Suitable Partner Node Selection and Resource Allocation in Cooperative Wireless Communication Using the Trade-Off Game

The performance of any cooperative communication system depends largely on the selection of a proper partner. Another important factor to consider is an efficient allocation of resource like power by the source node to help it in forwarding information to the destination. In this paper, we look at the concepts of partner selection and resource (power) allocation for a distributed communication network. A type of non-cooperative game referred to as Trade-Off game is employed so as to jointly consider the utilities of the source and relay nodes, where in this case, the source is the node that requires help with forwarding of its information while the partner is the node that is willing to help in forwarding the source node’s information, but at a price. The approach enables the source node to maximize its utility by selecting a partner node based on (i) the proximity of the partner node to the source and destination nodes, and (ii) the price the partner node will charge for the help being rendered. Our proposed scheme helps the source locate and select the relay nodes at ‘better’ locations and purchase power optimally from them. It also aids the contending relay nodes maximize their own utilities as well by asking proper prices. Our game scheme is seen to converge to unique equilibrium.

Multi-Linear Regression Based Prediction of Mass Transfer by Multiple Plunging Jets

The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90O; whereas, multiple inclined plunging jets have jet impact angle of θ = 60O. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (KLa) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets.

CVOIP-FRU: Comprehensive VoIP Forensics Report Utility

Voice over Internet Protocol (VoIP) products is an emerging technology that can contain forensically important information for a criminal activity. Without having the user name and passwords, this forensically important information can still be gathered by the investigators. Although there are a few VoIP forensic investigative applications available in the literature, most of them are particularly designed to collect evidence from the Skype product. Therefore, in order to assist law enforcement with collecting forensically important information from variety of Betamax VoIP tools, CVOIP-FRU framework is developed. CVOIP-FRU provides a data gathering solution that retrieves usernames, contact lists, as well as call and SMS logs from Betamax VoIP products. It is a scripting utility that searches for data within the registry, logs and the user roaming profiles in Windows and Mac OSX operating systems. Subsequently, it parses the output into readable text and html formats. One superior way of CVOIP-FRU compared to the other applications that due to intelligent data filtering capabilities and cross platform scripting back end of CVOIP-FRU, it is expandable to include other VoIP solutions as well. Overall, this paper reveals the exploratory analysis performed in order to find the key data paths and locations, the development stages of the framework, and the empirical testing and quality assurance of CVOIP-FRU.

Statistical Modeling of Constituents in Ash Evolved From Pulverized Coal Combustion

Industries using conventional fossil fuels have an  interest in better understanding the mechanism of particulate  formation during combustion since such is responsible for emission  of undesired inorganic elements that directly impact the atmospheric  pollution level. Fine and ultrafine particulates have tendency to  escape the flue gas cleaning devices to the atmosphere. They also  preferentially collect on surfaces in power systems resulting in  ascending in corrosion inclination, descending in the heat transfer  thermal unit, and severe impact on human health. This adverseness  manifests particularly in the regions of world where coal is the  dominated source of energy for consumption.  This study highlights the behavior of calcium transformation as  mineral grains verses organically associated inorganic components  during pulverized coal combustion. The influence of existing type of  calcium on the coarse, fine and ultrafine mode formation mechanisms  is also presented. The impact of two sub-bituminous coals on particle  size and calcium composition evolution during combustion is to be  assessed. Three mixed blends named Blends 1, 2, and 3 are selected  according to the ration of coal A to coal B by weight. Calcium  percentage in original coal increases as going from Blend 1 to 3.  A mathematical model and a new approach of describing  constituent distribution are proposed. Analysis of experiments of  calcium distribution in ash is also modeled using Poisson distribution.  A novel parameter, called elemental index λ, is introduced as a  measuring factor of element distribution.  Results show that calcium in ash that originally in coal as mineral  grains has index of 17, whereas organically associated calcium  transformed to fly ash shown to be best described when elemental  index λ is 7.  As an alkaline-earth element, calcium is considered the  fundamental element responsible for boiler deficiency since it is the  major player in the mechanism of ash slagging process. The  mechanism of particle size distribution and mineral species of ash  particles are presented using CCSEM and size-segregated ash  characteristics. Conclusions are drawn from the analysis of  pulverized coal ash generated from a utility-scale boiler.  

Overview of Energy Savings and Efficiency Strategies at the Hospitals

Hospitals represent approximately 6% of total energy consumption in the utility buildings sector. Heating, Ventilation and Air Conditioning (HVAC) systems are the major part of electrical energy consumption at the hospitals. The air-conditioning system is responsible for around 70% of total electricity consumption. Electric motors and lighting systems in a hospital represent approximately 19% and 21% of the total energy consumption, respectively. In this paper, profiles of hospital energy end-use consumption and an overview of energy saving areas at the hospitals are presented.

Students’ Perception of Using Dental e-Models in an Inquiry-Based Curriculum

Aim: To investigate students’ perceptions of using e-models in an inquiry-based curriculum. Approach: 52 second-year dental students completed a pre- and post-test questionnaire relating to their perceptions of e-models and their use in inquiry-based learning. The pre-test occurred prior to any learning with e-models. The follow-up survey was conducted after one year's experience of using e-models. Results: There was no significant difference between the two sets of questionnaires regarding students’ perceptions of the usefulness of e-models and their willingness to use e-models in future inquiry-based learning. Most students preferred using both plaster models and e-models in tandem. Conclusion: Students did not change their attitude towards e-models and most of them agreed or were neutral that e-models are useful in inquiry-based learning. Whilst recognizing the utility of 3D models for learning, students' preference for combining these with solid models has implications for the development of haptic sensibility in an operative discipline.

Minimizing Fresh and Wastewater Using Water Pinch Technique in Petrochemical Industries

This research involves the design and analysis of pinch-based water/wastewater networks to minimize water utility in the petrochemical and petroleum industries. A study has been done on Tehran Oil Refinery to analyze feasibilities of regeneration, reuse and recycling of water network. COD is considered as a single key contaminant. Amount of freshwater was reduced about 149m3/h (43.8%) regarding COD. Re-design (or retrofitting) of water allocation in the networks was undertaken. The results were analyzed through graphical method and mathematical programming technique which clearly demonstrated that amount of required water would be determined by mass transfer of COD.

Control Strategy of SRM Converters for Power Quality Improvement

The selection of control strategy depends on the converters of the drive including power, speed, performance and the possible system costs. A number of attempts were therefore made in recent times to develop novel power electronic converter structures for SRM drives, based on the utilization. Many of the converters with variable speed drives have no input power factor correction circuits. This results in harmonic pollution of the utility supply, which should be avoided. The effect of power factor variation in terms of harmonic content is also analyzed in this study. The proposed topologies were simulated using MATLAB / Simulink software package and the results are obtained.

Optimizing Hadoop Block Placement Policy and Cluster Blocks Distribution

The current Hadoop block placement policy do not fairly and evenly distributes replicas of blocks written to datanodes in a Hadoop cluster. This paper presents a new solution that helps to keep the cluster in a balanced state while an HDFS client is writing data to a file in Hadoop cluster. The solution had been implemented, and test had been conducted to evaluate its contribution to Hadoop distributed file system. It has been found that, the solution has lowered global execution time taken by Hadoop balancer to 22 percent. It also has been found that, Hadoop balancer respectively over replicate 1.75 and 3.3 percent of all re-distributed blocks in the modified and original Hadoop clusters. The feature that keeps the cluster in a balanced state works as a core part to Hadoop system and not just as a utility like traditional balancer. This is one of the significant achievements and uniqueness of the solution developed during the course of this research work.

Centralized Controller for Microgrid

This paper, proposes a control system for use with microgrid consiste of  multiple small scale embedded generation networks (SSEG networks) connected to the 33kV distribution network. The proposed controller controls power flow in the grid-connected mode of operation, enables voltage and frequency control when the SSEG networks are islanded, and resynchronises the SSEG networks with the utility before reconnecting them. The performance of the proposed controller has been tested in simulations using PSCAD.

Comparison of Router Intelligent and Cooperative Host Intelligent Algorithms in a Continuous Model of Fixed Telecommunication Networks

The performance of state of the art worldwide telecommunication networks strongly depends on the efficiency of the applied routing mechanism. Game theoretical approaches to this problem offer new solutions. In this paper a new continuous network routing model is defined to describe data transfer in fixed telecommunication networks of multiple hosts. The nodes of the network correspond to routers whose latency is assumed to be traffic dependent. We propose that the whole traffic of the network can be decomposed to a finite number of tasks, which belong to various hosts. To describe the different latency-sensitivity, utility functions are defined for each task. The model is used to compare router and host intelligent types of routing methods, corresponding to various data transfer protocols. We analyze host intelligent routing as a transferable utility cooperative game with externalities. The main aim of the paper is to provide a framework in which the efficiency of various routing algorithms can be compared and the transferable utility game arising in the cooperative case can be analyzed.

Secondary Ion Mass Spectrometry of Proteins

The adsorption of bovine serum albumin (BSA), immunoglobulin G (IgG) and fibrinogen (Fgn) on fluorinated selfassembled monolayers have been studied using time of flight secondary ion mass spectrometry (ToF-SIMS) and Spectroscopic Ellipsometry (SE). The objective of the work has to establish the utility of ToF-SIMS for the determination of the amount of protein adsorbed on the surface. Quantification of surface adsorbed proteins was carried out using SE and a good correlation between ToF-SIMS results and SE was achieved. The surface distribution of proteins were also analysed using Atomic Force Microscopy (AFM). We show that the surface distribution of proteins strongly affect the ToFSIMS results.

Intelligent Neural Network Based STLF

Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

Reliability Assessment of Bangladesh Power System Using Recursive Algorithm

An electric utility-s main concern is to plan, design, operate and maintain its power supply to provide an acceptable level of reliability to its users. This clearly requires that standards of reliability be specified and used in all three sectors of the power system, i.e., generation, transmission and distribution. That is why reliability of a power system is always a major concern to power system planners. This paper presents the reliability analysis of Bangladesh Power System (BPS). Reliability index, loss of load probability (LOLP) of BPS is evaluated using recursive algorithm and considering no de-rated states of generators. BPS has sixty one generators and a total installed capacity of 5275 MW. The maximum demand of BPS is about 5000 MW. The relevant data of the generators and hourly load profiles are collected from the National Load Dispatch Center (NLDC) of Bangladesh and reliability index 'LOLP' is assessed for the period of last ten years.

Lifetime Maximization in Wireless Ad Hoc Networks with Network Coding and Matrix Game

In this paper, we present a matrix game-theoretic cross-layer optimization formulation to maximize the network lifetime in wireless ad hoc networks with network coding. To this end, we introduce a cross-layer formulation of general NUM (network utility maximization) that accommodates routing, scheduling, and stream control from different layers in the coded networks. Specifically, for the scheduling problem and then the objective function involved, we develop a matrix game with the strategy sets of the players corresponding to hyperlinks and transmission modes, and design the payoffs specific to the lifetime. In particular, with the inherit merit that matrix game can be solved with linear programming, our cross-layer programming formulation can benefit from both game-based and NUM-based approaches at the same time by cooperating the programming model for the matrix game with that for the other layers in a consistent framework. Finally, our numerical example demonstrates its performance results on a well-known wireless butterfly network to verify the cross-layer optimization scheme.

Wind Energy Development in the African Great Lakes Region to Supplement the Hydroelectricity in the Locality: A Case Study from Tanzania

The African Great Lakes Region refers to the zone around lakes Victoria, Tanganyika, Albert, Edward, Kivu, and Malawi. The main source of electricity in this region is hydropower whose systems are generally characterized by relatively weak, isolated power schemes, poor maintenance and technical deficiencies with limited electricity infrastructures. Most of the hydro sources are rain fed, and as such there is normally a deficiency of water during the dry seasons and extended droughts. In such calamities fossil fuels sources, in particular petroleum products and natural gas, are normally used to rescue the situation but apart from them being nonrenewable, they also release huge amount of green house gases to our environment which in turn accelerates the global warming that has at present reached an amazing stage. Wind power is ample, renewable, widely distributed, clean, and free energy source that does not consume or pollute water. Wind generated electricity is one of the most practical and commercially viable option for grid quality and utility scale electricity production. However, the main shortcoming associated with electric wind power generation is fluctuation in its output both in space and time. Before making a decision to establish a wind park at a site, the wind speed features there should therefore be known thoroughly as well as local demand or transmission capacity. The main objective of this paper is to utilise monthly average wind speed data collected from one prospective site within the African Great Lakes Region to demonstrate that the available wind power there is high enough to generate electricity. The mean monthly values were calculated from records gathered on hourly basis for a period of 5 years (2001 to 2005) from a site in Tanzania. The documentations that were collected at a height of 2 m were projected to a height of 50 m which is the standard hub height of wind turbines. The overall monthly average wind speed was found to be 12.11 m/s whereas June to November was established to be the windy season as the wind speed during the session is above the overall monthly wind speed. The available wind power density corresponding to the overall mean monthly wind speed was evaluated to be 1072 W/m2, a potential that is worthwhile harvesting for the purpose of electric generation.

A PI Controller for Enhancing the Transient Stability of Multi Pulse Inverter Based Static Synchronous Series Compensator (SSSC) With Superconducting Magnetic Energy Storage(SMES)

The power system network is becoming more complex nowadays and it is very difficult to maintain the stability of the system. Today-s enhancement of technology makes it possible to include new energy storage devices in the electric power system. In addition, with the aid of power electronic devices, it is possible to independently exchange active and reactive power flow with the utility grid. The main purpose of this paper proposes a Proportional – Integral (PI) control based 48 – pulse Inverter based Static Synchronous Series Compensator (SSSC) with and without Superconducting Magnetic Energy Storage (SMES) used for enhancing the transient stability and regulating power flow in automatic mode. Using a test power system through the dynamic simulation in Matlab/Simulink platform validates the performance of the proposed SSSC with and without SMES system.

Neural Networks: From Black Box towards Transparent Box Application to Evapotranspiration Modeling

Neural networks are well known for their ability to model non linear functions, but as statistical methods usually does, they use a no parametric approach thus, a priori knowledge is not obvious to be taken into account no more than the a posteriori knowledge. In order to deal with these problematics, an original way to encode the knowledge inside the architecture is proposed. This method is applied to the problem of the evapotranspiration inside karstic aquifer which is a problem of huge utility in order to deal with water resource.

STLF Based on Optimized Neural Network Using PSO

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

A Study on Reducing Malicious Replies on the Internet: An Approach by Game Theory

Since the advent of the information era, the Internet has brought various positive effects in everyday life. Nevertheless, recently, problems and side-effects have been noted. Internet witch-trials and spread of pornography are only a few of these problems.In this study, problems and causes of malicious replies on internet boards were analyzed, using the key ideas of game theory. The study provides a mathematical model for the internet reply game to devise three possible plans that could efficiently counteract malicious replies. Furthermore, seven specific measures that comply with one of the three plans were proposed and evaluated according to the importance and utility of each measure using the orthogonal array survey and SPSS conjoint analysis.The conclusion was that the most effective measure would be forbidding unsigned user access to malicious replies. Also notable was that some analytically proposed measures, when implemented, could backfire and encourage malicious replies.