An apparent kinetic model for the carbonation of calcium oxide by carbon dioxide

For the apparent kinetics of the carbonation reaction of calcium oxide by carbon dioxide, as a kind of noncatalytic gas–solid reaction, a model equation has been proposed as follows: X=kbt/(b+t), where X is the conversion of CaO; k, a kinetic rate constant (time⁻¹); b, a constant (time) equivalent to the time taken to attain half the ultimate conversion of CaO, and t, the time. As a result of analyses for some literature-reported data of CaO-carbonation conversion, it has been found that the rate of the carbonation can be well represented by dX/dt=k(1−X/X_u)², where X_u is the ultimate conversion of CaO, which is given by the product of two parametric constants, k and b. The constants k and b in the two rate control regimes of CaO-carbonation, chemical reaction control and diffusion control, have been determined as functions of temperature, respectively. The activation energy in the carbonation of surface CaO with CO₂ is estimated to about 72 kJ/mol regardless of the sources of CaO, however, that in the diffusion control regime appears differently as 102.5 (mesoporous CaO) or 189.3 kJ/mol (commercial-available CaO), possibly due to the morphological differences of the two CaO samples. From a practical point of view, the simple model equation proposed in this study deserves attention in that the CaO-carbonation behavior at working temperatures higher than 700 °C could be closely predicted.     

A resource-aware scheduling algorithm with reduced task duplication on heterogeneous computing systems

To satisfy the high-performance requirements of application executions, many kinds of task scheduling algorithms have been proposed. Among them, duplication-based scheduling algorithms achieve higher performance compared to others. However, because of their greedy feature, they duplicate parents of each task as long as the finish time can be reduced, which leads to a superfluous consumption of resource. However, a large amount of duplications are unnecessary because slight delay of some uncritical tasks does not affect the overall makespan. Moreover, these redundant duplications would occupy the resources, delay the execution of subsequent tasks, and increase the schedule makespan consequently. In this paper, we propose a novel duplication-based algorithm designed to overcome the above drawbacks. The proposed algorithm is to schedule tasks with the least redundant duplications. An optimizing scheme is introduced to search and remove redundancy for a schedule generated by the proposed algorithm further. Randomly generated directed acyclic graphs and two real-world applications are tested in our experiments. Experimental results show that the proposed algorithm can save up to 15.59  % resource consumption compared with the other algorithms. The makespan has improvement as well.     

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.     

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.     

Approximate inference for dynamic Bayesian networks: sliding window approach

Dynamic Bayesian networks (DBNs) are probabilistic graphical models that have become a ubiquitous tool for compactly describing statistical relationships among a group of stochastic processes. A suite of elaborately designed inference algorithms makes it possible for intelligent systems to use a DBN to make inferences in uncertain conditions. Unfortunately, exact inference or even approximation in a DBN has been proved to be NP-hard and is generally computationally prohibitive. In this paper, we investigate a sliding window framework for approximate inference in DBNs to reduce the computational burden. By introducing a sliding window that moves forward as time progresses, inference at any time is restricted to a quite narrow region of the network. The main contributions to the sliding window framework include an exploration of its foundations, explication of how it operates, and the proposal of two strategies for adaptive window size selection. To make this framework available as an inference engine, the interface algorithm widely used in exact inference is then integrated with the framework for approximate inference in DBNs. After analyzing its computational complexity, further empirical work is presented to demonstrate the validity of the proposed algorithms.     

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.