efficiency generally increases with increasing of the turbine inlet pressure however has optimal condition for working fluids of low critical pressure such as iso-pentane or n-pentane.

Keywords: low-grade energy source, organic Rankine cycle(ORC), regeneration, Patel-Teja equation.

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Authors: João Paulo Teixeira, Paula Odete Fernandes

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The authors have been developing several models based on artificial neural networks, linear regression models, Box- Jenkins methodology and ARIMA models to predict the time series of tourism. The time series consist in the “Monthly Number of Guest Nights in the Hotels" of one region. Several comparisons between the different type models have been experimented as well as the features used at the entrance of the models. The Artificial Neural Network (ANN) models have always had their performance at the top of the best models. Usually the feed-forward architecture was used due to their huge application and results. In this paper the author made a comparison between different architectures of the ANNs using simply the same input. Therefore, the traditional feed-forward architecture, the cascade forwards, a recurrent Elman architecture and a radial based architecture were discussed and compared based on the task of predicting the mentioned time series.

Keywords: Artificial Neural Network Architectures, time series forecast, tourism.

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Authors: Hansung Wan, Hyungkwan Cho, Kiho Sung, Hongkyu Kim

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The amount of urban artificial heat which affects the urban temperature rise in urban meteorology was investigated in order to clarify the relationships between urbanization and urban meteorology in this study. The results of calculation to identify how urban temperate was increased through the establishment of a model for measuring the amount of urban artificial heat and theoretical testing revealed that the amount of urban artificial heat increased urban temperature by plus or minus 0.23 ˚ C in 2007 compared with 1996, statistical methods (correlation and regression analysis) to clarify the relationships between urbanization and urban weather were as follows. New design techniques and urban growth management are necessary from urban growth management point of view suggested from this research at city design phase to decrease urban temperature rise and urban torrential rain which can produce urban disaster in terms of urban meteorology by urbanization.

Keywords: The amount of urban artificial heat, Urban growth management, Urbanization, Urban meteorology

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Authors: S. M. Shahinul Islam, Israt Ara, Narendra Tuteja, Sreeramanan Subramaniam

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Through anther and microspore culture methods, complete homozygous plants can be produced within a year as compared to the long inbreeding method. Isolated microspore culture is one of the most important techniques for rapid development of haploid plants. The efficiency of this method is influenced by several factors such as cultural conditions, growth regulators, plant media, pretreatments, physical and growth conditions of the donor plants, pollen isolation procedure, etc. The main purpose of this study was to improve the isolated microspore culture protocol in order to increase the efficiency of embryoids, its regeneration and reducing albinisms. Under this study we have tested mainly three different microspore isolation procedures by glass rod, homozeniger and by blending and found the efficiency on gametic embryogenesis. There are three types of media viz. washing, pre-culture and induction was used. The induction medium as AMC (modified MS) supplemented by 2, 4-D (2.5 mg/l), kinetin (0.5 mg/l) and higher amount of D-Manitol (90 g/l) instead of sucrose and two types of amino acids (L-glutamine and L-serine) were used. Out of three main microspore isolation procedure by homogenizer isolation (P₄) showed best performance on ELS induction (177%) and green plantlets (104%) compared with other techniques. For all cases albinisims occurred but microspore isolation from excised anthers by glass rod and homogenizer showed lesser numbers of albino plants that was also one of the important findings in this study.

Keywords: Androgenesis, pretreatment, microspore culture, regeneration, albino plants, Oryza sativa.

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Authors: E. Nemati Lay, M. Peymani, E. Sanjari

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Prediction of viscosity of natural gas is an important parameter in the energy industries such as natural gas storage and transportation. In this study viscosity of different compositions of natural gas is modeled by using an artificial neural network (ANN) based on back-propagation method. A reliable database including more than 3841 experimental data of viscosity for testing and training of ANN is used. The designed neural network can predict the natural gas viscosity using pseudo-reduced pressure and pseudo-reduced temperature with AARD% of 0.221. The accuracy of designed ANN has been compared to other published empirical models. The comparison indicates that the proposed method can provide accurate results.

Keywords: Artificial neural network, Empirical correlation, Natural gas, Viscosity

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Authors: B. Mukanova, N. Glazyrina, S. Glazyrin

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The formulated problem of optimization of the technological process of water treatment for thermal power plants is considered in this article. The problem is of multiparametric nature. To optimize the process, namely, reduce the amount of waste water, a new technology was developed to reuse such water. A mathematical model of the technology of wastewater reuse was developed. Optimization parameters were determined. The model consists of a material balance equation, an equation describing the kinetics of ion exchange for the non-equilibrium case and an equation for the ion exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion exchange. A direct problem of calculating the impurity concentration at the outlet of the water treatment plant was numerically solved. The direct problem was approximated by an implicit point-to-point computation difference scheme. The inverse problem was formulated as relates to determination of the parameters of the mathematical model of the water treatment plant operating in non-equilibrium conditions. The formulated inverse problem was solved. Following the results of calculation the time of start of the filter regeneration process was determined, as well as the period of regeneration process and the amount of regeneration and wash water. Multi-parameter optimization of water treatment process for thermal power plants allowed decreasing the amount of wastewater by 15%.

Keywords: Direct problem, multiparametric optimization, optimization parameters, water treatment.

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Authors: Musbah M. Aqel, P. K. Mahanti, Soumya Banerjee

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This paper describes a simulation model for analyzing artificial emotion injected to design the game characters. Most of the game storyboard is interactive in nature and the virtual characters of the game are equipped with an individual personality and dynamic emotion value which is similar to real life emotion and behavior. The uncertainty in real expression, mood and behavior is also exhibited in game paradigm and this is focused in the present paper through a fuzzy logic based agent and storyboard. Subsequently, a pheromone distribution or labeling is presented mimicking the behavior of social insects.

Keywords: Artificial Emotion, Fuzzy logic, Game character, Pheromone label

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Authors: P. Naderi, S.M.T. Bathaee, R. Hoseinnezhad, R. Chini

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Using electrical machine in conventional vehicles, also called hybrid vehicles, has become a promising control scheme that enables some manners for fuel economy and driver assist for better stability. In this paper, vehicle stability control, fuel economy and Driving/Regeneration braking for a 4WD hybrid vehicle is investigated by using an electrical machine on each non-driven wheels. In front wheels driven vehicles, fuel economy and regenerative braking can be obtained by summing torques applied on rear wheels. On the other hand, unequal torques applied to rear wheels provides enhanced safety and path correction in steering. In this paper, a model with fourteen degrees of freedom is considered for vehicle body, tires and, suspension systems. Thereafter, powertrain subsystems are modeled. Considering an electrical machine on each rear wheel, a fuzzy controller is designed for each driving, braking, and stability conditions. Another fuzzy controller recognizes the vehicle requirements between the driving/regeneration and stability modes. Intelligent vehicle control to multi objective operation and forward simulation are the paper advantages. For reaching to these aims, power management control and yaw moment control will be done by three fuzzy controllers. Also, the above mentioned goals are weighted by another fuzzy sub-controller base on vehicle dynamic. Finally, Simulations performed in MATLAB/SIMULINK environment show that the proposed structure can enhance the vehicle performance in different modes effectively.

Keywords: Hybrid, pitch, roll, regeneration, yaw.

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Authors: H.Mohammadi Majd, M.Jalali Azizpour

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In this paper back-propagation artificial neural network (BPANN )with Levenberg–Marquardt algorithm is employed to predict the deformation of the upsetting process. To prepare a training set for BPANN, some finite element simulations were carried out. The input data for the artificial neural network are a set of parameters generated randomly (aspect ratio d/h, material properties, temperature and coefficient of friction). The output data are the coefficient of polynomial that fitted on barreling curves. Neural network was trained using barreling curves generated by finite element simulations of the upsetting and the corresponding material parameters. This technique was tested for three different specimens and can be successfully employed to predict the deformation of the upsetting process

Keywords: Back-propagation artificial neural network(BPANN), prediction, upsetting

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Authors: A.Atash J ameh

Abstract:

Sarkhoon gas plant, located in south of Iran, has been installed to removal H2S contained in a high pressure natural gas stream. The solvent used for the H2S removal from gaseous stream is 34% by weight (wt%) Di-ethanol amine (DEA) solutions. Due to increasing concentration of heat stable salt (HSS) in solvent, corrosivity of amine solution had been increased. Reports indicated that there was corrosion on the shell of regeneration column. Because source formation of HSS was unknown, we decided to control the amount of HSS at the limit less than 3% wt amine solvent. Therefore, two small columns were filled by strong anionic base and carbon active, and then polluted amine was passed through beds. Finally a temporary amine recovery package on industrial scale was made based on laboratory’s results. From economical point of view we could save $700000 beside corrosion occurrence of the stripping column has been vigorously decreased.

Keywords: Amines, corrosion, heat stable salt, resin anionic.

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Authors: Zohreh Sheikh Khozani, Wan Hanna Melini Wan Mohtar, Mojtaba Porhemmat

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Prediction of wall shear stress in a rectangular channel, with non-homogeneous roughness distribution, was studied. Estimation of shear stress is an important subject in hydraulic engineering, since it affects the flow structure directly. In this study, the Genetic Algorithm Artificial (GAA) neural network is introduced as a hybrid methodology of the Artificial Neural Network (ANN) and modified Genetic Algorithm (GA) combination. This GAA method was employed to predict the wall shear stress. Various input combinations and transfer functions were considered to find the most appropriate GAA model. The results show that the proposed GAA method could predict the wall shear stress of open channels with high accuracy, by Root Mean Square Error (RMSE) of 0.064 in the test dataset. Thus, using GAA provides an accurate and practical simple-to-use equation.

Keywords: Artificial neural network, genetic algorithm, genetic programming, rectangular channel, shear stress.

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Authors: Tim Falk, Chris Chatwin

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This study explores the possibility of a space station that will occupy a geostationary equatorial orbit (GEO) and create artificial gravity using centripetal acceleration. The concept of the station is to create a habitable, safe environment that can increase the possibility of space tourism by reducing the wide variation of hazards associated with space exploration. The ability to control the intensity of artificial gravity through Hall-effect thrusters will allow experiments to be carried out at different levels of artificial gravity. A feasible prototype model was built to convey the concept and to enable cost estimation. The SpaceX Falcon Heavy rocket with a 26,700 kg payload to GEO was selected to take the 675 tonne spacecraft into orbit; space station construction will require up to 30 launches, this would be reduced to 5 launches when the SpaceX BFR becomes available. The estimated total cost of implementing the Sussex Biocompatible International Space Station (BISS) is approximately $47.039 billion, which is very attractive when compared to the cost of the International Space Station, which cost $150 billion.

Keywords: Artificial gravity, biocompatible, geostationary orbit, space station.

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Authors: Jeewanee Bamunusinghe, Damminda Alahakoon

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Visual inputs are one of the key sources from which humans perceive the environment and 'understand' what is happening. Artificial systems perceive the visual inputs as digital images. The images need to be processed and analysed. Within the human brain, processing of visual inputs and subsequent development of perception is one of its major functionalities. In this paper we present part of our research project, which aims at the development of an artificial model for visual perception (or 'understanding') based on the human perceptive and cognitive systems. We propose a new model for perception from visual inputs and a way of understaning or interpreting images using the model. We demonstrate the implementation and use of the model with a real image data set.

Keywords: Image understanding, percept, visual perception.

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

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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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