Search

According to previous studies, the workforce competencies of engineers in manufacturing industries generally comprise technical, generic, and knowledge competencies. However, these categories of workforce competencies are insufficient for firms whose manufacturing processes are very easily interrupted by anomalies and whose core competency is highly related to production yield. In addition to drawing attention to the importance of identifying engineers’ problem-solving competencies, this article proposes a procedure to identify and rate workforce competencies for firms in the manufacturing industry. An IC packaging process engineer was used as an example to illustrate the proposed competency identification and rating procedure. In contrast to previous studies, this article suggests that problem-solving competencies can be identified from engineers’ responses to the anomalies commonly encountered in each step of the manufacturing process and that operational competency can be identified from the knowledge, techniques, or capabilities required to fulfill a task in each step of the manufacturing process. For the IC packaging and testing industry, this article can help human resources not only evaluate the competency of job candidates but also design training courses according to the job tenure of engineers. Furthermore, this article results can be used to design teaching material for apprenticeship to help engineers develop competencies in a structured manner and enhance their sensitivities to production-line problems.

Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients’ conditions by healthcare professionals but allow for a more optimal use of healthcare resources.

The integration of statistical process control and engineering process control has been reported as an effective way to monitor and control the autocorrelated process. However, because engineering process control compensates for the effects of underlying disturbances, the disturbance patterns become very hard to recognize, especially when various abnormal control chart patterns are mixed and co-existed in the engineering process. In this study, a new control chart pattern recognition model which integrates multivariate adaptive regression splines and recurrent neural network is proposed to not only address the problem of feature selection (i.e., lagged process measurements) but also improve the pattern recognition accuracy. The performance of the proposed method is evaluated by comparing the recognition results of multivariate adaptive regression splines and recurrent neural network with the results of four competing approaches (multivariate adaptive regression splines-extreme learning machine, multivariate adaptive regression splines-random forest, single recurrent neural network, and single random forest) on the simulated individual process data. The experimental study shows that the proposed multivariate adaptive regression splines and recurrent neural network approach can not only solve the problem of variable selection but also outperform other competing models. Moreover, according to the lagged process measurements selected by the proposed approach, lagged observations that exerted significant impact on the construction of the control chart pattern recognition model can be identified successfully. This study has significant implications for research and practice in production management and provides a valuable reference for manufacturing process managers to better understand and develop strategies for control chart pattern recognition.

An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient’s age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.

One of the primary tasks for process personnel is to detect and identify the underlying process disturbances such that they can be quickly removed. Most research has concluded that the integration of statistical process control (SPC) and engineering process control (EPC) is an effective way to achieve this task. Although this integration may lead to many benefits, it could result in problems with control chart pattern recognition. The EPC adjustments could cause the underlying disturbance patterns to be embedded in the control chart, thus dramatically increasing the degree of difficulty to identify the behavior of process disturbances. This study considers a zero-order autoregressive and integrated moving average process (ARIMA) that contains five common process disturbances. In addition, the minimum mean squared error (MMSE) control actions serve as the role of the EPC. In contrast to using the conventional soft computing methods, this study proposes two emerging soft computing techniques, extreme learning machine (ELM) and random forest (RF), to address the difficulties for recognition of embedded disturbance patterns in the control charts. Experimental results revealed that the proposed approaches are able to effectively recognize various disturbance patterns of an SPC-EPC process.

Heart disease is the leading cause of death among both men and women in most countries in the world. Thus, people must be mindful of heart disease risk factors. Although genetics play a role, certain lifestyle factors are crucial contributors to heart disease. Traditional approaches use thirteen risk factors or explanatory variables to classify heart disease. Diverging from existing approaches, the present study proposes a new hybrid intelligent modeling scheme to obtain different sets of explanatory variables, and the proposed hybrid models effectively classify heart disease. The proposed hybrid models consist of logistic regression (LR), multivariate adaptive regression splines (MARS), artificial neural network (ANN), and rough set (RS) techniques. The initial stage of the proposed process includes the use of LR, MARS, and RS techniques to reduce the set of explanatory variables. The remaining variables are subsequently used as inputs for the ANN method employed in the second stage. A real heart disease data set was used to demonstrate the development of the proposed hybrid models. The modeling results revealed that the proposed hybrid schemes effectively classify heart disease and outperform the typical, single-stage ANN method.

This paper combines two multicriteria decision-making (MCDM) tools to identify the critical success factors (CSFs) that impact the performance of personnel training course projects in the semiconductor industry in Taiwan. The study collected the factors that influence performance by interviewing experts and constructed a hierarchical factor table. The fuzzy analytic hierarchy process was used to determine the weight of each factor, and the acceptance advantage concept of VlseKriterijumska Optimizacija I Kompromisno Resenje was used to objectively identify the CSFs. The study found four CSFs that affect project performance and proposed five practical implications. This paper verified that combining two MCDM tools can help training institutions to allocate their resources appropriately by focusing on the CSFs, identified by their weights, and thus, promote their training courses at lower cost and with greater efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

Forecasting stock prices is a major activity of financial firms and private investors when they make investment decisions. Feature extraction is usually the first step of a stock price forecasting model development. Wavelet transform, used mainly for the extraction of information contained in signals, is a signal processing technique that can simultaneously analyze the time domain and the frequency domain. When wavelet transform is employed to construct a forecasting model, the wavelet basis functions and decomposition stages need to be determined first. However, because forecasting models constructed by different wavelet sub-series would exhibit different forecasting capabilities and yield varying forecast results, the selection of wavelet that can lead to an optimal forecast outcome is extremely critical in model construction. In this study, a new stock price forecasting model which integrates wavelet transform, multivariate adaptive regression splines (MARS), and support vector regression (SVR) (called Wavelet-MARS-SVR) is proposed to not only address the problem of wavelet sub-series selection but also improve the forecast accuracy. The performance of the proposed method is evaluated by comparing the forecasting results of Wavelet-MARS-SVR with the ones made by other five competing approaches (Wavelet-SVR, Wavelet-MARS, single ARIMA, single SVR and single ANFIS) on the stock price data of two newly emerging stock markets and two mature stock markets. The empirical study shows that the proposed approach can not only solve the problem of wavelet sub-series selection but also outperform other competing models. Moreover, according to the sub-series which are selected by the proposed approach, we can successfully identify the data of which sessions (or points in time) among past stock market prices exerted significant impact on the construction of the forecasting model.

Forecasting stock prices is a major activity of financial firms and private investors. In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA), a novel feature extraction technique that assumes the observed mixtures are non-linear combinations of latent source signals, is used to find independent sources when observed data are mixtures of unknown sources, and prior knowledge of the mixing mechanisms is not available. In this paper, a stock price forecasting model which first uses NLICA as preprocessing to extract features from forecasting variables is developed. Then the features, called independent components (ICs), serve as the inputs of support vector regression (SVR) to build the forecasting model. The advantage of the proposed methodology is that the information hidden in the original data can be discovered by feature extraction. Therefore, NLICA can provide more valuable information for financial forecasting. Two datasets of major Asian stock markets-China and Japan, Shanghai Stock Exchange Composite (SSEC) and Nikkei 225 stock indexes, are used as illustrative examples. For comparison, the integration of traditional principal component analysis (PCA) with SVR (called PCA-SVR), linear ICA with SVR (called LICA-SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Empirical results show that the proposed method (NLICA-SVR) not only improves the prediction accuracy of the SVR approach but also outperforms the PCA-SVR, LICA-SVR and single SVR methods.

A Bayesian latent variable model with classification and regression tree approach is built to overcome three challenges encountered by a bank in credit-granting process. These three challenges include (1) the bank wants to predict the future performance of an applicant accurately; (2) given current information about cardholders' credit usage and repayment behavior, financial institutions would like to determine the optimal credit limit and APR for an applicant; and (3) the bank would like to improve its efficiency by automating the process of credit-granting decisions. Data from a leading bank in Taiwan is used to illustrate the combined approach. The data set consists of each credit card holder's credit usage and repayment data, demographic information, and credit report. Empirical study shows that the demographic variables used in most credit scoring models have little explanatory ability with regard to a cardholder's credit usage and repayment behavior. A cardholder's credit history provides the most important information in credit scoring. The continuous latent customer quality from the Bayesian latent variable model allows considerable latitude for producing finer rules for credit granting decisions. Compared to the performance of discriminant analysis, logistic regression, neural network, multivariate adaptive regression splines (MARS) and support vector machine (SVM), the proposed model has a 92.9% accuracy rate in predicting customer types, is less impacted by prior probabilities, and has a significantly low Type I errors in comparison with the other five approaches.

Many studies have been conducted over the past several years evaluating the integrated use of statistical process control (SPC) and engineering process control (EPC). The majority of these studies reported that combining SPC with EPC outperforms the use of only SPC or EPC. Basically, the former aims to rapidly detect assignable causes and time points for abnormalities that take place during process; and the latter is a method in which input variables are adjusted against process outputs through a feedback control mechanism. Although combining SPC with EPC can effectively detect time points when abnormalities occur during process, their combination can also cause an increased occurrence of false alarms when autocorrelation is present in the process. In this study, to increase the accuracy of process disturbance identification, we propose the integration of spatiotemporal independent component analysis (stICA) with the classification and regression tree (CART) approach to improve our capability to identify process disturbances and recognize shifts in the correlated process parameters. The integration of the CART methodology results in the development of decision rules that can provide valuable information related to the impact of variation in process variable values. These decision rules can provide an increased understanding of process behavior and useful information for process control. For comparison, the integration of traditional principle component analysis (PCA) with CART (called PCA-CART), ICA with CART (called ICA-CART) and cumulative sum chart approaches were applied to evaluate the identification capability of the proposed approach. As the results reveal, the proposed approach is more effective for monitoring correlated process.

As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.

In order to reduce the variation in a manufacturing process, traditional statistical process control (SPC) techniques are the most frequently used tools in monitoring engineering process control (EPC)-controlled processes for detecting assignable cause process variation. Even though application of SPC with EPC can successfully detect time points when abnormalities occur during process, their combination can also cause an increased occurrence of false alarms when autocorrelation is present in the process. In this paper, we propose an independent component analysis-based signal extraction technique with classification and regression tree approach to identify disturbance levels in the correlated process parameters. For comparison, traditional cumulative sum (CUSUM) chart was constructed to evaluate the identifying capability of the proposed approach. The experimental results show that the proposed method outperforms CUSUM control chart in most instances. Copyright © 2008 John Wiley & Sons, Ltd.

Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process, and interpretative difficulties have often been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.

With the help of advanced information technology, it is no longer a difficult task in collecting relevant data sets of customers. However, the data sets growth too fast, it is not easy to identify the complicate relationship in the huge data sets. Moreover, the traditional management information systems can only conduct basic descriptive statistics with respect to the collected data and therefore unable to dig out important and latent information inside the data. Data mining is a fast growing application area in business. With data mining techniques, it allows the possibility of computer-driven exploration of the data, and we don't need to assume some hypothesis for the data. The purpose of this research is to provide a complete data analysis process, and there are two main stages included. In the first stage, we used Fuzzy ART to identify an appropriate number of clusters for the data. In the second stage, we integrated neural networks and classification and regression tree (CART) to solve the c...

Since solely using statistical process control (SPC) and engineering process control (EPC) cannot optimally control the manufacturing process, lots of studies have been devoted to the integrated use of SPC and EPC. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Almost all these studies have assumed that the assignable causes of process disturbance can be identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. In this paper, the EPC and neural network scheme were integrated in identifying the assignable causes of the underlying disturbance. For finding the appropriate setup of the networks' parameters, such as the number of hidden nodes and the suitable input variables, the all-possible-regression selection procedure is applied. For comparison, two SPC charts, Shewhart and cumulative sum (Cusum) charts were also developed for the same data sets. As the results reveal, the proposed approaches outperform the other methods and the shift of disturbance can be identified successfully.

Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network's topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring by integrating the backpropagation neural networks with traditional discriminant analysis approach. To demonstrate the inclusion of the credit scoring result from discriminant analysis would simplify the network structure and improve the credit scoring accuracy of the designed neural network model, credit scoring tasks are performed on one bank credit card data set. As the results reveal, the proposed hybrid approach converges much faster than the conventional neural networks model. Moreover, the credit scoring accuracies increase in terms of the proposed methodology and outperform traditional discriminant analysis and logistic regression approaches.

The study investigates the information content of SGX-DT Nikkei 225 futures prices during the non-cash-trading (NCT) period using an artificial neural network model. The cash market closing index, the futures prices from a period in the same trading day and on the following trading day are utilized to determine the appropriate input nodes of a back propagation neural network model in forecasting the opening cash price index. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate network topology. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. The effectiveness of the method is demonstrated on data from a 6-month historical record (1998-99). Analytic results demonstrate that the proposed neural network model outperforms a neural network model with the previous day's closing index as the input node and the random walk model forecasts. It, therefore, indicates that there is valua...

Nowadays, the capability to both generate and collect data has been expanded enormously and provides us with huge amount of data. Millions of databases have been used in business data management, scientific and engineering data management, and many other applications. It is noted that the number of such databases keeps growing rapidly because of the availability of powerful and affordable database management systems. To compete effectively in such an environment, business managers must take advantage of high-return opportunities in time. But however, the difficulty of discerning the value of information keeps many companies from capitalizing fully on the wealth of data at their disposal. Data mining is the art of finding patterns in data and is a new approach based on a general recognition that there is undraped value in large databases and utilities data-driven extraction of information. However, it is still not easy to identify the complicate relationship in the huge data set. Moreover, in most...

Because of the existence of the autocorrelation in the data series, traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries. Therefore how to reduce the process variability obtained through the use of SPC techniques has been discussed for years in process industries. Techniques are needed to serve the same functions as SPC control charts that are to identify shifts, in correlated parameters. Neural networks are a massively parallel system and are a potential tool that can be used to identify shifts in correlated process parameters. In this research, backpropagation neural networks are developed to identify shifts in process parameter values from papermaking and viscosity data set available in literature. For finding the appropriate number of input nodes to use in a neural network model, the all-possible-regression selection procedure is applied. For comparison, the time series residual control charts are also developed for the dat...

Rapidly evolving accelerated degradation testing (ADT) has provided an efficient channel for reliability assessment without the labor of conventional life testing that observations are complete or incomplete data. In step-stress loading, a specimen is subjected to successively higher level stress until an appropriate termination time. All specimens go through the same specified pattern of stress levels and testing time. A step-stress degradation testing (SSDT) is corresponding to collect the degradation measurements over time from a step-stress life testing before any specimen fail. The advantages of SSDT may shorten the testing time and reduce the test units compared to ADT. Conventional statistical approach for modeling the step-stress degradation process may be complicated. In this paper, we address a nonparametric approach by applying the methodology of neural networks to calibrate the step-stress degradation measurements and conducting the reliability prediction at use condition. A light emitting diode (LED) example is given to illustrate the implementation procedure.

Recently, a great deal of research has focused on integrating statistical process control (SPC) and engineering process control (EPC). Most of these studies have concluded that the integrated use of both SPC and EPC is superior in performance to the use of either alone. However, the majority of these studies have assumed that the assignable causes of disturbance can he identified and removed as soon as the out-of-control signal is triggered by SPC. In practice, the identification of the assignable causes of a disturbance is not so straightforward. Using SPC and EPC control schemes, this study introduces a simple graphical aid technique to display the pattern of the underlying disturbance. Using EPC and a neural network (NN) scheme, this study focuses on the development of another technique to identify the nature of the assignable causes of the underlying disturbance. The effectiveness and the superiority of the proposed approaches are demonstrated through a series of simulations.

Traditional statistical process control (SPC) techniues of control charting are not applicable in many process industries because data from these facilities are autocorrelated. Therefore the reduction in process variability obtained through the use of SPC techniques has not been realized in process industries. Techniques are needed to serve the same function as SPC control charts, that is to identify process shifts, in correlated parameters. Radial basis function neural networks were developed to identify shifts in process parameter values from papermaking and viscosity data sets available in the literature. Time series residual control charts were also developed for the data sets. Networks were successful at separating data that were shifted 1.5 and 2 standard deviations from nonshifted data for both the papermaking and viscosity parameter values. The network developed on the basis of the papermaking data set was also able to separate shifts of 1 standard deviation from nonshifted data. The SPC control charts were not able to identify the same process shifts. The radial basis function neural networks can be used to identify shifts in process parameters, thus allowing improved process control in manufacturing processes that generate correlated process data.

A back-propagation neural network with the output provided by a rule-based expert system is designed for short-term power load forecasting. To demonstrate that the inclusion of the prediction from a rule-based expert system of a power system would improve the predictive capability of the network, load forecasting is performed on the Taiwan power system. The hourly load for one typical day was evaluated and, in that case, the inclusion of the rule-based expert system prediction significantly improved the neural network's prediction of power load. Moreover, the proposed combined approach converges much faster than the conventional neural network and the rule-based expert system method. Extensive studies were performed on the robustness of the built network model by using different specified censoring time. The prediction intervals of future power load series are also provided, to evaluate the prediction efficiency of the neural network model.

A backpropagation neural network that used the output provided by a rule-based expert system was designed for short-term load forecasting. Extensive studies were performed on the effect of various factors such as learning rate and the number of hidden nodes. Load forecasting was performed on a Taiwan power system to demonstrate that the inclusion of the prediction from a rule-based expert system developed for a power system would improve the predictive capability of the neural network. The hourly power load for two typical days was evaluated, and for both days the inclusion of the rule-based expert system prediction as a network input significantly improved the neural network's prediction of power load. The predictive capability of the network was compared to the expert system as well as to a previously developed neural network. The proposed neural network provided improved predictive capability. In addition, the proposed combined approach converges much faster than both the conventional neural network and the rule-based expert system method.

Describes how a statistical Taguchi approach and a backpropagation neural network model were devised to evaluate the effect of various parameters and identify the optimal parameter setup values in a gas‐assisted injection moulding process. In applying the Taguchi approach, an L18 orthogonal array was employed to collect the observations, and the same collected data sets, with two additional inputs, were utilized to construct a neural network model to ascertain whether utilizing such a neural network would provide an improved generalization capability over a statistical method. The effect of the learning rate and the number of hidden nodes on the efficiency of the neural network learning algorithm was extensively studied to identify what provides the best forecasting of performance measure. In addition, to verify the generalization capability of the neural model, eight different parameter setups, which had not been included in the full factorial design, were constructed for network testing. The results revealed that the network is more efficient in identifying the real optimal parameter setup.

The accurate prediction of the values of critical quality parameters of a product during the production stage is a key factor in the success of a manufacturing operation. Neural network algorithms have been used to successfully predict process parameter values. However, techniques to further improve the predictive capability of neural network models are sought. Thus, an analysis was conducted to determine if the predictive capability of the network would he improved if the prediction from a time series model of a manufacturing process parameter were included in the training data set of a radial basis function neural network model. A manufacturing process data set was evaluated, and the use of the time series model prediction significantly improved the neural network's prediction of critical process parameters. Often in a manufacturing environment, the collection of adequate amounts of data for network training is difficult. This integrated technique offers potential for improving network performance witho...

Predicting stock index is a major activity of financial firms and private investors. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, evolutionary, and nonlinear dynamic system. In this study, a stock index prediction model by integrating nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used as preprocessing to extract features from observed stock index data. The features which can be used to represent underlying/hidden information of the original data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. Experimental results on Shanghai Stock Exchange composite (SSEC) closing cash index show that the proposed stock index prediction method is effective and efficient compared to the four comparison models.

In this paper, a two-stage approach of integrating spatiotemporal independent component analysis (stICA) and data envelopment analysis (DEA) is developed for efficiency measurement. stICA is used to search for latent source signals where no relevant signal mixture mechanisms are available; and DEA is used to measure the relative efficiencies of decision making units (DMUs). We suggest using stICA first to extract the input variables for generating independent components (IC), then selecting the ICs representing the independent sources of input variables, and finally inputting the selected ICs as new variables in the DEA model. The training institution dataset provided by the Semiconductor Institute in Taiwan is used for analysis. The result shows that the proposed method can not only separate performance differences between the training institutions but also improve the discriminatory capability of the DEA's efficiency measurement. The study results can serve as a reference for training institutions wishing to enhance their training efficiency.

In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA) is a novel feature extraction technique to find independent sources given only observed data that are mixtures of the unknown sources, without prior knowledge of the mixing mechanisms. It assumes that the observed mixtures are the nonlinear combination of latent source signals. This study propose a stock price forecasting model which first uses NLICA as preprocessing to extract features from forecasting variables. The features, called independent components (ICs), are served as the inputs of support vector regression (SVR) to build the prediction model. Experimental results on Nikkei 225 closing cash index show that the proposed method can produce the best prediction performance compared to the SVR models that use linear ICA, principal component analysis (PCA) and kernel PCA as feature extraction, and the single SVR model without feature extraction.

In this research, a time series prediction model by integrating nonlinear independent component analysis (NLICA) and neural network is proposed for stock price. NLICA is a novel feature extraction technique to find independent sources given only observed data that are mixtures of the unknown sources, without prior knowledge of the mixing mechanisms. It assumes that the observed mixtures are the nonlinear combination of latent source signals. The proposed method first use NLICA as preprocessing to transform the input space composed of original time series data into the feature space consisting of independent components (ICs) representing underlying information/features of the original data. Then, the ICs are served as the input variables of the backpropagation neural network (BPN) to build prediction model. Experimental results on Nikkei 225 closing cash index show that the proposed model outperforms the integrated linear ICA and BPN model and the single BPN model without ICA preprocessing.

Process monitoring and control of a production line are often used in industry to maintain high-quality production and to facilitate high levels of efficiency in the process. However, current process control techniques, such as statistical process control (SPC) and engineering process control (EPC), may not effectively detect abnormalities, especially when autocorrelation is present in the process. This paper proposes an independent component analysis (ICA)-based image reconstruction scheme with a neural network approach to identify disturbances and recognize shifts in the correlated process parameters. The resulting image can effectively remove the textual pattern and preserve disturbances distinctly. We illustrate our approach using two most commonly encountered disturbances, the step-change disturbance and the linear disturbance, in a manufacturing process. The experimental results reveal that the proposed method is effective and efficient for disturbance identification in correlated process parameters when disturbance is significant. Additionally, the identification rate made by the proposed method is slightly influenced by the data correlation.

In this paper, a two-stage approach by integrating independent component analysis (ICA) and data envelopment analysis (DEA) model is proposed for efficiency measurement. Basically, the former aims to search for latent source signals where no relevant signal mixture mechanisms are available; and the latter is a method for measuring the relative efficiencies of decision making units (DMUs). The proposed method firstly uses ICA to the input variables for generating independent components. After selecting the ICs representing the independent sources of input variables, the selected ICs are used as new input variables to DEA model. The experiments from a hospital data have shown that the proposed method can reduce the number of efficient DMUs and improve the discriminatory capability of the efficiency measurement of DEA.

This paper presents a DFT scheme which combines boundary scan with built-in self test for microcontrollers. It focuses on mutual effects between the significant steps in design process and DFT tasks. The steps includes power reduction application in front-end design, discontinuous static timing analysis and clock tree synthesis in back-end design. Our circuit modification for power reduction guarantees the latch's transparence and avoid unacceptable "X" value in logic build-in self test. An improved test point insertion method helps to prevent repetitious logic BIST insertion and static timing analysis. Simple test clock strategy brings out clock tree generation trouble, through change the position of the clock selection logic for DFF who is driven by different clock at different mode, it is resolved perfectly. Based on the experiment on a 450 k gates aerospace application microcontroller, the feasibility of the scheme above is verified.

This chapter investigates the information content of SGX-DT (Singapore Exchange-Derivatives Trading Limited) MSCI (Morgan Stanley Capital International) Taiwan futures contracts and its underlying cash market during the non-cash-trading (NCT) period. Previous day’s cash market closing index and the grey forecasts by using the futures during the NCT period are used to forecast the 09:00 AM opening cash price index by the neural networks model. To demonstrate the effectiveness of our proposed method, the five-minute intraday data of spot and futures index from October 1, 1998 to December 31, 1999 was evaluated using the special neural network modeling. Analytic results demonstrate that the proposed model of integrating grey forecasts and neural networks outperforms the neural network model with previous day’s closing index as the input variable, the random walk model and AR,IMA forecasting. It therefore indicates that there is valuable information involved in the futures prices during the NCT period in forecasting the opening cash price index. Besides, grey forecasts provide a better initial solution that speeds up the learning procedure for the neural networks which in turn give better forecasting results.

A multilayer neural network was designed for time series forecasting using response surface methodology (RSM). To optimize the network's parameters (the number of hidden nodes, the initial learning rate and momentum constant) RSM was employed to explore the mean square error response surface. Extensive studies were performed on the effect of the initial values of connection weights on the accuracy of the backpropagation learning method which was employed in the training of the artificial neural network. The effectiveness of the neural network with the proposed RSM technique is demonstrated with an example of forecasting the number of passengers on an international airline. It was found that with RSM the neural network provided a more accurate prediction of the response. >

The study presents a novel semiparametric prediction system for the Taiwan unemployment rate series. The prediction method incorporated into the system consists of a neural network model that estimates the trend, as well as a Box-Jenkins prediction of the residual series. The response surface methodology is employed to find the appropriate setup of network parameters as the neural network is applied. Also, extensive studies are performed on the robustness of the built network model using different specified censoring strategies. In terms of the adaptability of the Box-Jenkins method, the prediction intervals of the system can be successfully constructed. To demonstrate the effectiveness of our proposed method, the monthly unemployment rate from June 1983 to February 1992 is evaluated using a neural network model with Box-Jenkins technique and other alternative methods, e.g. space-time series analysis, univariate ARIMA model and state space model. Analysis results demonstrate that the proposed method outperforms other statistical methodologies.