A study on security strategy in ICT convergence environment

In the knowledge and information society, the value of utilizing ICT technology in companies is being newly interpreted upon the convergence of business and information technology of companies. Accordingly, companies are ensuring corporate competitiveness by improving product productivity and reducing the consumption of human/physical resources through the change of strategic management environment using ICT technology. On the other hand, companies’ core information is being leaked outside of company from the convergence with ICT technology and its scale is increasing each year. Accordingly, the need for improving the level of security according to companies’ informatization level is increasing. However, studies are lacking on the composition of security strategy in suitable forms according to the scale of company and level of retaining resources. Accordingly, this study examined the security advancement steps to verify the present condition of companies’ security and design security strategy from a macroscopic perspective by improving the level of security according to the scale of company and present condition of resources.     

Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.     

Morphology,crystallinity, and electrochemical properties of in situ formed poly(ethylene oxide)/TiO2 nanocomposite polymer electrolytes

A method to produce nanocomposite polymer electrolytes consisting of poly(ethylene oxide) (PEO) as the polymer matrix, lithium tetrafluoroborate (LiBF₄) as the lithium salt, and TiO₂ as the inert ceramic filler is described. The ceramic filler, TiO₂, was synthesized in situ by a sol–gel process. The morphology and crystallinity of the nanocomposite polymer electrolytes were examined by scanning electron microscopy and differential scanning calorimetry, respectively. The electrochemical properties of interest to battery applications, such as ionic conductivity, Li⁺ transference number, and stability window were investigated. The room‐temperature ionic conductivity of these polymer electrolytes was an order of magnitude higher than that of the TiO₂ free sample. A high Li⁺ transference number of 0.51 was recorded, and the nanocomposite electrolyte was found to be electrochemically stable up to 4.5 V versus Li⁺/Li. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 2815–2822, 2003     

A power-efficient vertical handover with MIH-based network scanning through consistency check

We propose an power-efficient scanning scheme considering the consistency of the Information Element (IE) for the Media Independent Handover (MIH) based Vertical Handover. Since the Green IT is one of the key issues of the science, we suggested the power saving scheme by extending the information and the event service of the MIH framework to reduce the number of full scanning. We proposed the criteria to select the update MT to quantify the power consumption alongside various network scanning methods. Since the frequent and multiple network scanning incurred by small mobile terminals (MT) is considerable, the scanning avoidance is essential for the framework we suggest. The MIH Information Service (MIIS) provides many usable factors and features for the scanning avoidance. However, because of the absence of the criteria to select the update node, these values are not actually usable. Therefore, we suggest an Energy Efficient Function, which tests the validity of MIIS values and makes a decision on whether to avoid the scanning or not.     

Discontinuous Dissolution of Iron Aluminate Spinel in the Al2O3–Fe2O3 System

When sintered 85Al₂O₃–15Fe₂O₃ (in wt%) specimens consisting of corundum grains and spinel particles were annealed at temperature where only a corundum phase was stable, phase transformation of spinel into metastable FeAIO₃ and subsequently complete dissolution of the metastable phase occurred together with the migration of grain boundaries at the surface of the specimens. Since the grain boundary migration was induced by grain boundary diffusion of Fe₂O₃ from the transforming and dissolving particles, the boundary migration by temperature decrease corresponds to a discontinuous dissolution of the spinel particles and a chemically induced grain boundary migration by temperature change. Inside the specimens, however, the transformation—dissolution and the grain boundary migration were suppressed because of unavailable accommodation of the volume expansion due to the transformation.     

An efficient algorithm for incrementally mining frequent closed itemsets

The purpose of mining frequent itemsets is to identify the items in groups that always appear together and exceed the user-specified threshold of a transaction database. However, numerous frequent itemsets may exist in a transaction database, hindering decision making. Recently, the mining of frequent closed itemsets has become a major research issue because sets of frequent closed itemsets are condensed yet complete representations of frequent itemsets. Therefore, all frequent itemsets can be derived from a group of frequent closed itemsets. Nonetheless, the number of transactions in a transaction database can increase rapidly in a short time period, and a number of the transactions may be outdated. Thus, frequent closed itemsets may be changed with the addition of new transactions or the deletion of old transactions from the transaction database. Updating previously closed itemsets when transactions are added or removed from the transaction database is challenging. This study proposes an efficient algorithm for incrementally mining frequent closed itemsets without scanning the original database. The proposed algorithm updates closed itemsets by performing several operations on the previously closed itemsets and added/deleted transactions without searching the previously closed itemsets. The experimental results show that the proposed algorithm significantly outperforms previous methods, which require a substantial length of time to search previously closed itemsets.     

A Framework for Moving Least Squares Method with Total Variation Minimizing Regularization

In this paper, we propose a computational framework to incorporate regularization terms used in regularity based variational methods into least squares based methods. In the regularity based variational approach, the image is a result of the competition between the fidelity term and a regularity term, while in the least squares based approach the image is computed as a minimizer to a constrained least squares problem. The total variation minimizing denoising scheme is an exemplary scheme of the former approach with the total variation term as the regularity term, while the moving least squares method is an exemplary scheme of the latter approach. Both approaches have appeared in the literature of image processing independently. By putting schemes from both approaches into a single framework, the resulting scheme benefits from the advantageous properties of both parties. As an example, in this paper, we propose a new denoising scheme, where the total variation minimizing term is adopted by the moving least squares method. The proposed scheme is based on splitting methods, since they make it possible to express the minimization problem as a linear system. In this paper, we employed the split Bregman scheme for its simplicity. The resulting denoising scheme overcomes the drawbacks of both schemes, i.e., the staircase artifact in the total variation minimizing based denoising and the noisy artifact in the moving least squares based denoising method. The proposed computational framework can be utilized to put various combinations of both approaches with different properties together.     

Water-induced effects of hydrogen adsorption on Ru(001)

Adsorption and desorption of hydrogen and coadsorption of H₂ with H₂O and CO over Ru(00l) surface have been studied under UHV conditions using the technique of TDS. Surface hydrogen interacts with adsorbed water resulting in an additional desorption state at 510 K which is not easily displaced by CO, but the total number of adsorption sites for hydrogen adsorption is independent of the amount of H₂O predosed at room temperature. Hydrogen adsorption is blocked easily by CO dose (more than 0.5 L) or a small amount of O(a) formed from dissociative adsorption of water, and adsorbed hydrogen formed in the absence of significant water is easily displaced by CO dose at even room temperature.     

Fault identification for process monitoring using kernel principal component analysis

In this research, we develop a new fault identification method for kernel principal component analysis (kernel PCA). Although it has been proved that kernel PCA is superior to linear PCA for fault detection, the fault identification method theoretically derived from the kernel PCA has not been found anywhere. Using the gradient of kernel function, we define two new statistics which represent the contribution of each variable to the monitoring statistics, Hotelling's T²and squared prediction error (SPE) of kernel PCA, respectively. The proposed statistics which have similar concept to contributions in linear PCA are directly derived from the mathematical formulation of kernel PCA and thus they are straightforward to understand. The main contribution of this work is that we firstly suggest a fault identification method especially applicable to process monitoring using kernel PCA. To demonstrate the performance, the proposed method is applied to two simulated processes, one is a simple nonlinear process and the other is a non-isothermal CSTR process. The simulation results show that the proposed method effectively identifies the source of various types of faults.