Applications of fuzzy‐based linguistic patterns for the assessment of computer screen design quality

The main objective of this study was to develop a modeling framework which would unify different aspects of computer screen design and result in a quantitative criterion for an optimized computer screen format. The fuzzy set‐based linguistic design patterns were utilized as a tool to build this model. The linguistic patterns are based on categories of expressions related closely to natural language and truth values, which are close to a human designer's intuition. The proposed framework is capable of assessing the quality of computer screen design based on existing knowledge in human‐computer interface domain using the fuzzy‐based linguistic pattern approach. Exemplary patterns for an optimal screen density, information grouping, and some aspects of screen layout are presented, along with a sequence of calculations based on the exemplary screen format. This study showed that it is possible to achieve a rational and relatively easy to interpret assessment of different screen designs in the form of the degrees of truth. Such an evaluation criterion reflects the compatibility of a given screen design with the optimal one based on the current knowledge in the field. It is believed that the proposed methodological framework for computer screen design should significantly augment the efforts of human designers.     

Classifying the risk of work related low back disorders due to manual material handling tasks

Work related low back disorders (LBDs) due to manual lifting tasks (MLTs) have long been recognized as one of the main occupational disabling injury that affects the quality of life of the industrial working population in the U.S. There have been a number of intensive research efforts devoted to understanding the phenomena of LBDs and building classification models that could effectively distinguish between high risk and low risk MLTs that contribute to LBDs. As of today, however, such models and the occupational exposure limits of different risk factors causing LBDs as well as the guidelines preventing them have not yet been fully proposed. One of the first efforts to comprehend the nature and phenomenon of LBDs was undertaken by Marras et al. (1993). They created a seminal data set and used it to build logistic regression (LR) models to identify significant variables and classify MLTs into high risk and low risk with respect to LBDs. Since then a number of studies have used the same data set to build and test various classifiers to detect the likelihood of LBDs due to manual material handling jobs. This paper summarizes and critiques the previous studies. It also employs this data set to build and test seven classification models, two of which have not been applied in this context yet. The parameters of the models have been calibrated for the best performance, and the models were constructed and validated on the full set and the reduced set of features. Though the performances of our best models are better than those reported in National Institute for Occupational Health and Safety (NIOHS) Guides and two of our previous studies, they are generally less optimistic than those reported in several other studies; this paper proposes a systematic and more reliable approach to creating and validating classifiers to distinguish between low and high risk MLTs that contribute to LBDs.     

A new design method for the complex-valued multistate Hopfield associative memory

A method to store each element of an integral memory set M subset {1,2,...,K}/sup n/ as a fixed point into a complex-valued multistate Hopfield network is introduced. The method employs a set of inequalities to render each memory pattern as a strict local minimum of a quadratic energy landscape. Based on the solution of this system, it gives a recurrent network of n multistate neurons with complex and symmetric synaptic weights, which operates on the finite state space {1,2,...,K}/sup n/ to minimize this quadratic functional. Maximum number of integral vectors that can be embedded into the energy landscape of the network by this method is investigated by computer experiments. This paper also enlightens the performance of the proposed method in reconstructing noisy gray-scale images.     

Nonlinear blind source separation using a radial basis functionnetwork

This paper proposes a novel neural-network approach to blind source separation in nonlinear mixture. The approach utilizes a radial basis function (RBF) neural-network to approximate the inverse of the nonlinear mixing mapping which is assumed to exist and able to be approximated using an RBF network. A contrast function which consists of the mutual information and partial moments of the outputs of the separation system, is defined to separate the nonlinear mixture. The minimization of the contrast function results in the independence of the outputs with desirable moments such that the original sources are separated properly. Two learning algorithms for the parametric RBF network are developed by using the stochastic gradient descent method and an unsupervised clustering method. By virtue of the RBF neural network, this proposed approach takes advantage of high learning convergence rate of weights in the hidden layer and output layer, natural unsupervised learning characteristics, modular structure, and universal approximation capability. Simulation results are presented to demonstrate the feasibility, robustness, and computability of the proposed method.     

Automated tuning of an electronic circuit board using the artificial neural network approach

Manufacturing of electronic circuits for microwave communication boards often requires tuning of different circuit characteristics by manual adjustment of several trimmer components, including the trimmer's resistance and capacitance. This manual tuning process was automated by applying the artificial neural network modeling approach. In the considered tuning process, which required manual adjustment of a set of trimmers, multiple specification criteria had to be satisfied by several trimmer rotations. The tuning process was described in terms of three independent steps: the circuit output measurement, trimmer selection, and trimmer rotation. The trimmer selection was performed by a semi-supervised neural network, which learned the patterns of circuit characteristics and the deviations between the ideal and practical outputs. Another network was developed for determination of trimmer rotation rate. The results, based on computer simulation of the tuning process, showed that the developed system improved performance of the tuning process, allowing for automation of the microwave circuit board tuning task in a real manufacturing environment.     

Deterministic convergence of conjugate gradient method for feedforward neural networks

Conjugate gradient methods have many advantages in real numerical experiments, such as fast convergence and low memory requirements. This paper considers a class of conjugate gradient learning methods for backpropagation neural networks with three layers. We propose a new learning algorithm for almost cyclic learning of neural networks based on PRP conjugate gradient method. We then establish the deterministic convergence properties for three different learning modes, i.e., batch mode, cyclic and almost cyclic learning. The two deterministic convergence properties are weak and strong convergence that indicate that the gradient of the error function goes to zero and the weight sequence goes to a fixed point, respectively. It is shown that the deterministic convergence results are based on different learning modes and dependent on different selection strategies of learning rate. Illustrative numerical examples are given to support the theoretical analysis.     

A neuro-fuzzy approach for robot system safety

Robot safety is a critical and largely unsolved problem involving the interaction of man and machine. The paper presents a new approach to robot safety which uses an integrated sensing architecture for monitoring the robot workspace, and a new detection and decision logic for regulating the safe operation of the robot. Sensory information is fused through a trained neural network to produce a map of the hazards. Using this combined map, and information about the robot's current position and velocity, a set of fuzzy logic rules has been implemented to regulate robot activity. Simulation results presented in the paper indicate that this method is both effective in detection of potentially hazardous situations and computationally feasible     

Analog implementation of neural networks

Analog silicon-based neural hardware, which represents a large category among special-purpose analog and digital neurocomputers, and neural processing algorithms are reviewed. Artificial neural networks usually contain a large number of synaptic connections and many fewer processing neurons. The central problem in implementing artificial neural networks-making weights that are continuously adjustable, preferably in response to an analog control signal-is discussed. A simple integrated-circuit analog multiplier built from all-MOS components for use in electrically tunable synapses is described     

Equivalent circuit of multiplying d.a.c. using R-2R ladder network

An equivalent circuit of a multiplying digital-analogue convertor using a switched R-2R ladder is derived. This circuit consists of dependent sources, output resistance and capacitance, all digitally controlled. For a controlled voltage attenuator the multiplying d.a.c. model has been used to ensure the correct compensation of the practical multiplying d.a.c./opamp system.