Problem Formulation. Figure 6: Problem setup: estimating the location of source A by analysing the signals recorded by the "Main" and "
Problem Formulation. In a typical classification problem, a training set of labelled examples is given. The training set can be described in a variety of languages, most frequently, as a collection of patterns denoted as S = (< x1 , y1 >,..., < xm , ym >) where xq ∈X is a vector of feature values charactering the pattern and y ∈{c1,..., ck } indicates the pattern’s class. Usually, it is assumed that the training set records are generated randomly and independently according to some fixed and unknown joint probability distribution D. Let Ω ={ M1,..., Mn } represent an ensemble of n classifiers. Mi is a classifier that can predict the class Mi ( xq ) of an observation xq. The problem of ensemble pruning is to find the best subset such that the combination of the selected classifiers will have the highest possible degree of accuracy. Consequently the problem can be formally phrased as follows: Given an ensemble Ω ={ M1,..., Mn} , a combination method C, and a training set S from a distribution D over the labeled instance space, the goal is to find an optimal subset Zopt ⊆ Ω . which minimizes the generalization error over the distribution D of the classification of classifiers in Zopt combined using method C. Note that we assume that the ensemble is given, thus we do not attempt to improve the creation of the original ensemble. It has been shown that the pruning effect is more noticeable on ensemble whose the diversity among its members is high (Margineantu and Dietterich, 1997). Boosting algorithms create diverse classifiers by using widely different parts of the training set at each iteration (Xxxxx et al., 2006). Specifically we employ the most popular methods for creating the ensemble: Bagging and AdaBoost. Bagging (Xxxxxxx, 1996) employs bootstrap sampling to generate several training sets and then trains a classifier from each generated training set. Note that, since sampling with replacement is used, some of the original instances may appear more than once in the same generated training set and some may not be included at all. The classifier predictions are often combined via majority voting. AdaBoost (Xxxxxx and Schapire, 1996) sequentially constructs a series of classifiers, where the training instances that are wrongly classified by a certain classifier will get a higher weight in the training of its subsequent classifier. The classifiers’ predictions are combined via weighted voting where the weights are determined by the algorithm itself based on the training error of ...
Problem Formulation. Our problem formulation is: “Improving LBO’s supply chain efficiency by looking at EPCI projects” Since this is a wide problem formulation we decided to use an exploratory research method. We wanted to get opinions from different point of views to get an objective insight. There are several departments and groups involved in the whole logistic chain we looked into. Therefore we were prepared to use a lot of our time on gathering information throughout the whole supply chain. Interviews were our main information source and also some documentation like procedures, reports from finished projects and financial reports from the company were used in our research.
Problem Formulation. Today’s market situation and the continuous need on improving distribution processes, cutting unnecessary costs while adapting to customers’ demand is a challenge for every company. For XxxXxxxx, the main challenge seems to be the transportation link along the value chain. Therefore, finding a system which can improve the rate of utilization for the main vessels became an important issue. Then, the main objective of this master thesis is to describe, analyze and develop a full scale model for the seaborne shipping problem at NorStone. Our approach is to describe the current situation and to develop a mathematical model that can help to minimize the shipping costs between production sites and customers in Rogaland and Hordaland. Basic vehicle routing problem (VRP)-models are used as a foundation to our mathematical model, and furthermore, we aim to capture the major complexity of this problem by adding special extensions to the model. In addition we have made some combinations of different models that make our model unique from former models within vehicle routing problems (VRPs). VRP is introduced in section 3.2. In the following we introduce to the reader the theories we found as meaningful for our attempt to answer to this problem formulation.
Problem Formulation. Let Fq be a finite field with size q. We assume that a key pre-distribution scheme [1] exists, such that each A V1 V2 Vi Vn B A P1 = [h2, h3, m1] P2 = [h1, h3, m2] P3 = [h1, h2, m3]
Problem Formulation. Second strategy proposes a different approach for energy trading optimization by jointly minimizing the total power consumption in the fronthaul through adjusting the degree of partial cooperation among RRHs, the RRHs’ total transmit power and the overall real-time energy purchase from the grid, under the constraints of satisfying the QoS/energy transmission requirements of the ITs/ETs, respectively. The proposed strategy 2 can be formulated as min ni n w ,vne,B[real] αP[coop] + β ∑ n∈Lb P[Tx] + ζ ∑ n∈Lb nB[real], n n
Problem Formulation. The problem is to construct a feasible manufacturing/remanufacturing schedule such that no deadlock occurs (if such a schedule exists), all the demands are satisfied with no overproduction, and the maximum lateness Lmax is minimized. This problem is to be denoted as P (W, R, B, Lmax), which is an abbreviation for Problem (Work, Rework, Buffer, Latenessmax). Deadlock handling methods in computer and manufacturing systems were discussed and a deadlock avoidance scheme was presented by Xxxxxxxxxxx and Van Brussel [19], who used in their developments data from an existing car paint shop in Sindelfingen (Germany). Notice that an ideal implementation of a feasible solution to the problem P (W, R, B, Lmax) will not need a deadlock handling mechanism. Due to the fact that items of the same product are identical, it can easily be seen that a search for an optimal solution can be limited to schedules in which defective items of the same product leave the buffer in the same order as they enter it, following the well known First-In-First-Out (FIFO) strategy. A managerial implication of this observation is that the buffer can be designed as a collection of unidirectional lines each of which is dedicated to a specific product.
Problem Formulation. We are interested in designing and analyzing an insurance contract be- tween a renewable producer and a storage owner. We first introduce the utility functions of each participant and then define the problem to be solved.
Problem Formulation. Given the system model (1)-(2) and attacker model (3)-(4), design a key-agreement scheme between a plant’s actuator (hereafter referred to as the smart actuator or just actuator) and the controller such that: - The key-agreement is achieved by leveraging the asymmetry (4) in the system model knowledge (i.e., no cryptography schemes are explicitly used); K / K ≈ K ∈ { } K ∈ { } K ∈ { } - Let c 0, 1 n, sa 0, 1 n and e 0, 1 n be the binary keys of length n > 0 identified by the controller, smart actuator and Eve, respectively. Then, P (Kc = Ksa) ≈ 1 and P ( c = e) 1. In this correspondence, a solution to the above problem is given under the assumption that an unknown input observer for (1) can be defined to simultaneously estimate the state xk (namely xˆk) and the input signal uk (namely uˆk) from the sensor measurement yk. In what follows, the UIO algorithm is abstractly described by means of the following recursive UIO function: [uˆk−1, xˆk] = UIO(uˆk−2, xˆk−1, yk, ł) (5) where the pairs (uˆk−1, xˆk) and (uˆk−2, xˆk−1) define the available estimation at the time k and k — 1, respectively.
Problem Formulation. How does the lack of audiovisual resources affecting on teaching-learning process of English language at Dr. Xxxx Xxxxx Xxxxxxx Xxxxxx High School´s Eight Grade students of Basic General Education?
