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The aim of this study is to evaluate maternal and neonatal outcomes following immediate delivery or expectant management of preterm premature rupture of membranes (PPROM) during the late preterm period at 34+ 0–36+ 6 weeks of pregnancy. We conducted a retrospective study on singleton pregnancies with PPROM during the late preterm period using medical records at twelve tertiary medical centres in Korea from January 2007 to December 2016. Data on demographic characteristics and outcome measures were collected. The primary outcomes were maternal sepsis for maternal outcome and neonatal sepsis and neonatal death for neonatal outcomes. Of the 1,072 women, 782 cases (72.9%) were assigned to the immediate delivery group, and 290 cases (27.1%) were categorized into the expectant management group. There was a significant difference in the rate of clinical neonatal sepsis (immediate delivery, 3.8% vs expectant management, 15.8%; p

A 36-year-old primipara woman was referred to the interventional department for management of severe primary post-partum hemorrhage with a vaginal laceration after a normal vaginal delivery. Angiography revealed that the superior rectal branch of the inferior mesenteric artery was one of the origins of persistent vaginal bleeding following embolization of both uterine arteries. The patient was successfully treated by selective embolization without other complications. Our case report highlights that the inferior mesenteric artery should be considered as an unusual extrauterine source for post-partum hemorrhage.

This study was conducted to monitor the current status of pesticide residues and to calculate the correlation between monthly price fluctuation and distribution of pesticide residues in leafy vegetables at the whole market in Gyeonggi-do from 2009 to 2013. From the result, the maximum residue limit (MRL) were frequently exceeded in 5 leafy vegetables which were 48 cases of crown daisy (5.8%), 28 cases of chard (5.1%), 28 cases of spinach (3.4%), 25 cases of perilla leaves (3.4%) and 24 cases of chamnamul (3.7%). The high rate of monthly pesti- cide residues exceeding MRL showed on crown daisy and chard observed in June and September, spinach in October to November and December to January, perilla leaves in July to August and January, and chamnamul in August to September. To calculate the correlation between monthly price fluctuation and distribution of pesticide residues, we performed the Pearson's correlation analysis. From the results, there were significant differences between monthly price and pesticide residues detected in chamnamul, spinach and perilla leaves ( p< 0.05). There were also significant differences between monthly price and pesticide residues exceeding MRL in chamnamul and crown daisy ( p< 0.05). Although the small number of leafy vegetables and short period were analyzed in this study, the consolidation monitor of pesticide residues according to the season and price will be needed to acquire the safety of agricultural products.

Bacillus cereus contamination is a major food safety problem for Korean fermented soybean products, but few studies have assessed its potential to cause foodborne illness. The objectives of this study were to investigate the prevalence and characteristics of B. cereus isolated from Korean fermented soybean products. B. cereus was detected in 110 of 162 (67.9%) samples. The highest B. cereus frequency was observed in deonjang (68 of 93 samples, 73.1%) and cheonggukjang (18 of 25, 72.0%); however, nonhemolytic enterotoxin was detected only in 22 of 162 samples (13.6%). Although the tested B. cereus isolates showed diverse pulsotypes according to repetitive sequence-PCR banding patterns, they displayed similar antibiotic sensitivity spectra. The low frequency of enterotoxin detection suggests that the potential risk of B. cereus foodborne illness associated with Korean fermented soybean products is lower than generally presumed. However, considering the prevalence of B. cereus and the high content of fermented soybean products in the Korean diet, it is necessary to constantly monitor the level of contamination with B. cereus and its toxins in such Korean food products.

Surveys on the consumption of caffeinated beverages by high school students (n=886) were per- formed. Of the students, 97.0% consumed a variety of caffeinated beverages, including carbonated drinks (90.0%), processed milk and cocoa (79.0%), coffee (63.0), teas (52.1), energy drinks (16.4%) and nourishment drinks (15.5%). The frequency of intake per student was 8.2 times per week. Caffeine intake through the caffeinated beverages was 41.53 mg/day, which was accounted for by coffee (51.5%), carbonated drinks (19.6%), processed milk and cocoa (11.5%), teas (11.4%), energy drinks (5.0%) and nourishment drinks (1.1%). Students with high levels of stress, those who consumed snacks twice a day, and those who used a computer (or smart phone) for more than 3 hours per day showed significantly higher caffeine intake. The groups with high caffeine intake experienced heart palpitations, insomnia and pollakiuria. Students indicated that they consumed the caffeinated beverages for the taste (57.9%), wak- ing up (18.0%), thirst (13.2%), etc. (10.9%). They tended to consume drinks with a high content of caffeine to sleep less. In addition, they rarely checked the label, and showed a lack of awareness of the caffeine contents in the bever- ages, which calls for education.

Branches of the internal iliac artery or ovarian artery are the typical sources of pelvic hemorrhage. The inferior mesenteric artery has been rarely reported as the origin of pelvic bleeding. We present 2 cases of intractable intrapelvic and vaginal bleeding after hysterectomy. One patient underwent a hysterectomy because of uncontrolled postpartum hemorrhage and another underwent a vaginal hysterectomy to treat vaginal prolapse. Both patients were subjected to angiography to control continuous vaginal bleeding after hysterectomy. The angiography revealed that the bleeding originated from the inferior mesenteric artery. Selective embolization of the inferior mesenteric artery successfully controlled the intractable intrapelvic and vaginal bleeding without complications. The inferior mesenteric artery is a potential source of intractable intrapelvic and vaginal bleeding for patients with a lower genital tract injury.

In this paper, we propose a novel approach to real-time business process monitoring for prediction of abnormal termination. Existing real-time monitoring approaches are difficult to use proactively, owing to unobserved data from gradual process executions. To improve the utility and effectiveness of real-time monitoring, we derived a KNNI (k nearest neighbor imputation)-based LOF (local outlier factor) prediction algorithm. In each monitoring period of an ongoing process instance, the proposed algorithm estimates the distribution of LOF values and the probability of abnormal termination when the ongoing instance is terminated, which estimations are conducted periodically over entire periods. Thereby, we can probabilistically predict outcomes based on the current progress. In experiments conducted with an example scenario, we showed that the proposed predictors can reflect real-time progress and provide opportunities for proactive prevention of abnormal termination by means of an early alarm. With the proposed method, abnormal termination of an ongoing instance can be predicted, before its actual occurrence, enabling process managers to obtain insights into real-time progress and undertake proactive prevention of probable risks, rather than merely reactive correction of risk eventualities.

Due to the rapid changes in circumstances of cooperates such as globalization, technical innovation and competition, inter-dependence among cooperates which compose supply chain has been intensified. This make cooperates be exposed to various risk and even a small uncertainty can disrupt the balance of whole supply chain. Therefore, in this paper, the framework to develop alternative backorder replenishment plan to minimize the total replenishment cost and expected risk cost has been devised. In order to model the relationship between risks and risk propagation, Bayesian Belief Network has been applied. Moreover, with the fast heuristic algorithm, breath first search and elementary stepwise system based reverse Dijkstra, the alternative backorder replenishment plan can be established. The numerical example shows how to apply the proposed framework and make dynamic backorder replenishment plan considering impact of risk.

This study was conducted to evaluate the microbiological hazard on nursery school children's hands and to investigate the reduction effect of hand washing education. A total of 59 nursery school children's hands were tested. The average number of total aerobic bacteria was 3.72 ± 0.38 log CFU/hand. Five children's hands(2 male and 3 female) were positive(14.3%) for the coliform bacteria. These results showed that hand washing education are required repetitively. Among the pathogenic bacteria tested in this study, Staphylococcus aureus and Bacillus cereus were detected in 9(25.7%) and 16(45.7%) out of 35 their hands, respectively. Twelve out of sixteen B. cereus isolates(70.0%) produced enterotoxin. The results indicate that the hand hygiene of nursery school children needs to be improved. Comparing before and after hand washing in educated and non-educated group, the reduction effect of total aerobic bacteria on their hands was 0.42 and 0.60 log CFU/hand, respectively. The educated group showed 0.18 log CFU/hand higher reduction effect than non-educated group but microorganism did not eliminate perfectly. From the results, using a hand sanitizer after washing with soap and the continuous hand washing education are required to control the contaminated bacteria on nursery school children's hands.

Order allocation is one of the most important decision-making problems of firms having significant influences on performances of themselves and the whole supply chain. Existing researches about order allocation have mainly focused on evaluating capabilities of directly connected suppliers so that it is hard to consider effects and interactions from undirected connections over multiple lower-layers. To alleviate the limitation, this paper proposed a novel approach to order allocation using structural hole. By applying the concept of structural hole to the supply network, we could evaluate the structural supplying powers of firms with respect to both of direct and indirect connections. In the proposed approach, we derived a methodology to measure the potential supplying power of each firm by modifying the effective size as one of the measurements of structural hole and then, proposed its application, the structural hole based order allocation strategy. Furthermore, we conducted the agent based modeling of supply chain to perform the decision-making process of order allocation and simulated the proposed strategy. As a results, by coping with the variance of demand more stably, it could improve the performance of supply chain from the aspects of fill rate, inventory level and demand-supply balance.

PurposeThe purpose of this paper is to propose a novel risk assessment approach that considers the inter‐relationship between supply chain risks and the structure of network at the same time. To reduce the impact of the supply chain risk and enhance the flexibility of transportation route finding during the product delivery, the authors propose a way to model the risk propagation and how to integrate it with the supply chain network using Bayesian Belief Network (BBN). The key risk indicators (KRI) of each vertex and edge of the supply chain network which are measured or computed by the proposed approach can be utilized to develop the optimal transportation route in the execution phase.Design/methodology/approachBBN is utilized to illustrate the relations among supply chain risks which may take place in a certain vertex. To apply the BBN to the supply chain network, the authors develop the framework to integrate BBN and the supply chain network by using the general functions that describe the characteristics of the risk factors and inter‐relationships between vertices.FindingsBy using the proposed risk assessment and dynamic route‐finding approach, it is possible to reduce the unexpected cost from the supply chain risk and overcome the limitations of previous risk management strategies which focus on developing counter plans and assume the independency of supply chain risks.Practical implicationsThe proposed approach describes how to develop KRI‐BBN to model the risk propagation and to integrate the KRI‐BBN and supply chain network. The KRIs directly measured or computed by KRI‐BBN in real time can be utilized to alternate supply chain execution plans such as inventory management, demand management and product flow management. Transportation problem considering risk is developed to show how to apply the proposed approach and numerical experiments are conducted to prove the cost effectiveness.Originality/valueThe contribution of this paper lies in the way of developing KRI‐BBN to assess the supply chain risk and modelling of the risk propagation by integrating KRI‐BBN with supply chain network. With the proposed risk assessment approach, it is able to alternate the transportation route to minimize the unexpected cost and transportation cost simultaneously.

PurposeThe purpose of this paper is to provide industrial managers with insight into the real‐time progress of running processes. The authors formulated a periodic performance prediction algorithm for use in a proposed novel approach to real‐time business process monitoring.Design/methodology/approachIn the course of process executions, the final performance is predicted probabilistically based on partial information. Imputation method is used to generate probable progresses of ongoing process and Support Vector Machine classifies the performances of them. These procedures are periodically iterated along with the real‐time progress in order to describe the ongoing status.FindingsThe proposed approach can describe the ongoing status as the probability that the process will be executed continually and terminated as the identical result. Furthermore, before the actual occurrence, a proactive warning can be provided for implicit notification of eventualities if the probability of occurrence of the given outcome exceeds the threshold.Research limitations/implicationsThe performance of the proactive warning strategy was evaluated only for accuracy and proactiveness. However, the process will be improved by additionally considering opportunity costs and benefits from actual termination types and their warning errors.Originality/valueWhereas the conventional monitoring approaches only classify the already occurred result of a terminated instance deterministically, the proposed approach predicts the possible results of an ongoing instance probabilistically over entire monitoring periods. As such, the proposed approach can provide the real‐time indicator describing the current capability of ongoing process.

The paper discussed on the managerial and research implications of sustainable supply chain management. We analyzed current researches about the sustainability and its introduction into the supply chain. And then, based on the necessity and requirements of sustainable supply chain management, we argued about what kind of strategies the company should conduct to obtain the sustainability in its supply chain. Finally, from the viewpoint of the company having the not-sustainable supply chain, we established the framework for strategy development to construct the sustainable supply chain, which could help the company in making a practical application, as well as could be suggested as research directions of future works to support it.

Density-based outlier detection identifies an outlying observation with reference to the density of the surrounding space. In spite of the several advantages of density-based outlier detections, its computational complexity remains one of the major barriers to its application. The purpose of the present study is to reduce the computation time of LOF (Local Outlier Factor), a density-based outlier detection algorithm. The proposed method incorporates kd-tree indexing and an approximated k-nearest neighbors search algorithm (ANN). Theoretical analysis on the approximation of nearest neighbor search was conducted. A set of experiments was conducted to examine the performance of the proposed algorithm. The results show that the method can effectively detect local outliers in a reduced computation time.

Imported Food Analysis Division, Gyeongin Regional KFDAAbstract Excess sugar intake by food consumption may contribute to the development of diabetes, dental caries, attentiondeficit hyperactivity disorder (ADHD), and cardiovascular disease. The objective of this study was to investigate the sugarintake from meals at nursery schools in Gyeonggi-Do, and to construct a database for reduction policies sugar intake. Atotal of 601 samples were analyzed for sugar intake in summer and winter, respectively. The average sugar intake fromfood service was 2.22 g/meal, which was 5% of the Recommended Daily Intake (RDI). Furthermore, the analysis resultsof sugar content were in the decreasing order of fruits (5.05 g/100 g), fried food (2.92 g/100 g), and braised food (2.31 g/100 g). The habit of excessive sugar intake in childhood can be threaten health in adulthood. Thus, it is necessary fornursery school children to consume schoolmeals that contain less sugar.Keywords: sugar intake, nursery school, meal, weighed food record method

To investigate the clinical characteristics and risk factors for miscarriage of intrauterine normal pregnancy of patients with a heterotopic pregnancy (HP) after treatment. This was a retrospective study of medical records from CHA Bundang Medical Center. Sixty-four patients who were diagnosed with a HP between February 2006 and July 2017 were included in this study. All analyses were performed using SAS software, version 9.4 (SAS Institute, Inc., Cary, NC). P values

Because weapon systems are perceived as crucial in determining the outcome of a war, selecting weapon systems is a critical task for nations. Just as with other forms of decision analysis involving multiple criteria, selecting a weapon system poses complex, unstructured problems with a huge number of points that must be considered. Some defense analysts have committed themselves to developing efficient methodologies to solve weapon systems selection problems for the Republic of Korea's (ROK) Armed Forces. In the present study, we propose a hybrid approach for weapon systems selection that combines analytic hierarchy process (AHP) and principal component analysis (PCA) to determine the weights to assign to the factors that go into these selection decisions. These weights are inputted into a goal programming (GP) model to determine the best alternative among the weapon systems. The proposed hybrid approach that combines AHP, PCA and GP process components offsets the shortcomings posed by obscurity and arbitrariness in AHP and therefore can provide decision makers with more reasonable and realistic decision criteria than AHP alone. A case study on weapon system selection for the air force demonstrates the usefulness and effectiveness of the proposed hybrid AHP-PCA-GP approach.

The objective of our study was to evaluate the feasibility and usefulness of the Bayesian classifier for predicting malignant renal cysts on MDCT.Ninety-three complicated cysts with pathologic confirmation were enrolled. Patient age and sex and seven morphologic features of the cysts including the maximum diameter, wall features, wall thickness, septa features, measurable enhancement of the wall and septa, presence of calcification, and presence of an enhancing soft-tissue component were used to train the Bayesian classifier. Four radiologists independently reviewed the MDCT images, and the probability of malignancy in each cyst was rated by the radiologists and the Bayesian classifier. The diagnostic performances of the radiologists' visual decisions and the Bayesian classifier were then compared using receiver operating characteristic (ROC) curve analysis. The sensitivity and specificity were also compared between the visual decisions and the Bayesian classifier.The area under the ROC curve for predicting malignant renal cysts by the Bayesian classifier was greater than the visual decisions of three readers (reader 1, p = 0.02; reader 2, p0.01; reader 4, p = 0.02) and was similar to the visual decision of one reader (reader 3, p = 0.51). The specificity for predicting malignant renal cysts was greater by the Bayesian classifier than by the visual decisions in readers 2 (p = 0.04) and 4 (p = 0.02) and was similar in readers 1 (p = 0.68) and 3 (p = 1.00). In terms of sensitivity, there was no significant difference between the Bayesian classifier and the visual decisions in all four readers (p0.05).For predicting malignant renal cysts on MDCT, the Bayesian classifier is feasible and may improve diagnostic performance.

Machine classifiers have been used to automate quantitative analysis and avoid intra-inter-reader variability in previous studies. The selection of an appropriate classification scheme is important for improving performance based on the characteristics of the data set. This paper investigated the performance of several machine classifiers for differentiating obstructive lung diseases using texture analysis on various ROI (region of interest) sizes. 265 high-resolution computerized tomography (HRCT) images were taken from 92 subjects. On each image, two experienced radiologists selected ROIs with various sizes representing area of severe centrilobular emphysema (PLE, n=63), mild centrilobular emphysema (CLE, n=65), bronchiolitis obliterans (BO, n=70) or normal lung (NL, n=67). Four machine classifiers were implemented: naive Bayesian classifier, Bayesian classifier, ANN (artificial neural net) and SVM (support vector machine). For a testing method, 5-fold cross-validation methods were used and each validation was repeated 20 times. The SVM had the best performance in overall accuracy (in ROI size of 32x32 and 64x64) (t-test, p

The motivation is to introduce new shape features and optimize the classifier to improve performance of differentiating obstructive lung diseases, based on high-resolution computerized tomography (HRCT) images. Two hundred sixty-five HRCT images from 82 subjects were selected. On each image, two experienced radiologists selected regions of interest (ROIs) representing area of severe centrilobular emphysema, mild centrilobular emphysema, bronchiolitis obliterans, or normal lung. Besides 13 textural features, additional 11 shape features were employed to evaluate the contribution of shape features. To optimize the system, various ROI size (16 × 16, 32 × 32, and 64 × 64 pixels) and other classifier parameters were tested. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess cross-validation of the system, a five-folding method was used. In the comparison of methods employing only the textural features, adding shape features yielded the significant improvement of overall sensitivity (7.3%, 6.1%, and 4.1% in the Bayesian and 9.1%, 7.5%, and 6.4% in the SVM, in the ROI size 16 × 16, 32 × 32, 64 × 64 pixels, respectively; t test, P

Objective To assess the influence of variable factors such as the size of the airway and the CT imaging parameters such as the reconstruction kernel, field-of-view (FOV), and slice thickness on the automatic measurement of airway dimension. Materials and Methods An airway phantom was fabricated that contained eleven poly-acryl tubes of various lumen diameters and wall thicknesses. The measured density of the poly-acryl wall was 150 HU, and the measured density of the airspace filled with polyurethane foam was -900 HU. CT images were obtained using a 16-MDCT (multidetector CT) scanner and were reconstructed with various reconstruction kernels, thicknesses and FOV. The luminal radius and wall thickness were measured using in-house software based on the full-width-half-maximum method. The measured values as determined by CT and the actual dimensions of the tubes were compared. Results Measurements were most accurate on images reconstructed with use of a standard kernel (mean error: -0.03 ± 0.21 mm for wall thickness and -0.12 ± 0.11 mm for the luminal radius). There was no significant difference in accuracy among images with the use of variable slice thicknesses or a variable FOV. Below a 1-mm threshold, the measurement failed to represent the change of the real dimensions. Conclusion Measurement accuracy was strongly influenced by the specific reconstruction kernel utilized. For accurate measurement, standardization of the imaging protocol and selection of the appropriate anatomic level are essential.

E-business automates the overall activities of a company and allows distributed systems to communicate their information. Process automation and information sharing improve a company's productivity and responsiveness, while Business-to-Business (B2B) workflow systems electronically aid the progress of business processes among trading partners, and also help companies to monitor and administrate their process execution. This study analyzes the technologies and standards for B2B workflow integration, and extracts a reference model for B2B workflow integration. Based on the reference model, three approaches to B2B workflow integration will be introduced here. In addition, we have developed a prototype system of one approach and have illustrated an example for B2B workflow integration. The result of our research can help business partners understand the workflow standards and the messaging technologies for B2B workflow integration, and also help them understand how to implement workflow integration systems that are appropriate to their e-business environments.

BPMS (Business Process Management Systems) is a revolutionary information system that supports designing, administrating, and improving the business processes systematically. BPMS enables the execution of business processes by assigning tasks to human or computer agents according to the predefined definitions of the processes. In this paper, we devise process execution rules using individual worklists and propose task assignment algorithms to maximize the overall process efficiency under the limitation of the agent's capacity. We first transform the business processes into queueing network models, in which the agents are considered as servers. With this step complete, workloads of agents are calculated from server utilization and the task assignment policy can be determined by balancing the workloads. This will serve to minimize the workloads of all agents, thus achieving overall process efficiency. Another application of these results can be capacity planning of agents in advance and business process optimization in the reengineering context. The simulation results and comparisons with other well-known dispatching policies with the shared worklists show the effectiveness of our algorithm.

This paper considers an assembly system where a firm produces a single product which is assembled using two types of components (component 1 and component 2). The components are provided by individual suppliers (supplier 1 and supplier 2). We assume that the firm makes different procurement contracts with supplier 1 and supplier 2. To supplier 1, the firm specifies the maximum inventory level of component 1 and makes a commitment to purchase the component as long as its inventory level is below this target level. To supplier 2, the firm has the option of purchasing or rejecting component 2 at each instant supplier 2 provides it. Formulating our model as a Markov decision problem, we identify a component 2 purchasing policy which maximizes the firm’s profits subject to the costs of rejecting component 1, holding component 2, and purchasing component 2. We also investigate how the changes in the sales price and cost parameters affect the optimal purchasing policy. Finally, we present numerical study for the optimal performance evaluation.

Changes in recent business environments have created the necessity for a more efficient and effective business process management. The workflow management system is software that assists in defining business processes as well as automatically controlling the execution of the processes. We propose a new approach to the automatic execution of business processes using event-condition-action (ECA) rules that can be automatically triggered by an active database. First of all, we propose the concept of blocks that can classify process flows into several patterns. A block is a minimal unit that can specify the behaviors represented in a process model. An algorithm is developed to detect blocks from a process definition network and transform it into a hierarchical tree model. The behaviors in each block type are modeled using ACTA formalism. This provides a theoretical basis from which ECA rules are identified. The proposed ECA rule-based approach shows that it is possible to execute the workflow using the active capability of database without users' intervention. The operation of the proposed methods is illustrated through an example process.

We describe a proposed methodology for business process choreography. We focus on two types of business processes (contract and executable) and provide an interface protocol to represent interoperability patterns between them. The approach is designed to let existing processes, usually managed by an enterprise's own internal workflow management system, collaborate.

We have considered the customization of monitoring services in Workflow Management System (WFMS). WFMS is a software tool to manage business processes effectively and efficiently. An important function of WFMS is monitoring, and WFMS usually involves many monitoring users having different responsibilities, roles, and preferences. The purpose of this paper is to develop a framework of customization for workflow monitoring that can produce a monitoring environment fitted to each user’s requirements. We have identified four components in customizing the workflow monitoring: data objects, analysis methods, presentation styles, and audit events. We have also developed a customization model, called monitoring template, to combine the components. A participant can specify each of the components on a template based on the participant’s own preference, which will then be used to provide workflow monitoring services that are automatically customized for the user. A prototype system has been implemented on top of a commercial WFMS to demonstrate the effectiveness of the customizable workflow monitoring.

A key to successful deployment of concurrent engineering is sharing of product data. We propose a new approach to sharing the product data on the Web. In a concurrent engineering environment, each participant who is as disparate as design, production, and purchase requires a different view depending on their roles and interests. Therefore, it is very important to support the individuals' data requirements. Our system supports views for custom-defined functionalities as well as predefined standardized views. The system allows individual users to design their own views. An important consideration is given to improve the system performance by maintaining indexes.

The capability of the timely provision of maintenance services and service parts is critical to the competitiveness of industrial systems. To enhance the timely operations in a product-support logistics chain, business partners (equipment manufacturers, parts distributors, customers) may have to collaborate for the efficient exchange of relevant information. We propose the architecture of an agent-based product-support logistics system. Emphasis is placed on the problems of sharing and exchanging information through agent communication. We adopt the Resource Description Framework (RDF) schema for information modelling in product-support logistics domain. The eXtensible Markup Language (XML) serialization generates messages for agent communication. The use of XML and RDF enables software agents to understand the contents of messages correctly and consistently. We demonstrate the feasibility of our agent architecture using a scenario in logistical support processes. We believe that the approach can provide ...

Efficient management of product information is critical to the enhancement of corporate competitiveness. This paper explains the design and development of a distributed, open, and intelligent product data management system. First, the requirements for Product Data Management (PDM) systems are summarized and then explained using a design scenario. Since product data are often managed in distributed computing environments, Common Object Request Broker Architecture (CORBA) is employed to ensure interoperability among distributed objects. Standard formats of data and knowledge, such as STandard for Exchange of Product model data (STEP) and Knowledge Query and Manipulation Language (KQML), are used to achieve the openness. A workflow management system has been developed that provides integrated management of task processes and information flows. While designing the system, the main emphasis is placed on dynamic process adaptation which is demanded by product development processes. Use of intelligent agents is ...

Runtime encapsulation of nested process models supports flexible workflow management in a distributed heterogeneous environment. Through its modular design, the WW-flow system provides a hierarchical control scheme over complex processes and subprocesses. Implemented in Java, the workflow engine and client interfaces are portable and platform-independent.

Although Concurrent Engineering can offer substantial benefits, and hence many companies take a strong interest in the col laborative approach, it is not yet clear how it can best be implemented, particularly for a geographically distributed Concurrent Engineering team that may be using a disparate range of computer systems. The recent rise in the number connected to the Internet would offer the possibility of using Internet standards to allow for collaboration over the Internet Central to the use of Internet standards for Concurrent En gineering is the key area of transmitting and viewing CAD and engineering information, and of communication between team members. This paper is concerned with addressing this issue and describes how 3D CAD files can be viewed, and engineering information exchanged, by geographically distributed team members in an interactive manner using Internet standards. In particular this paper is concerned with the is sue of storing STEP data so that it can be retrieved efficiently, how this data can be converted from STEP data to the Virtual Reality Model ing Language (VRML) to allow the product to be viewed in interactive 3-D on a wide number of platforms using the Internet, and is con cerned with the issue of how team members can markup the VRML worlds, with other team members being able to examine the markup comments of others. Formalisms for storing STEP data in an Object-Onented database schema, and for converting STEP data to VRML are described. An overview of the implementation of these formalisms, in a system called CyberView, is given.

The job shop scheduling problem has been a major target for many researchers. Unfortunately, though, most of the past studies assumed that a job consists of only a single part. If we assume that a job consists of a batch as in many real manufacturing environment, then we can obtain an improved schedule. However, then, the size of the scheduling problem would become too large to be solved in practical time limit. So, we proposed an algorithm to get an improved schedule by splitting the original batch into smaller batches, and thereby can meet the due date requirement, and adapt to unexpected dynamic events such as machine failure, rush order and expediting.

A typical product development team working in global manufacturing and marketing environment is often dispersed all over the world. Such a team may need a tool to aid effective communication and cooperation among the work groups. A 3 dimensional STEP geometry browsing system has been developed on the Internet and its associated Web. A major issue in implementing the system is the efficiency of computation and communication time of 3D visualization models. An adaptive subdivision method is developed to convert STEP data into a VRML visualization model. The method has been implemented and experimented, and the results show a substantial improvement in computational performance.

The behavior of concentrated coal-water mixtures having narrow particle size ssfractions of coal was investigated. The pulverized coal was fractionated into six distinct particle size ranges, i.e. -70+80, -80+120, -120+140,-140+200, -200+400 and -400 mesh sizes by using a series of sieves. Settling rates were determined as functions of solids concentration for suspensions in water of coal particles to establish the measurement of particle size and shape factor and to assess concentration effect upon the observed hindered settling rates. The settling rates were modelled using the Richardson-Zaki model with the exponent n variable to account for the nonspherical shape of the coal particles. The data was also correlated with the Michaels-Bolger model which explicitly account for the excess water which is dragged down along with the particles undergoing sedimentation. In addition, coal particles and suspensions were characterized by coal analysis, heating value, solid heat capacity and thermal conductivity, densities, maximum packing concentrations and pore size distributions.

This paper is about the development of an operation software for a real FMS. The developed FMS software includes several modules: production planning module, shop floor scheduling module, operation control module, and manufacturing database module. These software modules, at shop floor level, are integrated with a plant management system, which acts as a test-bed to build a computer integrated manufacturing system (CIMS) in the future. This paper shows an imroved approach to the recently developed distributed scheduling and control scheme. To implement the scheme, a cell controller structure, protocols for scheduling and control and decision making procedures are designed. The scheme proposed in this paper is the second version of an operation software that was used to run the FMS. The revised software seems to work more efficiently and reliably, and the result of this study is currently used for operating the ASRI FMS installed in the Automation and Systems Research Institute (ASRI) at Seoul National University. To show the efficiency of the proposed scheme, a comparison study with other schemes is also included.

We propose the use of a context-sensitive support vector machine (csSVM) to enhance the performance of a conventional support vector machine (SVM) for identifying diffuse interstitial lung disease (DILD) in high-resolution computerized tomography (HRCT) images. Nine hundred rectangular regions of interest (ROIs), each 20 × 20 pixels in size and consisting of 150 ROIs representing six regional disease patterns (normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation), were marked by two experienced radiologists using consensus HRCT images of various DILD. Twenty-one textual and shape features were evaluated to characterize the ROIs. The csSVM classified an ROI by simultaneously using the decision value of each class and information from the neighboring ROIs, such as neighboring region feature distances and class differences. Sequential forward-selection was used to select the relevant features. To validate our results, we used 900 ROIs with fivefold cross-validation and 84 whole lung images categorized by a radiologist. The accuracy of the proposed method for ROI and whole lung classification (89.88 ± 0.02%, and 60.30 ± 13.95%, respectively) was significantly higher than that provided by the conventional SVM classifier (87.39 ± 0.02%, and 57.69 ± 13.31%, respectively; paired t test, p

Process monitoring in manufacturing field such as chemistry and pharmacy is valuable to reduce the risk of the accidents and to improve the efficiency of the process. Due to the increased size and complicated technology of the modern plants, computer-aided process monitoring has been introduced. Nevertheless, the existing methods for computer-aided process monitoring such as Multivariate Statistical Process Control (MSPC) based on Principal Component Analysis (PCA) or Independent Component Analysis (ICA) present several limits in accurate fault detection of process. To overcome these limits, we introduce a novel online computer-aided process monitoring scheme based on ICA with Local Outlier Factor (LOF). The proposed scheme adopts LOF as the monitoring statistic. By utilizing LOF, the limits of existing related works using MSPC were successfully resolved. The scheme was tested by widely used benchmark dataset of Tennessee Eastman process. The results showed the effectiveness of the proposed scheme.

Due to the rapid changes in circumstances of cooperates such as globalization, technical innovation and competition, inter-dependence among cooperates which compose supply chain has been intensified. This make cooperates be exposed to various risk and even a small uncertainty can disrupt the balance of whole supply chain. Therefore, in this paper, the framework to develop alternative execution plan to minimize the total transportation cost and expected cost from risk has been devised. In order to model the relationship between risk factors and risk propagation effect, Bayesian Belief Network (BBN) has been applied. Moreover, with the simple and fast heuristic algorithm, Dijkstra, the alternative transportation plan can be found. The result of numerical experiment shows the cost effectiveness of the proposed framework

Objective: This study was designed to develop an automated system for quantification of various regional disease patterns of diffuse lung diseases as depicted on high-resolution computed tomography (HRCT) and to compare the performance of the automated system with human readers. Materials and Methods: A total of 600 circular regions-of-interest (ROIs), 10 pixels in diameter, were utilized. The 600 ROIs comprised 100 ROIs that represented six typical regional patterns (normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). The ROIs were used to train the automated classification system based on the use of a Support Vector Machine classifier and 37 features of texture and shape. The performance of the classification system was tested with a 5-fold cross-validation method. An automated quantification system was developed with a moving ROI in the lung area, which helped classify each pixel into six categories. A total of 92 HRCT images obtained from patients with different diseases were used to validate the quantification system. Two radiologists independently classified lung areas of the same CT images into six patterns using the manual drawing function of dedicated software. Agreement between the automated system and the readers and between the two individual readers was assessed. Results: The overall accuracy of the system to classify each disease pattern based on the typical ROIs was 89%. When the quantification results were examined, the average agreement between the system and each radiologist was 52% and 49%, respectively. The agreement between the two radiologists was 67%. Conclusion: An automated quantification system for various regional patterns of diffuse interstitial lung diseases can be used for objective and reproducible assessment of disease severity.

This paper proposes a real-time process quality control methodology for Business Activity Monitoring by estimating the risk level (RL) and its confidence intervals (CIs) of an ongoing process. We formulate algorithms to estimate RL and CIs as a continuous measure, based on the partial information of the ongoing process. By using the proposed method, RL and CIs can be estimated in real-time during process execution, so as to effectively control the process quality. Furthermore, it is feasible to generate early warnings based on the status of RL and CIs. We also provided experiments to compare the proposed method with existing approaches, which showed that the proposed method can detect the risks of the ongoing process more precisely and even earlier than existing approaches.

Objective To develop an algorithm to measure the dimensions of an airway oriented obliquely on a volumetric CT, as well as assess the effect of the imaging parameters on the correct measurement of the airway dimension. Materials and Methods An airway phantom with 11 poly-acryl tubes of various lumen diameters and wall thicknesses was scanned using a 16-MDCT (multidetector CT) at various tilt angles (0, 30, 45, and 60°). The CT images were reconstructed at various reconstruction kernels and thicknesses. The axis of each airway was determined using the 3D thinning algorithm, with images perpendicular to the axis being reconstructed. The luminal radius and wall thickness was measured by the full-width-half-maximum method. The influence of the CT parameters (the size of the airways, obliquity on the radius and wall thickness) was assessed by comparing the actual dimension of each tube with the estimated values. Results The 3D thinning algorithm correctly determined the axis of the oblique airway in all tubes (mean error: 0.91 ± 0.82°). A sharper reconstruction kernel, thicker image thickness and larger tilt angle of the airway axis resulted in a significant decrease of the measured wall thickness and an increase of the measured luminal radius. Use of a standard kernel and a 0.75-mm slice thickness resulted in the most accurate measurement of airway dimension, which was independent of obliquity. Conclusion The airway obliquity and imaging parameters have a strong influence on the accuracy of the airway wall measurement. For the accurate measurement of airway thickness, the CT images should be reconstructed with a standard kernel and a 0.75 mm slice thickness.

To develop a system for texture-based quantification of emphysema on high-resolution computed tomography (HRCT) and to compare it with density-based quantification in correlation with pulmonary function test (PFT).Two hundred sixty-one circular regions of interest (ROI) with 16-pixel diameter [66 ROIs representing typical area of normal lung; 69 representing bronchiolitis obliterans (BO); 64, mild emphysema (ME); and 62, severe emphysema (SE)] were used to train the automated classification system based on the Support Vector Machine classifier and on variable texture and shape features. An automated quantification system was developed with a moving ROI in the lung area, which classified each pixel into 4 categories. To validate the system, the HRCT and standard-kernel-reconstructed volumetric CT data of 39 consecutive patients with emphysema were included. Using this system, the whole lung area was evaluated, and the area fractions of each class were calculated (normal lung%, BO%, ME%, SE%, respectively). The emphysema index (EI) of texture-based quantification was defined as follows: (0.3 x ME% + SE%) (TEI). EIs from density-based quantification with a threshold of -950 Hounsfield Units, were measured on both HRCT (DEI_HR_2D) and on volumetric CT (DEI_standard_3D). The agreement between TEI, DEI_HR_2D, and DEI_standard_3D was assessed using interclass correlation coefficients (ICC). Correlation of the results on the TEI with the PFT results was compared with the results of the DEI_standard_3D and the DEI_HR_2D with Spearman's correlation test. To evaluate the contribution of each texture-based quantification lesion (BO%, ME%, SE%) on PFT, multiple linear regression analysis was performed.The calculated TEI (19.71% +/- 17.98%) was well correlated with the DEI_standard_3D (19.42% +/- 14.30%) (ICC = 0.95), whereas the ICC with DEI_HR_2D (37.22% +/- 9.42%) was 0.43. TEI showed better correlation with PFT than DEI_standard_3D or DEI_HR_2D did [R = 0.71 vs. 0.67 vs. 0.61 for forced expiratory volume in 1 second (FEV(1))/forced vital capacity (FVC); 0.54 vs. 0.50 vs. 0.43 for diffusing capacity (DLco), respectively]. Multiple linear regression analysis revealed that the BO% and SE% areas were independent determinants of FEV(1)/FVC, whereas the ME% and the SE% were determinants of DLco.Texture-based quantification of emphysema using an automated system showed better correlation with the PFT results than density-based quantification. Separate quantification of the BO, ME, and SE areas showed a different contribution of each component to the FEV(1)/FVC and the DLco. The proposed system can be successfully used for detailed regional and global evaluation of lung lesions on HRCT scanning for emphysema.

To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q ≤ 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q ≤ 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94.7%, 97%; EMPH, 100%, 100%; and CONS, 100%, 100%, respectively. We determined the optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT.

This paper presents an efficient version management method for BPM systems based on business process change patterns and the version stamp method. In managing business processes in a dynamic business environment, a proper process version management method for handling multiple coexisting versions of a business process is required. However, the traditional delta method for business process version management could not cope with the structural changes of a business process flexibly. The version management method proposed in this paper enables BPM systems to deal with dynamic situations more flexibly. Also, it contributes to increasing activity reusability by using the concept of activity pool.

In order to provide appropriate decision supports in medical domain, it is required that clinical knowledge should be implemented in a computable form and integrated with hospital information systems. Healthcare organizations are increasingly adopting tools that provide decision support functions to improve patient outcomes and reduce medical errors. This paper proposes a process centric clinical decision support system based on medical knowledge. The proposed system consists of three major parts - CPG (Clinical Practice Guideline) repository, service pool, and decision support module. The decision support module interprets knowledge base generated by both the CPG and service part and then generates a personalized and patient centered clinical process satisfying specific requirements of an individual patient during the entire treatment in hospitals. The proposed system helps health professionals to select appropriate clinical procedures according to the circumstances of each patient resulting in improving the quality of care and reducing medical errors.

This study is conducted to assess the influence of various CT imaging parameters and airway obliquity, such as reconstruction kernel, field of view, slice thickness, and obliquity of airway on automatic measurement of airway wall thickness with FWHM method and physical phantom. The phantom, consists of 11 poly-acryl tubes with various inner lumen diameters and thickness, was used in this study. The measured density of the wall was 150HU. The airspace outside of tube was filled with poly-urethane foam, whose density was -900HU, which is similar density of emphysema region. CT images, obtained with MDCT (Sensation 16, Siemens), was reconstructed with various reconstruction kernel (B10f, B30f, B50f, B70f and B80f), different field of views (180mm, 270mm, 360mm), and different thicknesses (0.75, 1, and 2 mm). The phantom was scanned at various oblique angles (0, 30, 45, 60 degree). Using in-house airway measurement software, central axis of oblique airway was determined by 3D thinning algorithm and CT image perpendicular to the axis was reconstructed. The luminal area, outer boundary, and wall thickness was measured by FWHM method at each image. Actual dimension of each tube and measured CT values on each CT data set was compared. Sharper reconstruction kernel, thicker image thickness, and larger oblique angle of airway axis results in decrease of measured wall thickness. There was internal interaction between imaging parameters and obliquity of airway on the accuracy of measurement. There was a threshold point of 1-mm wall thickness, below which the measurement failed to represent the change of real thickness. Even using the smaller FOV, the accuracy was not improved. Usage of standard kernel (B50f) and 0.75mm thickness results in the most accurate measurement results, which is independent of obliquity of airway. (Mean error: 0 Degree 0.067±0.05mm, 30 Degree 0.076±0.09, 45 Degree 0.074±0.09, 60 Degree 0.091±0.09). In this imaging parameters, there was no significant difference (paired t-test : p > 0.05) between actual measurement and each oblique angle measurement. The accuracy of airway wall measurement was strongly influenced by imaging parameters and obliquity of airway. For the accurate measurement, independent of obliquity, we recommend the CT images reconstructed with 0.75mm slice thickness and B50f or B30f with sharpening filter.

In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various sizes (16x16, 32x32, and 64x64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the order of ROI size 16x16, 32x32, 64x64 pixels, respectively (t-test, p