Shen-Tsu Wang

Abnormal diagnosis of Emergency Department triage explored with data mining technology: An Emergency Department at a Medical Center in Taiwan taken as an example

Triage helps to classify patients at emergency departments to make the most effective use of resources distributed. What is more important is that accuracy in carrying out triage matters greatly in terms of medical quality, patient satisfaction and life security. As the numbers of patients in emergency departments increase, learning from the examples of abnormal diagnosis of triage in order to make modifications, constitutes a significant issue. The researcher worked with the Emergency Department of a Taiwan Medical Center to build a model to view abnormal diagnoses in the database from the establishment of a flow path and the selection of parameters for sampling. Data on patients were derived from the database. Two-stage cluster analysis (Wards method and K-means) and decision tree analysis were made on 501 abnormal diagnoses in an emergency department. It was found that nursing personnel make more frequent triage diagnoses than physicians do. Most of abnormal diagnoses stems from patients rather than the diagnosis on the day. Pulse and temperature have greater distinction. The researcher proposes seven correlation laws based on confidence and support proportions, derived from sample point conforming to correlation law that abnormal diagnosis is most likely in diseases of pneumonia and cirrhosis, etc. Through data mining technology, the researchers triage expert system is written in simulation. After periodic updates, it can improve the system and education training without the influence of the subjective factor.

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

The light guide plate (LGP) printing process is used to obtain the optimal optical brightness, which requires the optimization of the LGP printing process parameters. This study analyzed the optical brightness optimization parameter design. This study integrated the back-propagation neural network (BPN) and a revised genetic algorithm (GA) to identify the optimal experimental level combination of the optical brightness parameter design. The obtained optical brightness process capability Cpk by the proposed method was 1.73, which was better than the process capability Cpk of 1.65 obtained by using the Taguchi method.

Research on integrating different methods of neural networks with case-based reasoning and rule-based system to infer causes of notebook computer breakdown

Important issues for notebook computer companies include how to ascertain the problems of machines sent by customers, and then assigning those machines to the appropriate department for servicing; and how to maintain breakdown data to save both handling time and costs. However, in practical application, unreliable data decreases the models accuracy, and thus, new methods are brought forward in rapid succession to increase accuracy when inferring causes of notebook computer breakdown. This study integrated several different methods, consisting of a neural network, with case-based reasoning (CBR) and a rule-based system (RBS) to propose a gradual model for inferring causes of notebook computer breakdown. It stressed that the model should have accuracy, elasticity, and transparent interpretability. The model contains three phases: data extracting, group indexing and knowledge creation. Initially, the data extraction phase uses a self-organizing map (SOM) and a revised learning vector quantization network method to reduce isomorphic data to similarity characteristic-based clustering, thus, improving data quality. Then, the group indexing phase establishes a clustering index prediction model based on a back-propagation network (BPN) and genetic algorithm (GA) to increase the efficiency of case selections. Then, the knowledge creation phase uses CBR and RBS to create a notebook computer breakdown case selection model to determine the breakdown cause. Finally, the experimental results show that data purification can actually improve the models accuracy. The CBR with clustering index and rule-based reasoning has a better classification accuracy rate than either the CBR, without the clustering index and rule-based reasoning, or the traditional CBR, in addition, it provides a reference for inferring causes of notebook computer breakdown.

A web-based model for developing

In the age of networking and digitalization, operations of dealing and production process can be accelerated through a World-Wide-Web collaboration, integrating designs and manufacturing management of products and molds. Molding is the most commonly used method of production for modern products, while a mold manufacturer today should connect firmly, through the supply chain system, to all the points, so as to increase service performance for the customers. A web-based sharing system of mold base design and ordering is considerable, being built whereby a customer will get the design with price information quickly and the order shipped more conveniently. This paper provides to the plastic mold industry a developing process of a web-based parametric design system of mold bases. First of all, contents of the related information and data are analyzed and expanded by the method of IDEF0, and are finely structured into design procedure and all sorts of database. A three-tiered mold base design model with distributed database is presented to reduce the expense of software building and the renewal of the system database. The system interface uses maps plus parametric and feature-based database to make the processes of design smoother, and also edition and viewing of the results.

Construction of a 3C Product Mold Manufacturing Process Predictive Optimization Model

Mold trials are critical in the mold development process; therefore, it is necessary to develop predictive models that can control processing results and solve problems in processing parameter optimization to ensure manufacturing efficiency and processing quality. Using the six sigma method, this research constructed an optimized 3C (Computer, Communication, and Consumer electronic) product mold manufacturing process predictive model, and conducted an empirical study on the largest electronic products foundry. The Taguchi parameter design method, the back propagation network (BPN) prediction method, and genetic algorithms (GAs) were used to establish an optimization search module. The surface quality was inferred by the network predictive model as a limiting condition for acquiring the maximized material removal rate in milling. The optimized milling processing parameters of the maximum fitness degree can be determined by GA. The findings can serve as a practical reference for quality improvements and decision planning.

An analysis of TFT-LCD industry success factors by integrating FDAHP and gray sequencing

As Taiwan’s TFT-LCD (thin film transistor liquid crystal display) industry plays a vital global role, discussion of its success factors is importance. Previous studies of the TFT-LCD industry success factors were usually based on personal experience and subjective judgment, and did not provide specifically effective success factors assessing methods. Therefore, it is important to establish a set of TFT-LCD industry success factor evaluation methods. Based on literature review and expert interviews, this study selected and summarized 5 major perspectives, and 18 evaluation indices applicable to the TFT-LCD industry. It also established a qualitative (semantics) and quantitative (real data) integrated evaluation model of TFT-LCD industry success factors and their sequencing, using the FDAHP (fuzzy Delphi analytic hierarchy process) and gray sequencing method, in order to provide reference for the TFT-LCD industry. The result showed the top three key success factors which are innovation and R&D (research and development) capabilities, the industry chain support, and manufacturing equipment upgrades. Finally, the top three success factors are employed in sequence to illustrate managerial implications.

Optimization of a light guide plate injection molding stability process by integrating Taguchi quality engineering and the gray sequencing method

The light guide plate is an essential key component in the development of the panel industry. In pursuit of lighter and thinner computer, communication, consumer (3C) electronics products, the panel industry continuously introduces new types of products to meet contemporary needs. Applications of a thinner light guide plate have become increasingly important. However, in the thin injection molding process, the control of geometric size and surface quality may be difficult to achieve owing to variations in conditions, such as injection pressure and temperature. Under the pressure of pursuing higher injection molding speed and lower cost, the injection industry strives to improve injection process capabilities to achieve higher quality yield, reduced production costs and processing time, and enhanced processing quality to maintain profits. Regarding the control of the Y-axis size of injection molding, this study proposed an experimental design method for injection quality improvement and injection process performance enhancement. The relevant injection parameters included temperature, back pressure, holding (dwell) pressure, and length measurement. The purpose of this study is to identify the best light guiding injection parameters to establish stable injection conditions and improve process capability. By employing the five stages of define, measure, analyze, improve, and control, this study conducted empirical research on the injection molding size stability processes of a photoelectric light guide plate, in a specific injection molding plant in Taiwan, in order to establish a process optimization model for injection molding Y-axis size stability. The research methodology integrated the parameter design method of Taguchi quality engineering and the gray sequencing method to identify the combinations of optimization parameters and experimental levels in injection molding. The experimental results suggested that C pk has been improved from 0.6 to 1.44, with a significant increase in process capability. The prediction result error rate meets accuracy requirements, which can help improve the control of capability, stability, and quality of the light guide plate injection molding size process. The research findings can provide Taiwan’s injection molding precision processing industry with a reference in quality.

An evaluation model for process capability of multiple-process machinery: a case study on the transfer machinery

Abstract The major requirements for products machining are speed, cost and quality, in which quality is the most fundamental demand. Process capability index (PCI) is a powerful tool in measuring the process capability of a product. Since the process capability of machinery can’t be evaluated by directly measuring the machinery, we appraise its process capability by the qualities of the product produced by the machine. Since a machined product is normally tolerance asymmetric and nominal-the-best, we developed a generalized measuring index to represent the process capability of a machine, by which its products can be evaluated for both the process yield and quality loss. First, by considering the relationship between process capability index and process yield, we developed a process capability index of a workstation in a machine tool. Secondly, we integrated the process capability indices of all machining workstations to acquire the process capability index for this machinery. Next, an analytic chart for machining with asymmetric tolerances was developed to help to effectively evaluate the process capability of a machine. Finally, a procedure to perform this evaluation model was proposed, and a case study of a transfer machine is given to show the feasibility of this evaluation model.

A decision support system for supply parts planning of operation with uncertainty of supply and demand in the notebook computer industry

This study explores multi-plant manufacturing problems in their key supply parts planning, and proposes a complete programming model. Furthermore, since multi-plants face product uncertainty demand in the market as well as uncertainty of supply part quantities from suppliers, manufacturers decision-making operations will be studied. If the supply and demand data are sufficient, frequency of demand and quantities of demand will be used to conform the Poisson and Normal distribution; otherwise, if the supply and demand data are insufficient, triangular fuzzy number of function will be used to estimate t e uncertainty probability function. Its expected that in uncertain circumstances of supply and demand, the manufacturer will be able to make an appropriate adjustment of production level, and make a procurement decision that will lead to the greatest profit in a single period. The decision support system of application will be illustrated with the notebook computer industry taken as the focus of the study

Corrigendum: Corrigendum Planning of educational training courses by data mining: Using China Motor Corporation as an example [Expert Systems with Applications] 36 (3P2) (2009) 7199-7209

Corrigendum ‘‘Planning of educational training courses by data mining: Using China Motor Corporation as an example” [Expert Systems with Applications] 36 (3P2) (2009) 7199–7209 Chiao-Tzu Huang , Wen-Tsann Lin , Shen-Tsu Wang *, Wen-Shan Wang a a Industrial Engineering and Management Department, National Chin-Yi University of Technology, Taiwan, ROC b Industrial Engineering and Engineering Management Department, National Tsing-Hau University, Taiwan, ROC c Department of Transportation and Logistics, TOKO University, Taiwan, ROC