Maximum Number Of Groups Research Articles

Grouping Method of Semiconductor Bonding Equipment Based on Clustering by Fast Search and Find of Density Peaks for Dynamic Matching According to Processing Tasks

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method sets the device group closed position constraint and the matching constraint between the device type and the processing type and uses the graph theory method to establish the device grouping model. The dynamic grouping of equipment under the capacity demand of different processing tasks was achieved by changing the relationship matrix between devices. The drawback of this grouping method is rather large grouping deviation, which we tried to solve with the clustering by fast search and find of density peaks (CFSFDP) that was added to cluster the sets of attribute information of the devices so as to obtain the maximum number of grouping groups obtained to reduce the grouping deviation. Simulation comparison experiments were carried out under different circumstances considering the size of the formation, the distribution of demand capacity, and the coefficient of difference in demand capacity. Compared with the standard device grouping method, the grouping method based on semiconductor bonding equipment and CFSFDP algorithm for dynamic matching according to processing tasks had better performance in solving the dynamic grouping problem.

Optimization of reinforced concrete building frames with automated grouping of columns

Abstract In structural design, it is common practice to adopt the same cross-section dimensions for a group of elements. This procedure is mainly for practical and aesthetic reasons, as well as to reduce labour costs, but it also has a positive effect of reducing the number of variables, which simplifies the usual trial and error design process. On the other hand, the total materials cost obtained is closely related to this grouping. Based on this, the present work aims to minimize the cost of reinforced concrete plane frames considering the automated grouping of columns. To achieve this objective, an optimization software was developed by the association of matrix structural analysis, dimensioning and optimization. The sections dimensions, the area of steel and the concrete strength of beams and columns were taken as design variables. For a given maximum number of groups, the optimum grouping and the corresponding values to design variables are obtained. The strategy proposed in this paper to obtain the grouping reduces significantly the number of infeasible candidate solutions during the search process and avoid the proposition of unrealistic designs. For the optimization, a variant of the Harmony Search method was adopted. Some structures were analyzed in order to validate the application of the proposed formulation, as well as to verify the influence of the grouping of elements on the final results. In these structures, it was possible to observe a significant additional reduction in the total cost when automated grouping is performed regarding a uniform grouping, even when a small number of groups is considered. For the 20-floor building frame analyzed, the cost reduction from uniform to automated grouping varied from 5.53 to 7.35%. The influence of the concrete strength on optimal results was also investigated, indicating a cost reduction of 9.74% from best (40 MPa) to worst case (20 MPa). In general, it can be concluded that, when applied in conjunction with the usual design variables, the proposed procedure can enable a significant additional economy, without affecting the structural safety.

Multiple phases in modularity-based community detection.

Detecting communities in a network, based only on the adjacency matrix, is a problem of interest to several scientific disciplines. Recently, Zhang and Moore have introduced an algorithm [Proc. Natl. Acad. Sci. USA 111, 18144 (2014)], called mod-bp, that avoids overfitting the data by optimizing a weighted average of modularity (a popular goodness-of-fit measure in community detection) and entropy (i.e., number of configurations with a given modularity). The adjustment of the relative weight, the "temperature" of the model, is crucial for getting a correct result from mod-bp. In this work we study the many phase transitions that mod-bp may undergo by changing the two parameters of the algorithm: the temperature T and the maximum number of groups q. We introduce a new set of order parameters that allow us to determine the actual number of groups q̂, and we observe on both synthetic and real networks the existence of phases with any q̂∈{1,q}, which were unknown before. We discuss how to interpret the results of mod-bp and how to make the optimal choice for the problem of detecting significant communities.

Bounds on Fast Decodability of Space-Time Block Codes, Skew-Hermitian Matrices, and Azumaya Algebras

We study fast lattice decodability of space-time block codes for n transmit and receive antennas, written very generally as a linear combination Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2l</sup> s <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , where the si are real information symbols and the A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> are n×n ℝ-linearly independent complex-valued matrices. We show that the mutual orthogonality condition A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> * + A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> * = 0 for distinct basis matrices is not only sufficient but also necessary for fast decodability. We build on this to show that for full-rate (l = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) transmission, the decoding complexity can be no better than |S|(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> +1), where |S| is the size of the effective real signal constellation. We also show that for full-rate transmission, g-group decodability, as defined by Jithamithra and Rajan, is impossible for any g ≥ 2. We then use the theory of Azumaya algebras to derive bounds on the maximum number of groups into which the basis matrices can be partitioned so that the matrices in different groups are mutually orthogonal-a key measure of fast decodability. We show that in general, this maximum number is of the order of only the 2-adic value of n. In the case where the matrices A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> arise from a division algebra, which is most desirable for diversity, we show that the maximum number of groups is only 4. As a result, the decoding complexity for this case is no better than |S|⌈l/2⌉ for any rate l.

Water Quality Assessment Using Artificial Intelligence Techniques: SOM and ANN—A Case Study of Melen River Turkey

Artificial intelligence methods have been employed with regard to 26 sets of physical and chemical pollution data obtained from the Melen River by the Turkish State Hydraulic Works during the period of 1995–2006. Water-quality data are divided into two parts relating to the high- and low-flow periods for the 1 KMP, 2 BMP, and 3 BMA stations. The self organizing map–artificial neural networks (SOM–ANNs) is employed to evaluate the high–low flow period correlations in terms of water-quality parameters. This is done in order to extract the most important parameters in assessing high–low flow period variations in terms of river water quality. The map size chosen is 9 × 9 in order to ensure that the maximum number of groups would be obtained from the training data. The groups explaining the pollution sources are identified as being responsible for the data structure at each dataset. The SOM, supported by ANN, is applied to provide a nonlinear relationship between input variables and output variables in order to determine the most significant parameters in each group. The multilayer feed-forward NN is chosen for this study. The most crucial parameters are determined, and the groups are conditionally named as mineral structure; soil structure and erosion; domestic, municipal, and industrial effluents; agricultural activity waste-disposal sites; and seasonal effects factors. Based on the explanation of the parameters, we can have an opinion about other parameters which can lead to cost and time savings. The aim of this study is to illustrate the usefulness of artificial intelligence for the evaluation of complex data in river- and water-quality assessment identification, and pollution sources, for effective water-quality management.

Unsupervised learning for classification of acoustic emission events from tensile and bending experiments with open-hole carbon fiber composite samples

Abstract Widespread use of composites for structural applications is hindered by the inability to fully understand and predict the materials response. The uncertainty in composite materials response is largely due to variability in the initiation and propagation of damage. To develop new tools for design with composite materials, techniques for identifying damage modes during operation are needed. While there is a large body of work on analysis of acoustic emission (AE) from different materials and different loading cases, the current research is focused on applying unsupervised learning for separating AE into a maximum number of groups with distinct evolution. AE data was collected during tensile and bending experiments on carbon fiber reinforced epoxy specimens with different material configurations. The unsupervised learning algorithms successfully identified AE groups with distinct initiation and evolution. With damage mechanisms inferred from load response and from deformation fields obtained with digital image correlation (DIC), strong correlations between the behaviors of the groups and the damage mechanisms were observed and are discussed.

Adaptive Frequency-Domain Group-Based Shrinkage Estimators for UWB Systems

In this work, we propose low-complexity adaptive biased estimation algorithms, called group-based shrinkage estimators (GSEs), for parameter estimation and interference suppression scenarios with mechanisms to automatically adjust the shrinkage factors. The proposed estimation algorithms divide the target parameter vector into a number of groups and adaptively calculate one shrinkage factor for each group. GSE schemes improve the performance of the conventional least squares (LS) estimator in terms of the mean-squared error (MSE), while requiring a very modest increase in complexity. An MSE analysis is presented which indicates the lower bounds of the GSE schemes with different group sizes. We prove that our proposed schemes outperform the biased estimation with only one shrinkage factor and the best performance of GSE can be obtained with the maximum number of groups. Then, we consider an application of the proposed algorithms to single-carrier frequency-domain equalization (SC-FDE) of direct-sequence ultra-wideband (DS-UWB) systems, in which the structured channel estimation (SCE) algorithm and the frequency domain receiver employ the GSE. The simulation results show that the proposed algorithms significantly outperform the conventional unbiased estimator in the analyzed scenarios.

Characteristics of Neural Test Theory: Comparison With Classical Test Theory and Rasch Modeling

The neural test theory (NTT) 'uses the mechanism of a self-organizing map' or generative topographic mapping and 'assumes the latent scale is ordinal' (Shojima, 2008c). The current study aims to reveal the characteristics of the NTT by applying it to the analysis of a placement test at a university. We compared the NTT results with those obtained using the Classical Test Theory (CTT) and Rasch modeling (RM). The participants comprised 147 Japanese learners of English, whose major subject was international studies or management studies. They took a 90-item multiple-choice vocabulary test. We obtained the test scores using the CTT (percentages correct), RM (latent ability estimates), and NTT (latent rank estimates), and classified the students into three or five groups with different proficiency levels based on the scores derived from the CTT and RM. After a detailed analysis, we ascertained three findings. First, our analysis revealed that the three types of scores and the two types of groups were highly correlated. This suggests that similar results can be obtained by using any one of the three test theories. Second, the maximum number of groups into which we could divide the students was the same (i.e., three) according to the separation index in the RM and the test model-fit indices in the NTT. Third, we compared the item difficulty and discrimination obtained from these three theories and showed that the results of the item difficulty using the CTT, RM, and NTT were highly correlated; similar results were observed for item discrimination computed using the CTT and NTT. Overall, the NTT results (i.e., the test-takers' latent ranks, the maximum number of groups, item difficulty, and item discrimination) are similar to those obtained using the CTT and RM. Furthermore, the NTT is advantageous in computing ordinal ranks based on test-takers' test response patterns with a relatively small sample size and in presenting more information on item monotonicity. Thus, the present study provides evidence for the effectiveness of the NTT in analyzing in language testing data, especially when only ordinal scale results are required.

A bayesian group sequential analysis procedure

In this work a method is developed for determining the expected sample size, 2nN, required by a group sequential test using a Bayesian approach. This method is proved to be superior to some recently developed methods. It gives a specific technique to determine the maximum number of groups, N, as well as the group, size, 2n. The proposed method allows a very early termination of the experiment when the alternative hypothesis is true, i.e. when i there is real difference between the treatments under consideration.

UNANOVA: An unweighted-means analysis of variance program for the Tl-59 programmable calculator

A recent article demonstrated that the usefulness of the Texas Instruments (TI) advanced programmable calculators is not restricted to data analysis, but rather, it extends to data acquisition and control of experimental events (Robinson, 1979). Despite the flexibility and power of these calculators, and although a variety of statistical programs are available via the TI solid state program modules, the flexibility of software to perform analysis of variance (ANOVA) is severely limited. The program described here permits the calculator to be used to conduct an ANOVA on completely randomized factorial designs, with equal or unequal group sizes. The program was written for the TI 59 calculator, and it will print the results.' Program Description. The program performs an unweighted-means analysis of variance (UNANOVA) on any completely randomized singleor multiplefactor design (nonhierarchical). The number of crossed variables is limited in the maximum number of groups that may be entered. Specifically, this maximum is defined by the equation,