Bangla User Adaptive Word Speech Recognition

Wang

2014

Sustainability

Cycle time management plays an important role in improving the performance of a wafer fabrication factory. It starts from the estimation of the cycle time of each job in the wafer fabrication factory. Although this topic has been widely investigated, several issues still need to be addressed, such as how to classify jobs suitable for the same estimation mechanism into the same group. In contrast, in most existing methods, jobs are classified according to their attributes. However, the differences between the attributes of two jobs may not be reflected on their cycle times. The bi-objective nature of classification and regression tree (CART) makes it especially suitable for tackling this problem. However, in CART, the cycle times of jobs of a branch are estimated with the same value, which is far from accurate. For these reason, this study proposes a joint use of principal component analysis (PCA), CART, and back propagation network (BPN), in which PCA is applied to construct a series of linear combinations of original variables to form new variables that are as unrelated to each other as possible. According to the new variables, jobs are classified using CART before estimating their cycle times with BPNs. A real case was used to evaluate the effectiveness of the proposed methodology. The experimental results supported the superiority of the proposed methodology over some existing methods. In addition, the managerial implications of the proposed methodology are also discussed with an example.

“…CART, introduced by Breiman et al [21], is a statistical procedure primarily used for object classification. The objective is to classify a group of objects into two or more populations.…”

Section: Job Classification Using Cartmentioning

confidence: 99%

“…The first stage is to grow the tree using a recursive partitioning technique that selects variables and split points according to a pre-specified criterion. Criteria to this end include Gini, towing, ordered towing, and maximum deviance reduction [21].…”

Section: Job Classification Using Cartmentioning

confidence: 99%

Enhancing the Effectiveness of Cycle Time Estimation in Wafer Fabrication-Efficient Methodology and Managerial Implications

Wang

2014

Sustainability

“…For a recent comparison of these algorithms, refer to [17]. Among these algorithms, the LM algorithm has a faster convergence speed, and therefore is applied in the proposed methodology, as described below.…”

Section: Estimating the Cycle Times Of Jobs Using A Bpnmentioning

confidence: 99%

“…There are K inputs to the BPN including the attributes of a job and the factory conditions when the job was released into the factory. A lot of past studies have shown that a BPN with a single hidden layer can achieve a satisfactory approximation performance [12][13][14][15][16][17]. In addition, several ways have been proposed in the literature to determine the number of neurons in the hidden layer, e.g., [18,19].…”

Section: Estimating the Cycle Times Of Jobs Using A Bpnmentioning

confidence: 99%

“…In addition, several ways have been proposed in the literature to determine the number of neurons in the hidden layer, e.g., [18,19]. A common practice is to set it to the double of the number of inputs [9,17], i.e., 2K. The activation/transformation functions for the input and hidden layers are the linear activation function and the hyperbolic tangent sigmoid function, respectively:…”

Section: Estimating the Cycle Times Of Jobs Using A Bpnmentioning

confidence: 99%

See 1 more Smart Citation

The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication

2014

Symmetry

An innovative classification and back-propagation-network tree (CABPN tree) approach is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory, which is one of the most important tasks in controlling the wafer fabrication factory. The CABPN tree approach is an extension from the traditional classification and regression tree (CART) approach. In CART, the cycle times of jobs of the same branch are estimated with the same value, which is far from accurate. To tackle this problem, the CABPN tree approach replaces the constant estimate with variant estimates. To this end, the cycle times of jobs of the same branch are estimated with a BPN, and may be different. In this way, the estimation accuracy can be improved. In addition, to determine the optimal location of the splitting point on a node, the symmetric partition with incremental re-learning (SP-IR) algorithm is proposed and illustrated with an example. The applicability of the CABPN tree approach is shown with a real case. The experimental results supported its effectiveness over several existing methods.

An Efficient and Effective Fuzzy Collaborative Intelligence Approach for Cycle Time Estimation in Wafer Fabrication

2015

Int. J. Intell. Syst.

A common characteristic of fuzzy collaborative forecasting methods is the establishment of an interval estimate that is guaranteed to include the actual value. Such a characteristic is crucial to various planning purposes because it reduces the risk of incorrect forecasting. A new fuzzy collaborative forecasting method is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory; this is a critical task for managing the fabrication of wafers. The proposed fuzzy collaborative forecasting method uses a fuzzy back propagation network that effectively improves the precision of estimation. The accuracy is enhanced through expert collaboration. The performance of the proposed methodology was selectively compared with those of two existing fuzzy collaborative forecasting methods by applying them to a real case. The results confirmed that the proposed methodology substantially enhanced the performance in estimating the cycle time of a job.