Multi-objective decision-making methodology to create an optimal design chain partner combination

In today’s highly competitive business environment, many companies adopt the time-to-market strategy to obtain a competitive advantage. To reduce the time and cost of product development and to employ global product development resources, design chain partner evaluation and selection has become a crucial issue. Thus, establishing an optimal design chain partner combination has received significant attention because it has a far-reaching effect on the results of product development. With this perspective, this paper develops an integrated decision-making methodology to assist enterprises as they create an optimal design chain partner combination. First, this study establishes the framework and evaluation models of the criteria for the different roles of design chain partners, including system integration, functional module development and software and component development. Then, this paper applies a weight-restricted DEA (data envelopment analysis) approach to create the models for performance analysis of design chain partners to acquire the performance value of each candidate and select the efficient design chain partners. Moreover, this paper employs the multi-objective performance evaluation model proposed in this paper to analyze the synthesized performance of design chain combinations. Moreover, this research uses a multi-objective genetic algorithm (GA) to search efficiently for the optimal design chain partner combination to minimize product development cost and time and maximize product reliability. Finally, this study employs a derivative new product development project for a digital TV box as a case study to illustrate the efficacy of the proposed methodology.     

Soft syntactic constraints for Arabic–English hierarchical phrase-based translation

In adding syntax to statistical machine translation, there is a tradeoff between taking advantage of linguistic analysis and allowing the model to exploit parallel training data with no linguistic analysis: translation quality versus coverage. A number of previous efforts have tackled this tradeoff by starting with a commitment to linguistically motivated analyses and then finding appropriate ways to soften that commitment. We present an approach that explores the tradeoff from the other direction, starting with a translation model learned directly from aligned parallel text, and then adding soft constituent-level constraints based on parses of the source language. We argue that in order for these constraints to improve translation, they must be fine-grained: the constraints should vary by constituent type, and by the type of match or mismatch with the parse. We also use a different feature weight optimization technique, capable of handling large amount of features, thus eliminating the bottleneck of feature selection. We obtain substantial improvements in performance for translation from Arabic to English.     

Thermal resistance analysis and validation of flip chip PBGA packages

This work proposes a finite element numerical methodology to predict the thermal resistance of both flip chip-plastic ball grid array (FC-PBGA) with a bare die and FC-PBGA with a metal cap. The 3D finite element model was initially constructed to simulate the thermal resistance of FC-PBGA. A thermal resistance experiment was performed to verify the FEM results, following the construction of specimens of FC-PBGA with a bare die and with an aluminum cap, using six-layered substrate. The verified finite element model was employed to determine the thermal resistance of FC-PBGA with a copper cap using four-layered and six-layered substrates. Experimental results demonstrated that FC-PBGA with a metal cap improves thermal performance by 35% over with a bare die. FC-PBGA with a copper cap slightly improves thermal performance from 2% to 2.8% over that of FC-PBGA with an aluminum cap. The thermal resistance of FC-PBGA with a four-layered substrate is reduced by 4.0% to 5.9% from that of FC-PBGA with a six-layered substrate, since the four-layered substrate contains less metal. The finite element numerical results negligibly differ from the experimental results by 6% to 8.1%. A finite element numerical methodology is here proposed to predict the thermal resistance of FC-PBGA. The methodology is effective in researching and developing new products or improving existing packages.     

Progressive Simplification of Tetrahedral Meshes Preserving All Isosurface Topologies

In this paper, we propose a novel technique for constructing multiple levels of a tetrahedral volume dataset whilepreserving the topologies of all isosurfaces embedded in the data. Our simplification technique has two majorphases. In the segmentation phase, we segment the volume data into topological‐equivalence regions, that is, thesub‐volumes within each of which all isosurfaces have the same topology. In the simplification phase, we simplifyeach topological‐equivalence region independently, one by one, by collapsing edges from the smallest to the largesterrors (within the user‐specified error tolerance, for a given error metrics), and ensure that we do not collapseedges that may cause an isosurface‐topology change. We also avoid creating a tetrahedral cell of negative volume(i.e., avoid the fold‐over problem). In this way, we guarantee to preserve all isosurface topologies in the entiresimplification process, with a controlled geometric error bound. Our method also involves several additionalnovel ideas, including using the Morse theory and the implicit fully augmented contour tree, identifying typesof edges that are not allowed to be collapsed, and developing efficient techniques to avoid many unnecessary orexpensive checkings, all in an integrated manner. The experiments show that all the resulting isosurfaces preservethe topologies, and have good accuracies in their geometric shapes. Moreover, we obtain nice data‐reductionrates, with competitively fast running times.     

A new kernel-based fuzzy clustering approach: support vector clustering with cell growing

In this paper, the support vector clustering is extended to an adaptive cell growing model which maps data points to a high dimensional feature space through a desired kernel function. This generalized model is called multiple spheres support vector clustering, which essentially identifies dense regions in the original space by finding their corresponding spheres with minimal radius in the feature space. A multisphere clustering algorithm based on adaptive cluster cell growing method is developed, whereby it is possible to obtain the grade of memberships, as well as cluster prototypes in partition. The effectiveness of the proposed algorithm is demonstrated for the problem of arbitrary cluster shapes and for prototype identification in an actual application to a handwritten digit data set.     

Query optimization in multidatabase systems considering schemaconflicts

In a multidatabase system, the participating databases are autonomous. The schemas of these databases may be different in various ways, while the same information is represented. A global query issued against the global database needs to be translated to a proper form before it can be executed in a local database. Since data requested by a query (or a part of a query) is sometimes available in multiple sites, the site (database) that processes the query with the least cost is the desired query processing site. The authors study the effect of differences in schemas on the cost of query processing in a multidatabase environment. They first classify schema conflicts to different types. For each type of conflict, they show how much more or less complex a translated query can become in comparison with the originally user-issued global query. Based on this observation, they propose an analytical method that considers the conflicts between local databases and finds the database(s) that renders the least execution cost in processing a global query. This research introduces a new level of query optimization (termed the schema-level optimization) in multidatabase environments. The results provide a new dimension of enhancement for the capability of a query optimizer in multidatabase systems     

Knowledge-based system for railway scheduling

In this paper, we describe a Knowledge-Based railway scheduling system (called RSS) for Taiwan Railway Administration's (TRA) railway scheduling operations. In our approach, the scheduling process is divided into two levels: global scheduling and local scheduling. In global scheduling, an initial train diagram is established by the Global Scheduler according to the master scheduling plan without considering conflicts. In local scheduling, scheduling conflicts are repaired by Local Scheduler. An embedded knowledge base provides advice for conflict resolution. Since cycles may exist among a sequence of repairs during the repair process, we also propose a cycle detection and resolution scheme in this paper. Through a graphic user interface, the user can interact with the Automatic Scheduler to solve the large-scale complex train scheduling problem in a cooperative and flexible fashion.     

Genetic algorithms combined with discriminant analysis for key variable identification

Many trouble-shooting problems in process industries are related to key variable identification for classifications. The contribution charts, based on principal component analysis (PCA), can be applied for this purpose. Genetic algorithms (GAs) have been proposed recently for many applications including variable selection for multivariate calibration, molecular modeling, regression analysis, model identification, curve fitting, and classification. In this paper, GAs are incorporated with Fisher discriminant analysis (FDA) for key variable identification. GAs are used as an optimization tool to determine variables that maximize the FDA classification success rate for two given data sets. GA/FDA is a proposed solution for the variable selection problem in discriminant analysis. The Tennessee Eastman process (TEP) simulator was used to generate the data sets to evaluate the correctness of the key variable selection using GA/FDA, and the T² and Q statistic contribution charts. GA/FDA correctly identifies the key variables for the TEP case studies that were tested. For one case study where the correlation changes in two data sets, the contribution charts incorrectly suggest that the operating conditions are similar. On the other hand, GA/FDA not only determines that the operating conditions are different, but also identifies the key variables for the change. For another case study where many key variables are responsible for the changes in the two data sets, the contribution charts only identifies a fraction of the key variables, while GA/FDA correctly identifies all of the key variables. GA/FDA is a promising technique for key variable identification, as is evidenced in successful applications at The Dow Chemical Company.     

A low-photocurrent CMOS retinal focal-plane sensor with a pseudo-BJT smoothing network and an adaptive current Schmitt trigger for scanner applications

In this work, a new structure of low-photocurrent CMOS retinal focal-plane sensor with pseudo-BJT smoothing network and adaptive current Schmitt trigger is proposed. The proposed structure is very simple and compact. This new circuit can easily be implemented in CMOS technology with a small chip area. Another innovation of this circuit is that the proposed circuit could be operated for low-induced current levels (pA), and the current hysteresis of the proposed current Schmitt trigger could be adjusted adaptively according to the value of induced photocurrents. In this work, the detection of static and moving objects, such as a moving white bar, are proven by projecting a pattern through HSPICE simulation. The proposed retinal focal-plane sensor includes a 32 /spl times/ 32 pixel array with a pixel size of 70 /spl times/ 70 /spl mu/m/sup 2/. The fill factor is 75% and the total chip area is 3000 /spl times/ 3030 /spl mu/m/sup 2/. It is with fully functional 32 /spl times/ 32 implementations consuming less than 8.8 /spl mu/W per pixel at 3.3 V. Measurement results show that the proposed new retinal focal-plane sensor has successfully been used in character recognition of scanner systems, such as pen scanners, etc.     

The Emotional Integration of Childhood Experience: Physiological, Facial Expressive, and Self-Reported Emotional Response During the Adult Attachment Interview.

Attachment researchers claim that individual differences in how adults talk about their early memories reflect qualitatively distinct organizations of emotion regarding childhood experiences with caregivers. Testing this assumption, the present study examined the relationship between attachment dimensions and physiological, facial expressive, as well as self-reported emotional responses during the Adult Attachment Interview (AAI). Consistent with theoretical predictions, more prototypically secure adults behaviorally expressed and reported experiencing emotion consistent with the valence of the childhood events they described. Insecure adults also showed distinctive and theoretically anticipated forms of emotional response: Dismissing participants evidenced increased electrodermal activity during the interview, a sign of emotional suppression, whereas preoccupied adults showed reliable discrepancies between the valence of their inferred childhood experiences and their facial expressive as well as reported emotion during the AAI. Results substantiate a case that the AAI reflects individual differences in emotion regulation that conceptually parallel observations of attachment relationships in infancy. (PsycINFO Database Record (c) 2010 APA, all rights reserved)