Performance Analysis of Multi-Hop Wireless Link under Maximum Flow Algorithm

To enhance link capacity of a wireless link one or more repeater is used between the sender and the receiver. Recent literature deals with multiple parallel links to enhance throughput instead of conventional single path. In case of a multidirectional and multi-hop wireless network, the selection of link of maximum signal to noise ratio (SNR) does not guarantee the maximum throughput. In this paper, we use augmenting path of Ford-Fulkerson algorithm in detection of maximum flow from sender to receiver. To reduce the process time at the sending node, minimum-cut theorem is used to determine maximum flow like power flow of previous work. Using the maximum flow algorithm, we obtain the capacity of multi-hop wireless link higher than the conventional theorem. The concept of the paper is applicable in MANET (Mobile Ad-hoc Network), WSN (Wireless Sensor Network) and CRN (Cognitive Radio Network).

Modification of the RUSLE slope length factor based on a multiple flow algorithm considering vertical leakage at karst landscapes

Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area.The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length.The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length,and to evaluate its predictive capability at different resolution DEMs.Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero.Using the multiple flow direction algorithm united with the transfer grid(MFDTG),the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths.The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China.The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5%showed that the relationship between surface-runoff intensity and slope length was a negative power function.Estimated by the proposed modified L-equation((al_(x)^((b+1))/22.13)^(m)),the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1,5,25 and 90 m resolutions respectively.This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes.Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.

Global existence of real roots and random Newton flow algorithm for nonlinear system of equations To memorize Qin's method for 770 anniversaries

To solve nonlinear system of equation,F(x) = 0,a continuous Newton flow x_t(t) = V(x) =-(DF(x))^(-1)F(x),x(0) =x^0 and its mathematical properties,such as the central field,global existence and uniqueness of real roots and the structure of the singular surface,are studied.We concisely introduce random Newton flow algorithm(NFA) for finding all roots,based on discrete Newton flow x^(j+1)=x^j+hV{x^j) with random initial value x^0 and h∈(0,1],and three computable quantities,g_j,d_j and K_j.The numerical experiments with dimension n=300 are provided.

Extended Approach to Water Flow Algorithm for Text Line Segmentation

This paper proposes a new approach to the water flow algorithm for text line segmentation. In the basic method the hypothetical water flows under few specified angles which have been defined by water flow angle as parameter. It is applied to the document image frame from left to right and vice versa. As a result, the unwetted and wetted areas are established. These areas separate text from non-text elements in each text line, respectively. Hence, they represent the control areas that are of major importance for text line segmentation. Primarily, an extended approach means extraction of the connected-components by bounding boxes over text. By this way, each connected component is mutually separated. Hence, the water flow angle, which defines the unwetted areas, is determined adaptively. By choosing appropriate water flow angle, the unwetted areas are lengthening which leads to the better text line segmentation. Results of this approach are encouraging due to the text line segmentation improvement which is the most challenging step in document image processing.

Totally Coded Method for Signal Flow Graph Algorithm

After a code-table has been established by means of node association information from signal flow graph, the totally coded method (TCM) is applied merely in the domain of code operation beyond any figure-earching algorithm. The code-series (CS) have the holo-information nature, so that both the content and the sign of each gain-term can be determined via the coded method. The principle of this method is simple and it is suited for computer programming. The capability of the computer-aided analysis for switched current network (SIN) can be enhanced.

An Algorithm for Traffic Equilibrium Flow with Capacity Constraints of Arcs

In the traffic equilibrium problem, we introduce capacity constraints of arcs, extend Beckmann’s formula to include these constraints, and give an algorithm for traffic equilibrium flows with capacity constraints on arcs. Using an example, we illustrate the application of the algorithm and show that Beckmann’s formula is a sufficient condition only, not a necessary condition, for traffic equilibrium with capacity constraints of arcs.

Improved multi-objective artificial bee colony algorithm for optimal power flow problem

The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness.

Volume Flow Rate Optimization of an Axial Fan by Artificial Neural Network and Genetic Algorithm

The present study is to improve the volume flow rate of an axial fan through optimizing the blade shape under the demand for a specified static pressure. Fourteen design variables were selected to control the blade camber lines and the stacking line and the values of these variables were determined by using the experimental design method of the Latin Hypercube Sampling (LHS) to generate forty designs. The optimization was carried out using the genetic algorithm (GA) coupled with the artificial neural network (ANN) to increase the volume flow rate of the axial fan under the constraint of a specific motor power and a required static pressure. Differences in the aerodynamic performance and the flow characteristics between the original model and the optimal model were analyzed in detail. The results showed that the volume flow rate of the optimal model increased by 33%. The chord length, the installation angle and the cascade turning angle changed considerably. The forward leaned blade was beneficial to improve the volume flow rate of the axial fan. The axial velocity distribution and the static pressure distribution on the blade surface were improved after optimization.

Research on the Active DDoS Filtering Algorithm Based on IP Flow

Distributed Denial-of-Service (DDoS) attacks against public web servers are increasingly common. Countering DDoS attacks are becoming ever more challenging with the vast resources and techniques increasingly available to attackers. It is impossible for the victim servers to work on the individual level of on-going traffic flows. In this paper, we establish IP Flow which is used to select proper features for DDoS detection. The IP flow statistics is used to allocate the weights for traffic routing by routers. Our system protects servers from DDoS attacks without strong client authentication or allowing an attacker with partial connectivity information to repeatedly disrupt communications. The new algorithm is thus proposed to get efficiently maximum throughput by the traffic filtering, and its feasibility and validity have been verified in a real network circumstance. The experiment shows that it is with high average detection and with low false alarm and miss alarm. Moreover, it can optimize the network traffic simultaneously with defending against DDoS attacks, thus eliminating efficiently the global burst of traffic arising from normal traffic.

AN IMPLICIT ALGORITHM OF THIN LAYER EQUATIONS IN VISCOUS，TRANSONIC，TWO－PHASE NOZZLE FLOW

Omitting viscosity along flow direction, we have simplified the dimensionless N-Sequations in arbitrary curved coordinate system as the thin layer equations. Using theimplicit approximate-factorization algorithm to solve the gas-phase governing equ-ations and the characteristic method to follow the tracks of particles, we then obtainedthe full coupled numerical method of two-phase.transonic, turbulent flow. Here, par- ticle size may be grouped, the subsonic boundary condition at entry of nozzle is ireatedby quasi-characteristic method in reference plane and the algebraic model is used forturbulent flow. These methods are applied in viscous two-phase flow. calculation of ro-cket nozzle and in the prediciton of thrust and specific impulse for solid propellant ro-cket motor. The calculation results are in good agreement with the measurerment va-lues. Moreover, the influences of different particle radius, different particle mass frac-tion and particle size grouped on flow field have been discussed, and the influences of particle two-dimensional radial velosity component and viscosity on specific impulse ofrocket motor have been analysed.The method of this paper possesses the advantage of saving computer time. More important, the effect is more obvious for the calculation of particle size being grouped.

Convergence and stability of the Newton-Like algorithm with estimation error in optimization flow control

The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. Based on the optimization theory, a sufficient condition for convergence of this algorithm with bounded price estimation error is obtained. Furthermore, even when this sufficient condition doesn＇t hold, this algorithm can also converge, provided a modified step size, and an attraction region is obtained. Based on Lasalle＇s invariance principle applied to a suitable Lyapunov function, the dynamic system described by this algorithm is proved to be global stability if the error is zero. And the Newton-Like algorithm with bounded price estimation error is also globally stable if the error satisfies the sufficient condition for convergence. All trajectories ultimately converge to the equilibrium point.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

A New Explicit Algorithm for Bi-Phasic Mixture Flow in MIM

Metal injection moulding (MIM) is a new technology to manufacture small intricate parts in large quantity. Numerical simulation plays an important role in its development. To predict the specific segregation effect in MIM injection, mixture theory is adopted to model the injection flow by a bi-phasic model. This model conducts to the solution of two-coupled Stokes equations. It is an extremely computational consuming solution in the scope of the traditional algorithms, which induce a serious challenge to cost-effectivity of the MIM simulation. Referred to some methods proposed by Lewis in mono-phasic simulation and the implicit algorithms in MIM simulation, a new explicit algorithm is proposed and realized to perform efficiently this type of bi-phasic flow. Numerically this algorithm is devised to perform the simulation in a fully uncoupled manner except for a global solution of the pressure field in each time step. The physical coupling is taken into account in a sequential pattern by fractional steps.

Effective Iterated Greedy Algorithm for Flow-Shop Scheduling Problems with Time lags

Flow shop scheduling problem with time lags is a practical scheduling problem and attracts many studies. Permutation problem（PFSP with time lags） is concentrated but non-permutation problem（non-PFSP with time lags） seems to be neglected. With the aim to minimize the makespan and satisfy time lag constraints, efficient algo- rithms corresponding to PFSP and non-PFSP problems are proposed, which consist of iterated greedy algorithm for permutation（IGTLP） and iterated greedy algorithm for non-permutation （IGTLNP）. The proposed algorithms are verified using well-known simple and complex instances of permutation and non-permutation problems with various time lag ranges. The permutation results indicate that the proposed IGTLP can reach near optimal solution within nearly 11% computational time of traditional GA approach. The non-permutation results indicate that the proposed IG can reach nearly same solution within less than 1% com- putational time compared with traditional GA approach. The proposed research combines PFSP and non-PFSP together with minimal and maximal time lag consideration, which provides an interesting viewpoint for industrial implementation.

AN EFFICIENT ALGORITHM FOR HYPERSONIC VISCOUS FLOWS

The CSCM-S algorithm proposed by Lombard et al.is a very attractive tool for solving multidimensional Euler and Navier-Stokes equations.However,it is not economical due to the use of global sweeps in the whole computational domain.In this paper we suggest a modified strategy,which combines a single-marching technique for supersonic dominated region with a multi-sweep procedure for pure subsonic and complex flowfield.The new algorithm may save significantly CPU time and is more suitable for engineering applications.

A New Parallel Algorithm in Power Flow Calculation:Dynamic Asynchronous Parallel Algorithm

Based on the general methods in power flow calculation of power system and on conceptions and classifications of parallel algorithm, a new approach named Dynamic Asynchronous Parallel Algorithm that applies to the online analysis and real-time dispatching and controlling of large-scale power network was put forward in this paper. Its performances of high speed and dynamic following have been verified on IEEE-14 bus system.

Hybrid partheno-genetic algorithm and its application in flow-shop problem

In order to solve the constraint satisfied problem in the genetic algorithm, the partheno-genetic algorithm is designed. And then the schema theorem of the partheno-genetic algorithm is proposed to show that the high rank schemas at the subsequent generation decrease exponentially even though its fitness is more optimal than the average one in the population and the low rank schemas at the subsequent generation increase exponentially when its fitness is more optimal than the average one in the population. In order to overcome the shortcoming that the optimal high rank schema can be deserted arbitrarily, the HGA (hybrid partheno-genetic algorithm) is proposed, that is, the hill-climbing algorithm is integrated to search for a better individual. Finally, the results of the simulation for facility layout problem and no-wait schedule problem are given. It is shown that the hybrid partheno- genetic algorithm is of high efficiency.

Application of a Genetic Algorithm Based on the Immunity for Flow Shop under Uncertainty

The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se- quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov- ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an- tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.

Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

Many researches discussing reduced energy consumption for environmental protection focus on machine efficiency or process redesign. To optimize the machine operation time can also save the energy, and these researches have received great interests in recent years. This study considers three different states of machines, among processing there are two different speeds, to solve the problem of minimizing energy costs under time-of-use tariff with no tardy jobs in flexible flow shop. This problem is basically NP-hard, we proposed a hybrid genetic algorithm (GA) to solve problems in reasonable timeliness. The result shows that to optimize different states of machines under time-of use tariff can reduce energy costs significantly in on-time delivery.

AN EFFICIENT FINITE-DIFFERENCE ALGORITHM FOR COMPUTING AXISYMMETRIC TRANSONIC NACELLE FLOW FIELDS

A finite difference method for computing the axisymmetric, transonic flows over a nacelle is presented in this paper. By use of the conservative full-potential equation, body-fitted grid, and the exact boundary conditions, a new AF scheme is constructed according to the criterion of optimum convergence. The proposed scheme has been applied to transonic nacelle flow problems. Computation for several nacelles shows the rapid convergence of this scheme and excellent agreement with the experimental results.