**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**591

# Search results for: stochastic decomposition.

##### 591 Network-Constrained AC Unit Commitment under Uncertainty Using a Bender’s Decomposition Approach

**Authors:**
B. Janani,
S. Thiruvenkadam

**Abstract:**

In this work, the system evaluates the impact of considering a stochastic approach on the day ahead basis Unit Commitment. Comparisons between stochastic and deterministic Unit Commitment solutions are provided. The Unit Commitment model consists in the minimization of the total operation costs considering unit’s technical constraints like ramping rates, minimum up and down time. Load shedding and wind power spilling is acceptable, but at inflated operational costs. The evaluation process consists in the calculation of the optimal unit commitment and in verifying the fulfillment of the considered constraints. For the calculation of the optimal unit commitment, an algorithm based on the Benders Decomposition, namely on the Dual Dynamic Programming, was developed. Two approaches were considered on the construction of stochastic solutions. Data related to wind power outputs from two different operational days are considered on the analysis. Stochastic and deterministic solutions are compared based on the actual measured wind power output at the operational day. Through a technique capability of finding representative wind power scenarios and its probabilities, the system can analyze a more detailed process about the expected final operational cost.

**Keywords:**
Benders’ decomposition,
network constrained AC unit commitment,
stochastic programming,
wind power uncertainty.

##### 590 Decomposition of Graphs into Induced Paths and Cycles

**Authors:**
I. Sahul Hamid,
Abraham V. M.

**Abstract:**

A decomposition of a graph G is a collection ψ of subgraphs H1,H2, . . . , Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path or an induced cycle in G, then ψ is called an induced path decomposition of G. The minimum cardinality of an induced path decomposition of G is called the induced path decomposition number of G and is denoted by πi(G). In this paper we initiate a study of this parameter.

**Keywords:**
Path decomposition,
Induced path decomposition,
Induced path decomposition number.

##### 589 Induced Acyclic Path Decomposition in Graphs

**Authors:**
Abraham V. M.,
I. Sahul Hamid

**Abstract:**

**Keywords:**
Cycle decomposition,
Induced acyclic path decomposition,
Induced acyclic path decomposition number.

##### 588 Solving Stochastic Eigenvalue Problem of Wick Type

**Authors:**
Hassan Manouzi,
Taous-Meriem Laleg-Kirati

**Abstract:**

In this paper we study mathematically the eigenvalue problem for stochastic elliptic partial differential equation of Wick type. Using the Wick-product and the Wiener-Itô chaos expansion, the stochastic eigenvalue problem is reformulated as a system of an eigenvalue problem for a deterministic partial differential equation and elliptic partial differential equations by using the Fredholm alternative. To reduce the computational complexity of this system, we shall use a decomposition method using the Wiener-Itô chaos expansion. Once the approximation of the solution is performed using the finite element method for example, the statistics of the numerical solution can be easily evaluated.

**Keywords:**
Eigenvalue problem,
Wick product,
SPDEs,
finite
element,
Wiener-Itô chaos expansion.

##### 587 Performance Analysis of a Discrete-time GeoX/G/1 Queue with Single Working Vacation

**Authors:**
Shan Gao,
Zaiming Liu

**Abstract:**

This paper treats a discrete-time batch arrival queue with single working vacation. The main purpose of this paper is to present a performance analysis of this system by using the supplementary variable technique. For this purpose, we first analyze the Markov chain underlying the queueing system and obtain its ergodicity condition. Next, we present the stationary distributions of the system length as well as some performance measures at random epochs by using the supplementary variable method. Thirdly, still based on the supplementary variable method we give the probability generating function (PGF) of the number of customers at the beginning of a busy period and give a stochastic decomposition formulae for the PGF of the stationary system length at the departure epochs. Additionally, we investigate the relation between our discretetime system and its continuous counterpart. Finally, some numerical examples show the influence of the parameters on some crucial performance characteristics of the system.

**Keywords:**
Discrete-time queue,
batch arrival,
working vacation,
supplementary variable technique,
stochastic decomposition.

##### 586 Urban Growth Analysis Using Multi-Temporal Satellite Images, Non-stationary Decomposition Methods and Stochastic Modeling

**Authors:**
Ali Ben Abbes,
ImedRiadh Farah,
Vincent Barra

**Abstract:**

Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images *I _{1}, I_{2}, … I_{n}* at different periods (

*t*= 1

*,*2

*, ..., n*). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method.

**Keywords:**
Multi-temporal satellite image,
urban growth,
Non-stationarity,
stochastic modeling.

##### 585 Blind Channel Estimation Based on URV Decomposition Technique for Uplink of MC-CDMA

**Authors:**
Pradya Pornnimitkul,
Suwich Kunaruttanapruk,
Bamrung Tau Sieskul,
Somchai Jitapunkul

**Abstract:**

In this paper, we investigate a blind channel estimation method for Multi-carrier CDMA systems that use a subspace decomposition technique. This technique exploits the orthogonality property between the noise subspace and the received user codes to obtain channel of each user. In the past we used Singular Value Decomposition (SVD) technique but SVD have most computational complexity so in this paper use a new algorithm called URV Decomposition, which serve as an intermediary between the QR decomposition and SVD, replaced in SVD technique to track the noise space of the received data. Because of the URV decomposition has almost the same estimation performance as the SVD, but has less computational complexity.

**Keywords:**
Channel estimation,
MC-CDMA,
SVD,
URV.

##### 584 Solving SPDEs by a Least Squares Method

**Authors:**
Hassan Manouzi

**Abstract:**

We present in this paper a useful strategy to solve stochastic partial differential equations (SPDEs) involving stochastic coefficients. Using the Wick-product of higher order and the Wiener-Itˆo chaos expansion, the SPDEs is reformulated as a large system of deterministic partial differential equations. To reduce the computational complexity of this system, we shall use a decomposition-coordination method. To obtain the chaos coefficients in the corresponding deterministic equations, we use a least square formulation. Once this approximation is performed, the statistics of the numerical solution can be easily evaluated.

**Keywords:**
Least squares,
Wick product,
SPDEs,
finite element,
Wiener chaos expansion,
gradient method.

##### 583 Calculation of Reorder Point Level under Stochastic Parameters: A Case Study in Healthcare Area

**Authors:**
Serap Akcan,
Ali Kokangul

**Abstract:**

**Keywords:**
Inventory control system,
reorder point level,
stochastic demand,
stochastic lead time

##### 582 Generalized Morphological 3D Shape Decomposition Grayscale Interframe Interpolation Method

**Authors:**
Dragos Nicolae VIZIREANU

**Abstract:**

One of the main image representations in Mathematical Morphology is the 3D Shape Decomposition Representation, useful for Image Compression and Representation,and Pattern Recognition. The 3D Morphological Shape Decomposition representation can be generalized a number of times,to extend the scope of its algebraic characteristics as much as possible. With these generalizations, the Morphological Shape Decomposition 's role to serve as an efficient image decomposition tool is extended to grayscale images.This work follows the above line, and further develops it. Anew evolutionary branch is added to the 3D Morphological Shape Decomposition's development, by the introduction of a 3D Multi Structuring Element Morphological Shape Decomposition, which permits 3D Morphological Shape Decomposition of 3D binary images (grayscale images) into "multiparameter" families of elements. At the beginning, 3D Morphological Shape Decomposition representations are based only on "1 parameter" families of elements for image decomposition.This paper addresses the gray scale inter frame interpolation by means of mathematical morphology. The new interframe interpolation method is based on generalized morphological 3D Shape Decomposition. This article will present the theoretical background of the morphological interframe interpolation, deduce the new representation and show some application examples.Computer simulations could illustrate results.

**Keywords:**
3D shape decomposition representation,
mathematical morphology,
gray scale interframe interpolation

##### 581 Non-Stationary Stochastic Optimization of an Oscillating Water Column

**Authors:**
María L. Jalón,
Feargal Brennan

**Abstract:**

**Keywords:**
Non-stationary stochastic optimization,
oscillating
water column,
temporal variability,
wave energy.

##### 580 Dynamic Slope Scaling Procedure for Stochastic Integer Programming Problem

**Authors:**
Takayuki Shiina

**Abstract:**

**Keywords:**
stochastic programming problem with recourse,
simple
integer recourse,
dynamic slope scaling procedure

##### 579 Stochastic Estimation of Cavity Flowfield

**Authors:**
Yin Yin Pey,
Leok Poh Chua,
Wei Long Siauw

**Abstract:**

**Keywords:**
Open cavity,
Particle Image Velocimetry,
Stochastic
estimation,
Turbulent kinetic energy.

##### 578 Stochastic Programming Model for Power Generation

**Authors:**
Takayuki Shiina

**Abstract:**

**Keywords:**
electric power capacity expansion problem,
integerprogramming,
L-shaped method,
stochastic programming

##### 577 N-Sun Decomposition of Complete Graphs and Complete Bipartite Graphs

**Authors:**
R. Anitha,
R. S. Lekshmi

**Abstract:**

**Keywords:**
Hamilton cycle,
n-sun decomposition,
perfectmatching,
spanning tree.

##### 576 Analysis of Catalytic Properties of Ni3Al Thin Foils for the Methanol and Hexane Decomposition

**Authors:**
M. Michalska-Domańska,
P. Jóźwik,
Z. Bojar

**Abstract:**

**Keywords:**
hexane decomposition,
methanol decomposition,
Ni3Al thin foils,
Ni nanoparticles

##### 575 Stochastic Scheduling to Minimize Expected Lateness in Multiple Identical Machines

**Authors:**
Ghulam Zakria,
Zailin Guan ,
Yasser Riaz Awan,
Wan Lizhi

**Abstract:**

**Keywords:**
Quantity Production Flow Shop,
LPT Scheduling,
Stochastic Scheduling,
Maximum Lateness,
Random Due Dates

##### 574 Optimizing Approach for Sifting Process to Solve a Common Type of Empirical Mode Decomposition Mode Mixing

**Authors:**
Saad Al-Baddai,
Karema Al-Subari,
Elmar Lang,
Bernd Ludwig

**Abstract:**

**Keywords:**
Empirical mode decomposition,
mode mixing,
sifting
process,
over-sifting.

##### 573 N-Sun Decomposition of Complete, Complete Bipartite and Some Harary Graphs

**Authors:**
R. Anitha,
R. S. Lekshmi

**Abstract:**

**Keywords:**
Decomposition,
Hamilton cycle,
n-sun graph,
perfect matching,
spanning tree.

##### 572 Comparison of Reliability Systems Based Uncertainty

**Authors:**
A. Aissani,
H. Benaoudia

**Abstract:**

**Keywords:**
Uncertainty,
Stochastic comparison,
Reliability,
serie's system,
imperfect repair.

##### 571 On Diffusion Approximation of Discrete Markov Dynamical Systems

**Authors:**
Jevgenijs Carkovs

**Abstract:**

**Keywords:**
Markov dynamical system,
diffusion approximation,
equilibrium stochastic stability.

##### 570 The Strict Stability of Impulsive Stochastic Functional Differential Equations with Markovian Switching

**Authors:**
Dezhi Liu Guiyuan Yang Wei Zhang

**Abstract:**

**Keywords:**
Impulsive; Stochastic functional differential equation; Strict stability; Razumikhin technique.

##### 569 Mean Square Stability of Impulsive Stochastic Delay Differential Equations with Markovian Switching and Poisson Jumps

**Authors:**
Dezhi Liu

**Abstract:**

In the paper, based on stochastic analysis theory and Lyapunov functional method, we discuss the mean square stability of impulsive stochastic delay differential equations with markovian switching and poisson jumps, and the sufficient conditions of mean square stability have been obtained. One example illustrates the main results. Furthermore, some well-known results are improved and generalized in the remarks.

**Keywords:**
Impulsive,
stochastic,
delay,
Markovian switching,
Poisson jumps,
mean square stability.

##### 568 Segmentation of Noisy Digital Images with Stochastic Gradient Kernel

**Authors:**
Abhishek Neogi,
Jayesh Verma,
Pinaki Pratim Acharjya

**Abstract:**

**Keywords:**
Image segmentation,
edge Detection,
noisy images,
spatialfilters,
stochastic gradient kernel.

##### 567 Noise Analysis of Single-Ended Input Differential Amplifier using Stochastic Differential Equation

**Authors:**
Tarun Kumar Rawat,
Abhirup Lahiri,
Ashish Gupta

**Abstract:**

In this paper, we analyze the effect of noise in a single- ended input differential amplifier working at high frequencies. Both extrinsic and intrinsic noise are analyzed using time domain method employing techniques from stochastic calculus. Stochastic differential equations are used to obtain autocorrelation functions of the output noise voltage and other solution statistics like mean and variance. The analysis leads to important design implications and suggests changes in the device parameters for improved noise characteristics of the differential amplifier.

**Keywords:**
Single-ended input differential amplifier,
Noise,
stochastic differential equation,
mean and variance.

##### 566 A Scenario-Based Approach for the Air Traffic Flow Management Problem with Stochastic Capacities

**Authors:**
Soumia Ichoua

**Abstract:**

In this paper, we investigate the strategic stochastic air traffic flow management problem which seeks to balance airspace capacity and demand under weather disruptions. The goal is to reduce the need for myopic tactical decisions that do not account for probabilistic knowledge about the NAS near-future states. We present and discuss a scenario-based modeling approach based on a time-space stochastic process to depict weather disruption occurrences in the NAS. A solution framework is also proposed along with a distributed implementation aimed at overcoming scalability problems. Issues related to this implementation are also discussed.

**Keywords:**
Air traffic management,
sample average approximation,
scenario-based approach,
stochastic capacity.

##### 565 PTH Moment Exponential Stability of Stochastic Recurrent Neural Networks with Distributed Delays

**Authors:**
Zixin Liu,
Jianjun Jiao Wanping Bai

**Abstract:**

In this paper, the issue of pth moment exponential stability of stochastic recurrent neural network with distributed time delays is investigated. By using the method of variation parameters, inequality techniques, and stochastic analysis, some sufficient conditions ensuring pth moment exponential stability are obtained. The method used in this paper does not resort to any Lyapunov function, and the results derived in this paper generalize some earlier criteria reported in the literature. One numerical example is given to illustrate the main results.

**Keywords:**
Stochastic recurrent neural networks,
pth moment exponential stability,
distributed time delays.

##### 564 New Subband Adaptive IIR Filter Based On Polyphase Decomposition

**Authors:**
Young-Seok Choi

**Abstract:**

We present a subband adaptive infinite-impulse response (IIR) filtering method, which is based on a polyphase decomposition of IIR filter. Motivated by the fact that the polyphase structure has benefits in terms of convergence rate and stability, we introduce the polyphase decomposition to subband IIR filtering, i.e., in each subband high order IIR filter is decomposed into polyphase IIR filters with lower order. Computer simulations demonstrate that the proposed method has improved convergence rate over conventional IIR filters.

**Keywords:**
Subband adaptive filter,
IIR filtering. Polyphase decomposition.

##### 563 A Computational Stochastic Modeling Formalism for Biological Networks

**Authors:**
Werner Sandmann,
Verena Wolf

**Abstract:**

Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the so-called chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storage-efficient model representations that are directly suitable for computer implementation has received significantly less attention. Instead, a model is usually described in terms of a stochastic process or a "higher-level paradigm" with graphical representation such as e.g. a stochastic Petri net. A serious problem then arises due to the exponential growth of the model-s state space which is in fact a main reason for the popularity of stochastic simulation since simulation suffers less from the state space explosion than non-simulative numerical solution techniques. In this paper we present transition class models for the representation of biological network models, a compact mathematical formalism that circumvents state space explosion. Transition class models can also serve as an interface between different higher level modeling paradigms, stochastic processes and the implementation coded in a programming language. Besides, the compact model representation provides the opportunity to apply non-simulative solution techniques thereby preserving the possible use of stochastic simulation. Illustrative examples of transition class representations are given for an enzyme-catalyzed substrate conversion and a part of the bacteriophage λ lysis/lysogeny pathway.

**Keywords:**
Computational Modeling,
Biological Networks,
Stochastic Models,
Markov Chains,
Transition Class Models.

##### 562 Passivity Analysis of Stochastic Neural Networks With Multiple Time Delays

**Authors:**
Biao Qin,
Jin Huang,
Jiaojiao Ren,
Wei Kang

**Abstract:**

This paper deals with the problem of passivity analysis for stochastic neural networks with leakage, discrete and distributed delays. By using delay partitioning technique, free weighting matrix method and stochastic analysis technique, several sufficient conditions for the passivity of the addressed neural networks are established in terms of linear matrix inequalities (LMIs), in which both the time-delay and its time derivative can be fully considered. A numerical example is given to show the usefulness and effectiveness of the obtained results.

**Keywords:**
Passivity,
Stochastic neural networks,
Multiple time
delays,
Linear matrix inequalities (LMIs).