Catálogo de publicaciones - libros

We have insight into the importance of resource exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligence-based model using quantum minimization (QM) to tune a composite model of adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of resources exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.

A lot of test cases must be executed in statistical software testing to simulate the usage of software. Therefore automated oracle is needed to automatically generate the expected outputs for these test cases and compare the actual outputs with them. An attempt has been made in this paper to use neural networks as automated test oracle. The oracle generates the approximate output that is close to expected output. The actual output from the application under test is then compared with the approximate output to validate the correctness. By the method, oracle can be automated. It is of potential application in software testing.

During the era of the rapid automation of the manufacturing processes, the automation of the metal cutting and drilling process, which is one of the most crucial stages in the industrial process, has become inevitable. The most important difficulty in the automation of machining process is time and production loss that occurs as a result of tool wear and tool breakage. In this study, a fuzzy logic based decision mechanism was developed to determine tool wear condition by using cutting forces. The statistical parameters of the cutting forces collected during the drilling operation have been determined as variables for the membership functions of the fuzzy logic decision mechanism. The system developed in this study, successfully determined the tool wear condition in drilling processes.

The neural network diagnosis method based on fault features denoted by frequency domain kernel in nonlinear circuit was presented here. Each order frequency domain kernel of circuit response under all fault states can be got by vandermonde method; the circuit features extracted was preprocessed and regarded as input samples of neural network, faults is classified. The uniform recurrent arithmetical formula of each order frequency-domain kernel was given, the Volterra frequency-domain kernel acquisition method was discussed, and the fault diagnosis method based on recurrent neural network was showed. A fault diagnosis illustration verified this method. The fault diagnosis method showed the advantages: no precise circuit model is needed in avoiding the difficulty in identifying nonlinear system online, less computation amount, high fault diagnosis efficiency.

Failure detection in machine condition monitoring involves a classification mainly on the basis of data from normal operation, which is essentially a problem of one-class classification. Inspired by the successful application of KFA (Kernel Function Approximation) in classification problems, an approach of KFA-based normal condition domain description is proposed for outlier detection. By selecting the feature samples of normal condition, the boundary of normal condition can be determined. The outside of this normal domain is considered as the field of outlier. Experiment results indicated that this method can be effectively and successfully applied to gear crack diagnosis.

A framework model proposed in this paper is a data-link Equipment Redundant Strategy based on reliability theory. The strategy combined with the normal maintenance could greatly improve the performance of the network system. The static-checking and policy of authentication mechanism ensure the running network without any error. The redundant equipments are independent but are capable of communication with each other when they work their actions. The model is independent of specific application environment, thus providing a general-purpose framework for fault diagnosis. An example is given to express the calculating method.

This paper deals with the problem of minimizing the maximum completion time (makespan) of jobs on identical parallel machines. A Hopfield type dynamical neural network is proposed for solving the problem which is known to be NP-hard even for the case of two machines. A penalty function approach is employed to construct the energy function of the network and time evolving penalty coefficients are proposed to be used during simulation experiments to overcome the tradeoff problem. The results of proposed approach tested on a scheduling problem across 3 different datasets for 5 different initial conditions show that the proposed network converges to feasible solutions for all initialization schemes and outperforms the LPT (longest processing time) rule.

For cleaning silicon wafers via the RCA clean, temperature control is important in order to obtain a stable performance, but it is difficult mainly because the RCA solutions expose nonlinear and time-varying exothermic chemical reactions. So far, the MSPC (model switching predictive controller) using the CAN2 (competitive associative net 2) has been developed and the effectiveness has been validated. However, we have observed that the control performance, such as overshoot and settling time, does not always improve as the number of learning iterations increases when using multiple units of the CAN2. So we apply the ensemble learning scheme to the CAN2 for stable control over learning iterations, and we examine the effectiveness of the present method by means of computer simulation.

It is difficult to predict the mechanical properties of welded joints because of non-linearity in welding process and complicated mutual effects in multi composition welding material. Based on these practical problems, the application of neural network technology in predicting mechanical properties of welding joints is developed. The modeling method has been studied and the author puts forward that the parameters of neutral network can be optimized by the method of uniform design. The neutral network model of mechanical properties of welding joints is established on the basis of the data of welding thermal simulation, and the experimental results show that this model can predict the mechanical properties including impact toughness, tensile strength, subdued strength, reduction ratio of area and hardness more accurately. At the same time, using this method can improve estimating precision largely compared with using traditional statistic method. That is, this method provides an effective approach to estimate the mechanical properties of welding joints.