Hybrid approach to distribution planning reflecting a stochastic supply chain

Today’s business environment is experiencing as a period of expansion and the globalization. Therefore, a distribution plan with low cost and high customer satisfaction in supply chain management (SCM) has been widely investigated. The purpose of this study is to establish optimal distribution planning in the supply chain. In this paper, a hybrid approach involving a genetic algorithm (GA) and simulation is presented to solve this problem. The GA is employed in order to quickly generate feasible distribution sequences. Considering uncertain factors such as queuing, breakdowns and repairing time in the supply chain, the simulation is used to minimize completion time for the distribution plan. The computational results for an example of a simple supply chain are given and discussed to validate the proposed approach. We obtained a more realistic distribution plan with optimal completion time by performing the iterative hybrid GA simulation procedure which reflects the stochastic nature of supply chains.     

Condition monitoring scheme via one-class support vector machine and multivariate control charts

A condition-based maintenance (CBM) has been widely employed to reduce maintenance cost by predicting the health status of many complex systems in prognostics and health management (PHM) framework. Recently, multivariate control charts used in statistical process control (SPC) have been actively introduced as monitoring technology. In this paper, we propose a condition monitoring scheme to monitor the health status of the system of interest. In our condition monitoring scheme, we first define reference data set using one-class support vector machine (OC-SVM) to construct the control limit of multivariate control charts in phase I. Then, parametric control chart or non-parametric control chart is selected according to the results from multivariate normality tests. The proposed condition monitoring scheme is applied to sensor data of two anemometers to evaluate the performance of fault detection power.

     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Encapsulation of organic light-emitting devices by means of photopolymerized polyacrylate films

For encapsulation of organic light-emitting devices (OLEDs) built on glass substrate, photopolymerizable blend consists of pentaerythritol triacrylate (PETIA) and HSP188 (photoinitiator) was spin-coated onto an OLED and then cured to form a cross-linked passivation layer. The electroluminescence (EL) and the rate of degradation were examined to compare the electrical and the emissive properties of the device before and after forming the passivation layer. In this case, wet process encapsulation, which did not influence the EL characteristic of the device, enhanced the lifetime of the device in air.     

Projective moment invariants

The paper is devoted to the moment invariants with respect to projective transform. It has been a common belief that such invariants do not exist. We show that projective moment invariants exist in a form of infinite series containing moments with positive as well as negative indices.     

A Network Traffic Aware Tuning Algorithm in Wireless Sensor Networks

Wireless Personal Communications - Wireless sensor networks (WSNs) are being widely deployed in many areas such as smart homes and Internet of Things. A main concern in WSNs is energy conservation...     

Influences of the dynamic strain aging on the J–R fracture characteristics of the ferritic steels for reactor coolant piping system

The leak-before-break (LBB) design of the piping system for nuclear power plants has been based on the premise that the leakage due to the through-wall crack can be detected by using leak detection systems before a catastrophic break. The piping materials are required to have excellent J–R fracture characteristics. However, where ferritic steels for reactor coolant piping systems operate at the temperatures where dynamic strain aging (DSA) could occur, the fracture resistance could be reduced with the influence of DSA under dynamic loading. Therefore, in order to apply the LBB design concept to the piping system under seismic loading, both static and dynamic J–R characteristics must be evaluated.Materials used in this study are SA516 Gr.70 for the elbow pipe and SA508 Cl.1a for the main pipe and their welding joints. The crack extension during the dynamic and the static J–R tests was measured by the direct current potential drop (DCPD) and the compliance method, respectively. This paper describes the influences of the dynamic strain aging on the J–R fracture characteristics with the loading rate of the pipe materials and their welding joints.     

High heat flux test with HIP bonded Be/Cu/SS mock-ups for the ITER first wall

In order to verify the integrity of the first wall (FW) of the International Thermonuclear Experimental Reactor (ITER), especially for preparing its qualification program by ITER-O, Be/Cu/SS mock-ups, which were the same size as the qualification mock-ups, were fabricated and tested at the TSEFEY, an e-beam facility, in Efremov, Russia. These mock-ups were joined with a 316 L austenitic stainless steel (SS316L) block for a structural material, CuCrZr for a heat sink material and SS316L tubes for a coolant and then, joined with three Be tiles for an armor material. A hot isostatic pressing (HIP) was used as manufacturing methods at a 1050 °C, 100 MPa for 2 h for a Cu/SS joining and at a 580 °C, 100 MPa for 2 h for a Be/(Cu/SS) joining. Two mock-ups were fabricated by using 1 μmCr/10 μmCu of an interlayer between the Be tile and Cu block. The high heat flux (HHF) tests were performed at 1.5 and 2.0 MW/m² heat fluxes for each mock-up. The given conditions and the expected fatigue lifetime were evaluated from a preliminary analysis with ANSYS. Both mock-ups survived for up to 1000 and 268 cycles at 1.5 and 2.0 MW/m² heat fluxes, respectively. They are higher than the expected numbers of cycles to a failure.