Modeling of epoxy dispensing process using a hybrid fuzzy regression approach

In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die bonding as well as in microchip encapsulation for electronic packaging. Modeling the epoxy dispensing process is important because it enables us to understand the process behavior, as well as determine the optimum operating conditions of the process for a high yield, low cost, and robust operation. Previous studies of epoxy dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy dispensing is difficult to develop because of its complex behavior and high degree of uncertainty associated with the process in a real-world environment. Previous studies of modeling the epoxy dispensing process have not addressed the development of explicit models involving high-order and interaction terms, as well as fuzziness between process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two process models are generated for the two quality characteristics of the process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression

An integrated approach of particle swarm optimization and support vector machine for gene signature selection and cancer prediction

To improve cancer diagnosis and drug development, the classification of tumor types based on genomic information is important. As DNA micro array studies produce a large amount of data, expression data are highly redundant and noisy, and most genes are believed to be uninformative with respect to the studied classes. Only a fraction of genes may present distinct profiles for different classes of samples. Classification tools to deal with these issues are thus important. These tools should learn to robustly identify a subset of informative genes embedded in a large dataset that is contaminated with high dimensional noises. In this paper, an integrated approach of support vector machine (SVM) and particle swarm optimization (PSO) is proposed for this purpose. The proposed approach can simultaneously optimize the selection of feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied to search the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients. Cross validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one out of fourteen patient samples, suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma

Large Photonic Band Gaps in Certain Periodic and Quasi-Periodic Networks in two and three dimensions

The photonic band structures in certain two- and three-dimensional periodic networks made of one-dimensional waveguides are studied by using the Floquet-Bloch theorem. We find that photonic band gaps exist only in those structures where the fundamental loop exhibits anti-resonant transmission. This is also true for quasi-periodic networks in two and three dimensions, where the photonic band structures are calculated from the spectra of total transmission arising from a source inside the samples. In all the cases we have studied, it is also found that the gap positions in a network are dictated by the frequencies at which the anti-resonance occurs.Comment: 7 pages, 10 figures and 1 table. Published in Phys. Rev. B, 70, 125104 (2004

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account

An Evolutionary Variable Neighborhood Search for Selecting Combinational Gene Signatures in Predicting Chemo-Response of Osteosarcoma

In genomic studies of cancers, identification of genetic biomarkers from analyzing microarray chip that interrogate thousands of genes is important for diagnosis and therapeutics. However, the commonly used statistical significance analysis can only provide information of each single gene, thus neglecting the intrinsic interactions among genes. Therefore, methods aiming at combinational gene signatures are highly valuable. Supervised classification is an effective way to assess the function of a gene combination in differentiating various groups of samples. In this paper, an evolutionary variable neighborhood search (EVNS) that integrated the approaches of evolutionary algorithm and variable neighborhood search (VNS) is introduced.It consists of a population of solutions that evolution is performed by a variable neighborhood search operator, instead of the more usual reproduction operators, crossover and mutation used in evolutionary algorithms. It is an efficient search algorithm especially suitable for tremendous solution space. The proposed EVNS can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. This method was applied in searching the combinational gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that EVNS outperforms the other existing approaches in classifying initial biopsy samples

Temporal Variability Modulates pH Impact On Larval Sea Urchin Development: Themed Issue Article: Biomechanics And Climate Change

Coastal organisms reside in highly dynamic habitats. Global climate change is expected to alter not only the mean of the physical conditions experienced but also the frequencies and/or the magnitude of fluctuations of environmental factors. Understanding responses in an ecologically relevant context is essential for formulating management strategies. In particular, there are increasing suggestions that exposure to fluctuations could alleviate the impact of climate change-related stressors by selecting for plasticity that may help acclimatization to future conditions. However, it remains unclear whether the presence of fluctuations alone is sufficient to confer such effects or whether the pattern of the fluctuations matters. Therefore, we investigated the role of frequency and initial conditions of the fluctuations on performance by exposing larval sea urchin Heliocidaris crassispina to either constant or fluctuating pH. Reduced pH alone (pH 7.3 vs 8.0) did not affect larval mortality but reduced the growth of larval arms in the static pH treatments. Changes in morphology could affect the swimming mechanics for these small organisms, and geometric morphometric analysis further suggested an overall shape change such that acidified larvae had more U-shaped bodies and shorter arms, which would help maintain stability in moving water. The relative negative impact of lower pH, computed as log response ratio, on larval arm development was smaller when larvae were exposed to pH fluctuations, especially when the change was less frequent (48- vs 24-h cycle). Furthermore, larvae experiencing an initial pH drop, i.e. those where the cycle started at pH 8.0, were more negatively impacted compared with those kept at an initial pH of 7.3 before the cycling started. Our observations suggest that larval responses to climate change stress could not be easily predicted from mean conditions. Instead, to better predict organismal performance in the future ocean, monitoring and investigation of the role of real-time environmental fluctuations along the dispersive pathway is key

A guided search genetic algorithm using mined rules for optimal affective product design

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design

An intelligent fuzzy regression approach for affective product design that captures nonlinearity and fuzziness

Affective product design aims at incorporating customers’ affective needs into design variables of a new product so as to optimise customers’ affective satisfaction. Faced with fierce competition in marketplaces, companies try to determine the settings in order to maximise customers’ affective satisfaction with products. To achieve this, a set of customer survey data is required in order to develop a model which relates customers’ affective responses to the design variables of a new product. Customer survey data are usually fuzzy since human feeling is usually fuzzy, and the relationship between customers’ affective responses and design variables is usually nonlinear. However, previous research on modelling the relationship between affective response and design variables has not addressed the development of explicit models involving either nonlinearity or fuzziness. In this paper, an intelligent fuzzy regression approach is proposed to generate models which represent this nonlinear and fuzzy relationship between affective responses and design variables. In order to do this, we extend the existing work on fuzzy regression by first utilising an evolutionary algorithm to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. The fuzzy regression algorithm is then used to determine the fuzzy coefficients of the model. The models thus developed are explicit, and consist of fuzzy, nonlinear terms which relate affective responses to design variables. A case study of affective product design of mobile phones is used to illustrate the proposed method

Common Hypercyclic Vectors for the Conjugate Class of a Hypercyclic Operator

Given a separable, infinite dimensional Hilbert space, it was recently shown by the authors that there is a path of chaotic operators, which is dense in the operator algebra with the strong operator topology, and along which every operator has the exact same dense Gδ set of hypercyclic vectors. In the present work, we show that the conjugate set of any hypercyclic operator on a separable, infinite dimensional Banach space always contains a path of operators which is dense with the strong operator topology, and yet the set of common hypercyclic vectors for the entire path is a dense Gδ set. As a corollary, the hypercyclic operators on such a Banach space form a connected subset of the operator algebra with the strong operator topology