Computer-aided measurement plan for an impeller on a coordinate measurement machine with a rotating and tilting probe

This paper presents a computer-aided measurement plan (CAMP) for an impeller on a coordinate measurement machine (CMM) with a rotating/tilting probe. The blade surfaces of a machined impeller were measured exactly to secure the machining tolerance and surface finish of the impeller. CMM with a rotating and tilting probe is commonly used for this measurement. However, it is not easy to evaluate all the points on the impeller surfaces due to the time-consuming nature of the CMM measurement. In addition, many points cannot be easily accessed by the CMM probe due to the interference between the probe and the impeller blades. Furthermore, the inherent complexity of the existing teaching operation of a probe using a joystick suggests the necessity of developing a new method for the measurement planning of an impeller. Thus, this paper proposes a CAMP for effectively gauging the inspection points based on the ruled line information of the impeller blade surfaces. The proposed plan partitions the surfaces into several measurement regions based on the ruled lines of a blade surface and the approach vectors of the probe. Finally, a case example for an impeller is presented to demonstrate the effectiveness of the proposed strategy.     

Factors affecting the implementation of electronic data interchange in Korea

Although there have been a number of studies of the determinant factors of Electronic Data Interchange (EDI) implementation, most studies were carried out in the United States or Europe. The purpose of this study is to identify the determinant factors which affect implementation of EDI systems by Korean companies. Decentralization, EDI standards, technical compatibility, technical support by EDI vendors, education and training and participation of trading partners positively affect the extent of EDI usage. Formalization, technical compatibility, user involvement and participation of trading partners affect EDI integration with internal applications. Top management support and the participation of trading partners promote the extent of EDI operations. This study provides evidence of the effect of important determinant factors such as participation of trading partners and technical compatibility replicating previous EDI studies conducted in that US and Europe. For policy makers ad EDI developers, this study provides insights as to appropriate measures and incentives system designed to encourage EDI and B2B implementation.     

Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.     

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

Optimal infinite scheduling for multi-priced timed automata

This paper is concerned with the derivation of infinite schedules for timed automata that are in some sense optimal. To cover a wide class of optimality criteria we start out by introducing an extension of the (priced) timed automata model that includes both costs and rewards as separate modelling features. A precise definition is then given of what constitutes optimal infinite behaviours for this class of models. We subsequently show that the derivation of optimal non-terminating schedules for such double-priced timed automata is computable. This is done by a reduction of the problem to the determination of optimal mean-cycles in finite graphs with weighted edges. This reduction is obtained by introducing the so-called corner-point abstraction, a powerful abstraction technique of which we show that it preserves optimal schedules. This work has been mostly done while visiting CISS at Aalborg University in Denmark and has been supported by CISS and by ACI Cortos, a program of the French Ministry of Research.     

A new graph cut-based multiple active contour algorithm without initial contours and seed points

This paper presents a new graph cut-based multiple active contour algorithm to detect optimal boundaries and regions in images without initial contours and seed points. The task of multiple active contours is framed as a partitioning problem by assuming that image data are generated from a finite mixture model with unknown number of components. Then, the partitioning problem is solved within a divisive graph cut framework where multi-way minimum cuts for multiple contours are efficiently computed in a top-down way through a swap move of binary labels. A split move is integrated into the swap move within that framework to estimate the model parameters associated with regions without the use of initial contours and seed points. The number of regions is also estimated as a part of the algorithm. Experimental results of boundary and region detection of natural images are presented and analyzed with precision and recall measures to demonstrate the effectiveness of the proposed algorithm.     

Replication of nanostructures on microstructures by intermediate film mold inserted hot embossing process

The present study proposes a simple method to replicate nano/micro combined multiscale structures using an intermediate film mold and anodic aluminum oxide (AAO) nanomold in hot embossing process. The proposed method is simply to add an intermediate film mold with microscale thru-hole patterns to the ordinary mold system, on which nanostructures are patterned, in the hot embossing process. The intermediate film mold is inserted between polymer substrate and AAO nanomold. During the hot embossing process, the polymer first fills microscale thru-hole patterns in the intermediate film mold and subsequently fills nanopores in AAO nanomold, resulting in the nano/micro combined structures. The intermediate film molds, which have microscale thru-hole patterns were fabricated by micro-milling, laser ablation, etching methods and/or LIGA process. The nano/micro combined structures were successfully replicated by the proposed method.     

Collapse-free thermal bonding technique for large area microchambers in plastic lab-on-a-chip applications

Bonding is an essential step to form microchannels or microchambers in lab-on-a-chip applications. In this paper, we present a novel plastic thermal bonding technique to seal and form large area microchambers (planar characteristic width and length on the order of 1 mm and characteristic thickness on the order of 10–100 μm) without collapse by introducing a holed pressure equalizing plate (HPEP) that includes holes of the same size and shape as the microchambers. To demonstrate the proposed technique, two types of large area microchambers [(1) 20 × 10 mm and 40 μm thick and (2) 12 × 2.5 mm and 120 μm thick] with microchannels were designed and replicated on plastic substrates by means of hot embossing and injection molding processes with prepared two nickel mold inserts. The replicated large area microchambers as well as the microchannels in the plastic lab-on-a-chip were successfully sealed (i.e., no leakage) and formed without any collapse by the proposed thermal bonding technique with the help of the HPEP.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

数据中心管理不再复杂

随着数据中心基础设施以及应用系统的增多,IT人员的工作量逐渐加大,自动化管理让这些变得更容易。