SIMULATION STUDY OF GENERALIZED MINIMUM VARIANCE CONTROL FOR AN EXTRACTION TURBINE

In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control the two quantities accurately because of existence of nonlinearity and coupling. A generalized minimum variance control method is studied for an extraction turbine. Firstly, a nonlinear mathematical model of the control system about the two quantities is transformed into a linear system with two white noises. Secondly, a generalized minimum variance control law is applied to the system. A comparative simulation is done. The simulation results indicate that precision and dynamic quality of the regulating system under the new control law are both better than those under the state feedback control law.

Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.

Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images

The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs,their matching has high false alarm.This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference(SAD)in different manner for the new optical satellite generation called near-equatorial orbit satellite(NEqO)and multi-sensor images.The proposed method leads to improving CPs matching with a significantly higher rate of correct matches.The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area.The proposed method consists of three parts:(1)applying the SIFT to extract CPs automatically,(2)refining CPs matching by SAD algorithm with empirical threshold,and(3)evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method.The result indicates an accurate and precise performance of the model,which showed the effectiveness and robustness of the proposed approach.

Automated extraction of attributes from natural language attribute-based access control(ABAC)Policies

The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.

Automated extraction of attributes from natural language attribute-based access control(ABAC)Policies

The National Institute of Standards and Technology(NIST)has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy(NLACP)to a machine-readable form.To study the automation process,we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations.Therefore,this paper focuses on the questions of:how can we automatically infer the hierarchical structure of an ABAC model given NLACPs;and,how can we extract and define the set of authorization attributes based on the resulting structure.To address these questions,we propose an approach built upon recent advancements in natural language processing and machine learning techniques.For such a solution,the lack of appropriate data often poses a bottleneck.Therefore,we decouple the primary contributions of this work into:(1)developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts,and(2)generating a set of realistic synthetic natural language access control policies(NLACPs)to evaluate the proposed framework.Our experimental results are promising as we achieved-in average-an F1-score of 0.96 when extracting attributes values of subjects,and 0.91 when extracting the values of objects’attributes from natural language access control policies.

Beam-pointing error compensation method of phased array radar seeker with phantom-bit technology

A phased array radar seeker（PARS） must be able to effectively decouple body motion and accurately extract the line-of-sight（LOS） rate for target missile tracking.In this study,the realtime two-channel beam pointing error（BPE） compensation method of PARS for LOS rate extraction is designed.The PARS discrete beam motion principium is analyzed,and the mathematical model of beam scanning control is finished.According to the principle of the antenna element shift phase,both the antenna element shift phase law and the causes of beam-pointing error under phantom-bit conditions are analyzed,and the effect of BPE caused by phantom-bit technology（PBT） on the extraction accuracy of the LOS rate is examined.A compensation method is given,which includes coordinate transforms,beam angle margin compensation,and detector dislocation angle calculation.When the method is used,the beam angle margin in the pitch and yaw directions is calculated to reduce the effect of the missile body disturbance and to improve LOS rate extraction precision by compensating for the detector dislocation angle.The simulation results validate the proposed method.

Green-synthesized gold nanoparticles from Plumeria alba flower extract to augment catalytic degradation of organic dyes and inhibit bacterial growth

Bio-inspired eco-friendly gold nanoparticles were synthesized by a green method using aqueous Plume- ria alba flower extract （PAFE）. The use of 1% and 5% concentrations of PAFE resulted in two different sizes of P. alba gold nanoparticles, PAGNPsl and PAGNPs2, with surface plasmon resonance （SPR） peaks at 552 and 536 nm, respectively. Size-controlled formation of gold nanoparticles was indicated by the SPR shift observed with increasing concentration of PAFE. The accurate size and morphology of PAGNPs I and PAGNPs2 were determined by transmission electron microscope （TEM） analysis is found to be 28 ＋ 5.6 and 15.6 4-3.4 nm, respectively, and those are spherical in shape. The antibacterial activity of PAGNPsl and PAGNPs2 was tested against Escherichia coil; the small-sized PAGNPs2 exhibited better antibacterial activity with a 16-mm zone of inhibition at a concentration of 400 txg/mL. Furthermore, the catalytic activity of PAGNPsl and PAGNPs2 was analyzed on six hazardous dyes; PAGNPs2 exhibited more pro- nounced catalytic activity than PAGNPsl. Among all of the dyes, 4-nitrophenol was most rapidly degraded to 4-aminophenol by PAGNPs2 within 5 min. The mechanism of catalysis in the presence of PAGNPsl and PAGNPs2 can be described as an electron transfer process from donor NaBH4 to an acceptor. The facile green synthesis of such eco-friendly nanoparticles in bulk suggests this method has potential industrial applications.