A fuzzy approach for performance modeling in a batch plant:application to semiconductor manufacturing

In the current literature dealing with job shop scheduling, most of the approaches have developed models based on the assumption that the problem domain does not contain any imprecision. However, this hypothesis is strongly challenged in the implementation phase of such models-imprecision is inherent to production systems involving human intervention. The aim of this paper is to demonstrate the advantages of possibilistic production data modeling in a real-world application, i.e., semiconductor manufacturing. In this work, a discrete-event simulation model (MELISSA) for performance evaluation of a batch-manufacturing facility previously developed in our laboratory has been extended to treat uncertainties modeled by fuzzy numbers. Due to the confidential nature of industrial data, an illustrative example, presenting the same typical features as a real problem, is treated and analyzed using fuzzy concepts. Inclusion of fuzzy techniques provides the decision-maker with a range of possible values for completion times, average storage times, and operator workload instead of a unique value (which has little significance due to the variety of human operators). In addition, the negative portion of average waiting times yields useful information for the manager to detect deficient resources in the production system     

Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling

Co-evolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper, we combine the search power of co-evolutionary computation with the expressive power of fuzzy systems, and introduce a novel algorithm, called Fuzzy CoCo (fuzzy cooperative coevolution). We demonstrate the efficacy of Fuzzy CoCo by applying it to a hard, real-world problem - breast cancer diagnosis, obtaining the best results to date while expending less computational effort than previous processes. Analyzing our results, we derive guidelines for setting the algorithm parameters given a (hard) problem to solve. We hope Fuzzy CoCo proves to be a powerful tool in the fuzzy modeler toolkit     

An analytical approach to performance/ cost modeling of parallel computers

Although parallel processing has been a focal point of computer architecture research for many years, fundamental questions and trade-offs still remain puzzling, not necessarily because of complexity but because of the multitude of possible answers (e.g., shared vs distributed memory, centralized vs distributed control, vector vs scalar). This paper addresses one such issue, namely heterogeneous vs homogeneous parallel machine organizations. Using simple performance and cost models, we argue that multiprocessors based on a fast global control unit capable of fast execution of serial code, and capable of managing an ensemble of slower processors, offer a performance/ cost ratio significantly better than any comparable homogeneous multiprocessor with distributed control. Although the issue of “deliverable” performance is an open question, it appears that such systems can achieve faster execution and higher program speedups at a much lower cost.     

Organizational memory information systems: a transactive memory approach

Effective management of organizational memory (OM) is critical to collaboration and knowledge sharing in organizations. We present a framework for managing organizational memory based on transactive memory, a mechanism of collective memory in small groups. While being effective in small groups, there are difficulties hindering the extension of transactive memory to larger groups. We claim that information technology can be used to help overcome these difficulties. We present a formal architecture for directories of meta-memories required in extended transactive memory systems and propose the use of meta-knowledge to substitute for the lack of tacit group knowledge that exists in small groups.     

A novel approach to estimate proximity in a random forest: An exploratory study

A data proximity matrix is an important information source in random forests (RF) based data mining, including data clustering, visualization, outlier detection, substitution of missing values, and finding mislabeled data samples. A novel approach to estimate proximity is proposed in this work. The approach is based on measuring distance between two terminal nodes in a decision tree. To assess the consistency (quality) of data proximity estimate, we suggest using the proximity matrix as a kernel matrix in a support vector machine (SVM), under the assumption that a matrix of higher quality leads to higher classification accuracy. It is experimentally shown that the proposed approach improves the proximity estimate, especially when RF is made of a small number of trees. It is also demonstrated that, for some tasks, an SVM exploiting the suggested proximity matrix based kernel, outperforms an SVM based on a standard radial basis function kernel and the standard proximity matrix based kernel.     

DigitalSculpture: a subdivision-based approach to interactive implicit surface modeling

This paper presents DigitalSculpture, an interactive sculpting framework founded upon iso-surfaces extracted from recursively subdivided, 3D irregular grids. Our unique implicit surface model arises from an interpolatory, volumetric subdivision scheme that is C¹ continuous across the domains defined by arbitrary 3D irregular grids. We assign scalar coefficients and color to each control vertex and allow these quantities to participate in the volumetric subdivision of irregular grids. In the subdivision limit, a virtual sculpture is obtained by extracting the zero-level from the volumetric, scalar field defined over the irregular grid. This novel shape geometry extends concepts from solid modeling, recursive subdivision, and implicit surfaces; facilitates many techniques for interactive sculpting; permits rapid, local evaluation of iso-surfaces; and affords level-of-detail control of the sculpted surfaces.     

Nonlinear modeling of a SOFC stack based on ANFIS identification

An adaptive neural-fuzzy inference system (ANFIS) model is developed to study different flows effect on the performance of solid oxide fuel cell (SOFC). During the process of modeling, a hybrid learning algorithm combining backpropagation (BP) and least squares estimate (LSE) is adopted to identify linear and nonlinear parameters in the ANFIS. The validity and accuracy of modeling are tested by simulations and the simulation results reveal that the obtained ANFIS model can efficiently approximate the dynamic behavior of the SOFC stack. Thus it is feasible to establish the model of SOFC stack by ANFIS.     

MOSFET mismatch modeling: a new approach

Digital and analog ICs generally rely on the concept of matched behavior between identically designed devices. Time-independent variations between identically designed transistors, called mismatch, affect the performance of most analog and even digital MOS circuits. This article focuses on the analysis of mismatch in MOS transistors resulting from random fluctuations of the dopant concentration, first studied by Keyes. Today, we recognize these fluctuations as the main cause of mismatch in bulk CMOS transistors.     

A contribution to a multimodal approach to knowledge modeling

At present a steep increase in efforts by human beings in search of information take place. Pattern Recognition extracts information from the real world. It is a tool for modelling and real-world learning. Thus it requires precise knowledge of the objectives to be achieved with the information it produces. Human senses are part of their interface with the environment. Our senses are signal encoders according some representation that is understood by the central nervous system. Multimodality in signal capturing makes the spectrum of activities of intelligent agents wider. This paper aims to stimulate research for providing multimodal information, say, information that can be captured by different senses. This technique can improve decision-making; promote the inclusion of sense disabled individuals, giving higher amplitude for usefulness of Pattern Recognition. It considers multimodal display of knowledge to practical feasibility of knowledge presentation adapted to human sensing and perception towards decision improvement and inclusion of perception-impaired people.     

A Bayesian approach to estimate evoked potentials

Several approaches, based on different assumptions and with various degree of theoretical sophistication and implementation complexity, have been developed for improving the measurement of evoked potentials (EP) performed by conventional averaging (CA). In many of these methods, one of the major challenges is the exploitation of a priori knowledge. In this paper, we present a new method where the 2nd-order statistical information on the background EEG and on the unknown EP, necessary for the optimal filtering of each sweep in a Bayesian estimation framework, is, respectively, estimated from pre-stimulus data and obtained through a multiple integration of a white noise process model. The latter model is flexible (i.e. it can be employed for a large class of EP) and simple enough to be easily identifiable from the post-stimulus data thanks to a smoothing criterion. The mean EP is determined as the weighted average of the filtered sweeps, where each weight is inversely proportional to the expected value of the norm of the correspondent filter error, a quantity determinable thanks to the employment of the Bayesian approach. The performance of the new approach is shown on both simulated and real auditory EP. A signal-to-noise ratio enhancement is obtained that can allow the (possibly automatic) identification of peak latencies and amplitudes with less sweeps than those required by CA. For cochlear EP, the method also allows the audiology investigator to gather new and clinically important information. The possibility of handling single-sweep analysis with further development of the method is also addressed.     

The fuzzy crystallization algorithm: a new approach to complexsystems modeling

A new identification method for fuzzy modeling is introduced. Since the method has some analogy with the process of material crystallization in nature, the name of fuzzy crystallization algorithm (FCA) is given to this novel approach. This method accomplishes structure identification and parameter identification at the same time, and possesses the properties of simplicity, flexibility, and high calculation speed. Compared with other modeling strategies, it is easier to construct a model with a specific accuracy. Numerical examples are provided to demonstrate the performance of this approach.     

A comparative modeling study to estimate wear of concrete

This paper investigates the impacts of fuzzy genetic (FG), a new fuzzy logic model with genetic algorithm, artificial neural networks (ANN) and general linear model (GLM) approaches on abrasive wear of concrete. For this purpose, experimental studies were made to investigate the influence on wear of the following input parameters: hematite, cement, compressive strength and different loads on the experiments. In these models, 60 data sets were used. For training set, 48 data (80 %) were randomly selected and the residual data (12 data, 20 %) were test set. Model results were compared with experimental results. In this paper, main model performance criterion was root mean square errors. Also, sum of squared error and determination coefficient statistics were used as comparing criteria for the evaluation of models’ performances. Comparison results indicate that FG models are superior to ANN and GLM models in modeling of influence hematite, cement, compressive strength and loads on wear of concrete.     

Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

Cell tracking: a modeling and minimization approach

This paper presents a model of motion suitable for cell tracking. It includes a representation for cell dynamics enabling it to maintain a correspondence between successive images of cells undergoing morphological changes. This model is based on a minimization problem whose computational solution is similar in form to a Newton-Rhapson iteration. The model is supported by experimental results from an actual tracking problem.     

Commonsense approach to process modeling

The first phase of the three-phase ProSLCSE project, a US government-sponsored effort to give users of the Software Life-Cycle Support Environment (SLCSE) the ability to model and then execute processes, is described. The emphasis of the first phase is on providing a complete notation and vernacular for process definition. The proposed modeling notation includes infrastructure-modeling, information-modeling, and process-modeling notations that are intended to be easy to learn and use, even by busy project managers. The process concepts, modeling concepts, modeling notation, and process enactment of ProSLCSE are discussed     

One-eyed stereo: a general approach to modeling 3-d scene geometry

A single two-dimensional image is an ambiguous representation of the three-dimensional world?many different scenes could have produced the same image?yet the human visual system is ex-tremely successful at recovering a qualitatively correct depth model from this type of representation. Workers in the field of computational vision have devised a number of distinct schemes that attempt to emulate this human capability; these schemes are collectively known as ``shape from...' methods (e.g., shape from shading, shape from texture, or shape from contour). In this paper we contend that the distinct assumptions made in each of these schemes is tantamount to providing a second (virtual) image of the original scene, and that each of these approaches can be translated into a conventional stereo formalism. In particular, we show that it is frequently possible to structure the problem as one of recovering depth from a stereo pair consisting of the supplied perspective image (the original image) and an hypothesized orthographic image (the virtual image). We present a new algorithm of the form required to accomplish this type of stereo reconstruction task.     

Predicting the performance of a 3D processor-memory chip stack

We are exploring a 3D processor-memory stack for use with the message passing interface (MPI). The communication among processors in huge servers wastes several thousands of cycles. Most of these wasted cycles do not come from the communication link among the processors across the system, but rather in handling the message packets. A processor that could handle this message packing and communication at a much faster rate could significantly increase this task's efficiency and thus increase the utilization of such supercomputers, currently a very low 1%. However, at such high clock rates, the memory wall would become a significant problem. Tackling this problem requires innovative technologies, such as 3D memories, which alleviate some problems with long on-chip interconnects. The importance of interconnection wires to circuit performance is on a chip. The need for shorter interconnection delays suggests shorter interconnection wires. Shorter interconnections are more likely in 3D architectures than in equivalent 2D systems. This article explores the advantages of 3D in a processor-memory stack system. We conducted simulations using simple tools like Dinero IV and the cache access and cycle time information (Cacti) to evaluate the performances of various memory architectures.     

Immersive structural biology: a new approach to hybrid modeling of macromolecular assemblies

Advanced biophysical imaging techniques, such as cryo-electron microscopy or tomography, enable 3D volumetric reconstructions of large macromolecular complexes in a near-native environment. However, pure volumetric data is insufficient for a detailed understanding of the underlying protein–protein interactions. This obstacle can be overcome by assembling an atomic model of the whole protein complex from known atomic structures, which are available from either X-ray crystallography or homology modeling. Due to many factors such as noise, conformational variability, experimental artifacts, and inexact model structures, existing automatic docking procedures are known to report false positives for a significant number of cases. The present paper focuses on a new technique to combine an offline exhaustive search algorithm with interactive visualization, collision detection, and haptic rendering. The resulting software system is highly immersive and allows the user to efficiently solve even difficult multi-resolution docking problems. Stereoscopic viewing, combined with head tracking and force feedback, generates an ideal virtual environment for true interaction with and solution of hybrid biomolecular modeling problems.     

A systemic approach to process modeling

This article presents a system's view of a common sense management model for systems (COSMOS) [1]. Salient features of COSMOS are introduced through the unfolding story of process development of a hypothetical corporation called IM Co. This systemic view models the dynamic complexity of a system or organization so that inerrelationships, rather than things, patterns of changes, rather than snapshots, are captured. COSMOS views changes as an ongoing opportunity and provides guidance for system changes to be performed in small steps. However, these small steps can build a long lever that is capable of producing dramatic effects. When performing changes, essential trade-offs have to be considered. COSMOS provides three perspectives—activity, communication, and infrastructure—of a process to assist managers in dealing with these trade-offs. The model also includes a generic two-level hierarchy—control and execution levels—to keep balance among the three perspectives.Small Is Beautiful — Ernst Fredrich Schumacher Give me a lever long enough ... and single-handed I can move the world — Archimedes