Under the trend of globalization, rapid advancement of Information Technology, shorter electronic product life cycles, and the rise of consumer consciousness, there is fiercer market competition in most industries, especially in the semiconductor industry. Effective supply chain management is a critical successful factor for enterprises to survive in such a competitive market since it can help enterprises in reducing cost and improving operational efficiency. For an integrated device manufacturer (IDM) in the semiconductor industry, due to its complex network, strategic alliance among partners as well as supply chain integration are efficient approaches to enhance competitiveness and make more profit. In this paper, we develop an integrated model of supply chain planning for IDMs. The integrated model is developed to help decision makers determine the production quantities and delivery quantities at various locations, the decoupling point of a push-pull supply chain, the inventory buffer at the boundary and the vendor managed inventory scenario. The semiconductor manufacturing characteristics including binning, substitution, and yield are taken into consideration in the formulated model. In addition, the model is developed for a semiconductor supply chain characterized by multiple stages, plants, products, and product grades.

The very short-term load forecasting (VSTLF) problem is of particular interest for use in smart grid and automated demand response applications. An effective solution for VSTLF can facilitate real-time electricity deployment and improve its quality. In this paper, a novel approach to model the very short-term load of individual households based on context information and daily schedule pattern analysis is proposed. Several daily behavior pattern types were obtained by analyzing the time series of daily electricity consumption, and context features from various sources were collected and used to establish a rule set for use in anticipating the likely behavior pattern type of a specific day. Meanwhile, an electricity consumption volume prediction model was developed for each behavior pattern type to predict the load at a specific time point in a day. This study was concerned with solving the VSTLF for individual households in Taiwan. The proposed approach obtained an average mean absolute percentage error (MAPE) of 3.23% and 2.44% for forecasting individual household load and aggregation load 30-min ahead, respectively, which is more favorable than other methods.

Cutting is an essential process in touch panel manufacturing, which concerns the effectiveness of whole touch panel manufacturing process and reliability of final application products. An alternative is laser cutting technology, which is expected to eclipse conventional cutting method in terms of cost and quality. The laser cutting quality of touch panel is determined by the appropriation of value settings of several parameters/factors involved in the complex laser cutting process. However, the parameter/factor adjusting method applied in practice mainly relies on the engineer's experience and trial-and-error experiment, which are unsystematic and susceptible to inefficiency and ineffectiveness. This study proposes a procedure for parameter/factor optimization of the touch panel laser cutting process to obtain maximum cutting quality. The proposed optimization procedure is successfully applied in a real case of laser cutting for projected capacitive touch panel, a type of touch panels using capacitive technology; it gradually becomes mainstream in the newly emerged information appliance marketplace. Results reveal that defect rate of the laser cutting process decreases from 32.6% to 0.3% once the proposed procedure on this paper is implemented, and this achievement outperforms other optimization strategies.

Multiclass Mahalanobis-Taguchi system (MMTS), the extension of MTS, is developed for simultaneous multiclass classification and feature selection. In MMTS, the multiclass measurement scale is constructed by establishing an individual Mahalanobis space for each class. To increase the validity of the measurement scale, the Gram-Schmidt process is performed to mutually orthogonalize the features and eliminate the multicollinearity. The important features are identified using the orthogonal arrays and the signal-to-noise ratio, and are then used to construct a reduced model measurement scale. The contribution of each important feature to classification is also derived according to the effect gain to develop a weighted Mahalanobis distance which is finally used as the distance metric for the classification of MMTS. Using the reduced model measurement scale, an unknown example will be classified into the class with minimum weighted Mahalanobis distance considering only the important features. For evaluating the effectiveness of MMTS, a numerical experiment is implemented, and the results show that MMTS outperforms other well-known algorithms not only on classification accuracy but also on feature selection efficiency. Finally, a real case about gestational diabetes mellitus is studied, and the results indicate the practicality of MMTS in real-world applications.

Under the highly developed automation today, the manufacture of saxophone is still a nonautomatic process and much relies on highly skilled technicians. In order to insure the timbre quality, the sound of finished saxophone must be tested in the final inspection stage. The evaluation of timbre quality mainly depends on the professional musicians' hearing judgment; however, the sensitivity of human perception can be influenced by many factors. To improve the reliability of saxophone timbre quality inspection, an automatic multiclass timbre classification system (AMTCS) is developed and used to assist in the inspection work. The AMTCS is composed of our proposed waveform-shape-based feature extraction method in parameterization phase and multiclass Mahalanobis-Taguchi system in classification phase. The numerical experiments show that the musical instrument classification accuracy obtained by our proposed AMTCS is satisfactory. Through employing the AMTCS, strong assistance was provided to the inspection of saxophone timbre quality, and a perfect identification rate on the saxophones with different timbre quality levels is achieved. Moreover, the significant tones having impact on saxophone timbre quality can also be easily identified by AMTCS.

In classification problems, the class imbalance problem will cause a bias on the training of classifiers and will result in the lower sensitivity of detecting the minority class examples. The Mahalanobis-Taguchi System (MTS) is a diagnostic and forecasting technique for multivariate data. MTS establishes a classifier by constructing a continuous measurement scale rather than directly learning from the training set. Therefore, it is expected that the construction of an MTS model will not be influenced by data distribution, and this property is helpful to overcome the class imbalance problem. To verify the robustness of MTS for imbalanced data, this study compares MTS with several popular classification techniques. The results indicate that MTS is the most robust technique to deal with the classification problem on imbalanced data. In addition, this study develops a "probabilistic thresholding method" to determine the classification threshold for MTS, and it obtains a good performance. Finally, MTS is employed to analyze the radio frequency (RF) inspection process of mobile phone manufacturing. The data collected from the RF inspection process is typically an imbalanced type. Implementation results show that the inspection attributes are significantly reduced and that the RF inspection process can also maintain high inspection accuracy.