Mathematical model for computer simulation and control of steelmaking

A dynamic model for computer simulation and control of steelmaking has been developed. It is essentially based on multicomponent mixed transport control theory with the incorporation of energy balance calculations. The model is applicable to both steelmaking in electric furnaces as well as in oxygen steelmaking converters. The adjustable parameters of the model for simulation of oxygen steelmaking are gas evolution rate (G_co). oxygen flux factor (F_o) and emulsification factor (EM). These simulation parameters, when combined with on-line measurement of off-gas composition and temperature, enable complete dynamic control of the process. The model developed is applied, as an example, to an industrially produced heat in a top blown oxygen steelmaking converter and the results of simulation are discussed.     

An adaptive descent method for non-linear viscoplasticity

A forming model based on a viscoplastic flow formulation is derived which includes the effects of small elastic strains. A significant feature of the formulation is its reliance on the dominant inelastic material characteristics in the formation of the stiffness matrix for large strain problems. The resultant non-linear system of equations is solved by an adaptive descent method which combines the rapid convergence of Newton's method near the solution with the robustness of a method of successive approximations. The use of the adaptive descent method effectively extends the viscoplastic flow formulations into the nearly rate-insensitive range of behaviours exhibited, for example, by metals at low temperature, where slow convergence of the non-linear solution algorithm has traditionally hampered their use.     

Synthesis and characterization of acidic properties of Al-SBA-15 materials with varying Si/Al ratios

Al-SBA-15 of varying Si/Al ratios in the range 11.4–78.4 was synthesized using tri-block copolymer P123. The calcined materials were examined by XRD, pore size distribution, surface area, ²⁷Al NMR spectroscopy. The acidity and acid strength distribution were studied using microcalorimetric adsorption of NH₃. The acidic properties were also examined by cumene cracking reaction as a function of Si/Al ratios. Systematic variation of acidity and activity was observed as a function of Si/Al ratio. The initial heats of NH₃ adsorption correlated well with activity indicate that acid sites with ΔH > 100 kJ/mole is responsible for cumene cracking activity. Linear correlations were obtained with total acidity and cumene cracking activities. The tetrahedral aluminum was found to be responsible for the observed acidities and catalytic activities.     

Parallel heap: An optimal parallel priority queue

We describe a new parallel data structure, namely parallel heap, for exclusive-read exclusive-write parallel random access machines. To our knowledge, it is the first such data structure to efficiently implement a truly parallel priority queue based on a heap structure. Employing p processors, the parallel heap allows deletions of (p) highest priority items and insertions of (p) new items, each in O(log n) time, where n is the size of the parallel heap. Furthermore, it can efficiently utilize processors in the range 1 through n.This work was supported by U.S. Army's PM-TRADE contract N61339-88-g-0002, Florida High Technology and Industry grant 11-28-716, and Georgia State University's internal research support during spring and summer quarters, 1991.     

Particle distribution control in cast aluminium alloy-mica composites

A suspension of mica particles (40m diameter and 3.7 thick) obtained in a mechanically stirred Al-4 wt % Cu-1.5 wt % Mg melt was poured and solidified in a variety of moulds under different heat flow configurations. The resulting cast structure showed a non-uniform distribution of dispersed mica particles with mica-depleted and segregated zones due to their flotation before and during solidification. The experimentally observed profiles of mica-free regions deviate significantly from those computed on the basis of Stokes's law and freezing-time computations. In relatively thick castings, segregation of mica could be minimized by using low pouring temperatures and/or side as well as bottom chilling. It was found, however, that thin castings (12.5 mm) could easily be produced with a homogeneous distribution of mica particles.     

Entanglement sudden death and birth in qubit–qutrit systems under Dzyaloshinskii–Moriya interaction

We study entanglement dynamics of qubit–qutrit pair under Dzyaloshinskii–Moriya (DM) interaction. The qubit–qutrit pair acts as a closed system and one external qubit serve as the environment for the pair. The external qubit interact with qubit of closed system via DM interaction. This interaction frequently kills the entanglement between qubit–qutrit pair, which is also periodically recovered. On the other hand two parameter class of state of qubit–qutrit pair also affected by DM interaction and one parameter class of state remains unaffected. The frequency of occurrence of entanglement sudden death and entanglement sudden birth in two parameter class of state is half than qubit–qutrit pure state. We used our quantification of entanglement as negativity measure.     

Security Driven Scheduling Model for Computational Grid Using NSGA-II

Number of software applications demands various levels of security at the time of scheduling in Computational Grid. Grid may offer these securities but may result in the performance degradation due to overhead in offering the desired security. Scheduling performance in a Grid is affected by the heterogeneities of security and computational power of resources. Customized Genetic Algorithms have been effectively used for solving complex optimization problems (NP Hard) and various heuristics have been suggested for solving Multi-objective optimization problems. In this paper a security driven, elitist non-dominated sorting genetic algorithm, Optimal Security with Optimal Overhead Scheduling (OSO₂S), based on NSGA-II, is proposed. The model considers dual objectives of minimizing the security overhead and maximizing the total security achieved. Simulation results exhibit that the proposed algorithm delivers improved makespan and lesser security overhead in comparison to other such algorithms viz. MinMin, MaxMin, SPMinMin, SPMaxMin and SDSG.     

Validity as a Measure of Data Quality in Internet of Things Systems

Data quality became significant with the emergence of data warehouse systems. While accuracy is intrinsic data quality, validity of data presents a wider perspective, which is more representational and contextual in nature. Through our article we present a different perspective in data collection and collation. We focus on faults experienced in data sets and present validity as a function of allied parameters such as completeness, usability, availability and timeliness for determining the data quality. We also analyze the applicability of these metrics and apply modifications to make it conform to IoT applications. Another major focus of this article is to verify these metrics on aggregated data set instead of separate data values. This work focuses on using the different validation parameters for determining the quality of data generated in a pervasive environment. Analysis approach presented is simple and can be employed to test the validity of collected data, isolate faults in the data set and also measure the suitability of data before applying algorithms for analysis. On analyzing the data quality of the two data sets on the basis of above-mentioned parameters. We show that validity for data set 1 was found to be 75% while it was found to be 67% only for data set 2. Availability and data freshness metrics performance were analyzed graphically. It was found that for data set 1, data freshness was better while availability metric was found better for data set 2. Usability obtained for data set 2 was 86% which was higher as compared to data set 1 whose usability metric was 69%. Thus, this work presents methods that can be leveraged for estimating data quality that can be beneficial in various IoT based industries which are essentially data centric and the decisions made by them depends upon the validity of data.

     

Correction to: Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Wireless Personal Communications -     

Layered Approach Using Conditional Random Fields for Intrusion Detection

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.