Hybrid Weighted Multiple Attribute Decision Making Handover Method for Heterogeneous Networks

Small cell deployment in 5G networks is a promising technology to enhance the capacity and coverage. However, unplanned deployment may cause high interference levels and high number of unnecessary handovers, which in turn result in an increase in the signalling overhead. To guarantee service continuity, minimize unnecessary handovers and reduce signalling overhead in heterogeneous networks, it is essential to properly model the handover decision problem. In this paper, we model the handover decision problem using Multiple Attribute Decision Making (MADM) method, specifically Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and propose a hybrid TOPSIS method to control the handover in heterogeneous network. The proposed method adopts a hybrid weighting policy, which is a combination of entropy and standard deviation. A hybrid weighting control parameter is introduced to balance the impact of the standard deviation and entropy weighting on the network selection process and the overall performance. Our proposed method show better performance, in terms of the number of frequent handovers and the mean user throughput, compared to the existing methods.

Handover for Dense Small Cells Heterogeneous Networks: A Power-Efficient Game Theoretical Approach

In this paper, a non-cooperative game method is formulated where all players compete to transmit at higher power. Every base station represents a player in the game. The game is solved by obtaining the Nash equilibrium (NE) where the game converges to optimality. The proposed method, named Power Efficient Handover Game Theoretic (PEHO-GT) approach, aims to control the handover in dense small cell networks. Players optimize their payoff by adjusting the transmission power to improve the performance in terms of throughput, handover, power consumption and load balancing. To select the desired transmission power for a player, the payoff function considers the gain of increasing the transmission power. Then, the cell selection takes place by deploying Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A game theoretical method is implemented for heterogeneous networks to validate the improvement obtained. Results reveal that the proposed method gives a throughput improvement while reducing the power consumption and minimizing the frequent handover.

Movie Genre Preference Prediction Using Machine Learning for Customer-Based Information

Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Heat Transfer and Entropy Generation in a Partial Porous Channel Using LTNE and Exothermicity/Endothermicity Features

This work aims to provide a comprehensive study on the heat transfer and entropy generation rates of a horizontal channel partially filled with a porous medium which experiences internal heat generation or consumption due to exothermic or endothermic chemical reaction. The focus has been given to the local thermal non-equilibrium (LTNE) model. The LTNE approach helps us to deliver more accurate data regarding temperature distribution within the system and accordingly to provide more accurate Nusselt number and entropy generation rates. Darcy-Brinkman model is used for the momentum equations, and constant heat flux is assumed for boundary conditions for both upper and lower surfaces. Analytical solutions have been provided for both velocity and temperature fields. By incorporating the investigated velocity and temperature formulas into the provided fundamental equations for the entropy generation, both local and total entropy generation rates are plotted for a number of cases. Bifurcation phenomena regarding temperature distribution and interface heat flux ratio are observed. It has been found that the exothermicity or endothermicity characteristic of the channel does have a considerable impact on the temperature fields and entropy generation rates.

Design and Implementation of a Fan Coil Unit Controller Based on the Duty Ratio Fuzzy Method

A microcontroller-based fan coil unit (FCU) fuzzy controller is designed and implemented in this paper. The controller employs the concept of duty ratio on the electric valve control, which could make full use of the cooling and dehumidifying capacity of the FCU when the valve is off. The traditional control method and its limitations are analyzed. The hardware and software design processes are introduced in detail. The experimental results show that the proposed method is more energy efficient compared to the traditional controlling strategy. Furthermore, a more comfortable room condition could be achieved by the proposed method. The proposed low-cost FCU fuzzy controller deserves to be widely used in engineering applications.

The Application of Data Mining Technology in Building Energy Consumption Data Analysis

Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Nonoscillation Criteria for Nonlinear Delay Dynamic Systems on Time Scales

In this paper, we consider the nonlinear delay dynamic system xΔ(t) = p(t)f1(y(t)), yΔ(t) = −q(t)f2(x(t − τ )). We obtain some necessary and sufficient conditions for the existence of nonoscillatory solutions with special asymptotic properties of the system. We generalize the known results in the literature. One example is given to illustrate the results.

CFD simulation of Pressure Drops in Liquid Acquisition Device Channel with Sub-Cooled Oxygen

In order to better understand the performance of screen channel liquid acquisition devices (LADs) in liquid oxygen (LOX), a computational fluid dynamics (CFD) simulation of LOX passing through a LAD screen channel was conducted. In the simulation, the screen is taken as a 'porous jump' where the pressure drop across the screen depends on the incoming velocity and is formulated by Δp = Av + Bv2 . The CFD simulation reveals the importance of the pressure losses due to the flow entering from across the screen and impacting and merging with the channel flow and the vortices in the channel to the cumulative flow resistance. In fact, both the flow resistance of flows impact and mergence and the resistance created by vortices are much larger than the friction and dynamic pressure losses in the channel and are comparable to the flow resistance across the screen. Therefore, these resistances in the channel must be considered as part of the evaluation for the LAD channel performance. For proper operation of a LAD in LOX these resistances must be less than the bubble point pressure for the screen channel in LOX. The simulation also presents the pressure and velocity distributions within the LAD screen channel, expanding the understanding of the fluid flow characteristics within the channel.

A New Method for Estimation of the Source Coherency Structure of Wideband Sources

Based on the sources- smoothed rank profile (SRP) and modified minimum description length (MMDL) principle, a method for estimation of the source coherency structure (SCS) and the number of wideband sources is proposed in this paper. Instead of focusing, we first use a spatial smoothing technique to pre-process the array covariance matrix of each frequency for de-correlating the sources and then use smoothed rank profile to determine the SCS and the number of wideband sources. We demonstrate the availability of the method by numerical simulations.

CFD Simulations of Flow in Capillary Flow Liquid Acquisition Device Channel

Future space vehicles will require the use of non-toxic, cryogenic propellants, because of the performance advantages over the toxic hypergolic propellants and also because of the environmental and handling concerns. A prototypical capillary flow liquid acquisition device (LAD) for cryogenic propellants was fabricated with a mesh screen, covering a rectangular flow channel with a cylindrical outlet tube, and was tested with liquid oxygen (LOX). In order to better understand the performance in various gravity environments and orientations with different submersion depths of the LAD, a series of computational fluid dynamics (CFD) simulations of LOX flow through the LAD screen channel, including horizontally and vertically submersions of the LAD channel assembly at normal gravity environment was conducted. Gravity effects on the flow field in LAD channel are inspected and analyzed through comparing the simulations.

Percolation Transition with Hidden Variables in Complex Networks

A new class of percolation model in complex networks, in which nodes are characterized by hidden variables reflecting the properties of nodes and the occupied probability of each link is determined by the hidden variables of the end nodes, is studied in this paper. By the mean field theory, the analytical expressions for the phase of percolation transition is deduced. It is determined by the distribution of the hidden variables for the nodes and the occupied probability between pairs of them. Moreover, the analytical expressions obtained are checked by means of numerical simulations on a particular model. Besides, the general model can be applied to describe and control practical diffusion models, such as disease diffusion model, scientists cooperation networks, and so on.

Multi-stage Directional Median Filter

Median filter is widely used to remove impulse noise without blurring sharp edges. However, when noise level increased, or with thin edges, median filter may work poorly. This paper proposes a new filter, which will detect edges along four possible directions, and then replace noise corrupted pixel with estimated noise-free edge median value. Simulations show that the proposed multi-stage directional median filter can provide excellent performance of suppressing impulse noise in all situations.

A State Aggregation Approach to Singularly Perturbed Markov Reward Processes

In this paper, we propose a single sample path based algorithm with state aggregation to optimize the average rewards of singularly perturbed Markov reward processes (SPMRPs) with a large scale state spaces. It is assumed that such a reward process depend on a set of parameters. Differing from the other kinds of Markov chain, SPMRPs have their own hierarchical structure. Based on this special structure, our algorithm can alleviate the load in the optimization for performance. Moreover, our method can be applied on line because of its evolution with the sample path simulated. Compared with the original algorithm applied on these problems of general MRPs, a new gradient formula for average reward performance metric in SPMRPs is brought in, which will be proved in Appendix, and then based on these gradients, the schedule of the iteration algorithm is presented, which is based on a single sample path, and eventually a special case in which parameters only dominate the disturbance matrices will be analyzed, and a precise comparison with be displayed between our algorithm with the old ones which is aim to solve these problems in general Markov reward processes. When applied in SPMRPs, our method will approach a fast pace in these cases. Furthermore, to illustrate the practical value of SPMRPs, a simple example in multiple programming in computer systems will be listed and simulated. Corresponding to some practical model, physical meanings of SPMRPs in networks of queues will be clarified.