Safety analysis of safety-critical systems for their applicability on NPP systems: A state-of-the-art review

Since any risk in the safety-critical systems (SCSs) might lead to a terrible disaster, the utmost quality attribute of these systems is safety. Researchers are putting their efforts to ensure the safety of such systems. This paper summarizes the state-of-the-art in the area of the safety attribute of such critical systems. The in depth literature survey is performed to explore the several methods/models used for safety analysis of the SCS. The limitations of such methods are also shown regarding their applicability in SCS of the nuclear power plant (NPP). We also propose a framework to perform safety analysis, which can deal with these limitations and demonstrated the approach on a case study of NPP. Further, the direction for future work is advised to extend the further scope of research based on the case study.     

Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images

In this paper we have used two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson–Kessel clustering (GKC) along with local information for unsupervised change detection in multitemporal remote sensing images. In conventional FCM and GKC no spatio-contextual information is taken into account and thus the result is not so much robust to small changes. Since the pixels are highly correlated with their neighbors in image space (spatial domain), incorporation of local information enhances the performance of the algorithms. In this work we have introduced a new technique for incorporation of local information. Change detection maps are obtained by separating the pixel-patterns of the difference image into two groups. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. Two fuzzy cluster validity measures (Xie–Beni and fuzzy hypervolume) have been used to quantitatively evaluate the performance. Results are compared with those of existing state of the art Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.     

Computing Extremal Quasiconformal Maps

Conformal maps are widely used in geometry processing applications. They are smooth, preserve angles, and are locally injective by construction. However, conformal maps do not allow for boundary positions to be prescribed. A natural extension to the space of conformal maps is the richer space of quasiconformal maps of bounded conformal distortion. Extremal quasiconformal maps, that is, maps minimizing the maximal conformal distortion, have a number of appealing properties making them a suitable candidate for geometry processing tasks. Similarly to conformal maps, they are guaranteed to be locally bijective; unlike conformal maps however, extremal quasiconformal maps have sufficient flexibility to allow for solution of boundary value problems. Moreover, in practically relevant cases, these solutions are guaranteed to exist, are unique and have an explicit characterization. We present an algorithm for computing piecewise linear approximations of extremal quasiconformal maps for genus‐zero surfaces with boundaries, based on Teichmüller's characterization of the dilatation of extremal maps using holomorphic quadratic differentials. We demonstrate that the algorithm closely approximates the maps when an explicit solution is available and exhibits good convergence properties for a variety of boundary conditions.     

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters (changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie-Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.     

A mirror based polar magneto-optical Kerr effect spectroscopy arrangement

An arrangement is described for performing magneto-optical Kerr effect (MOKE) spectroscopy in polar geometry with a conventional C-frame or H-frame type electromagnet. It uses an additional mirror which eliminates the need for an electromagnet pole piece with an axial hole and allows for easy switching between polar MOKE geometry and longitudinal or transverse MOKE geometries. A theoretical analysis of the photo-elastic modulation based detection scheme shows that the mirror causes a strong mixing of signals corresponding to Kerr rotation and ellipticity. The influence of the mirror is experimentally demonstrated and a procedure is given to correct for it. MOKE spectrum of nickel films obtained using this arrangement is shown to match with reports in the literature.     

A recursive, numerically stable, and efficient simulation algorithm for serial robots with flexible links

A methodology for the formulation of dynamic equations of motion of a serial flexible-link manipulator using the decoupled natural orthogonal complement (DeNOC) matrices, introduced elsewhere for rigid bodies, is presented in this paper. First, the Euler Lagrange (EL) equations of motion of the system are written. Then using the equivalence of EL and Newton–Euler (NE) equations, and the DeNOC matrices associated with the velocity constraints of the connecting bodies, the analytical and recursive expressions for the matrices and vectors appearing in the independent dynamic equations of motion are obtained. The analytical expressions allow one to obtain a recursive forward dynamics algorithm not only for rigid body manipulators, as reported earlier, but also for the flexible body manipulators. The proposed simulation algorithm for the flexible link robots is shown to be computationally more efficient and numerically more stable than other algorithms present in the literature. Simulations, using the proposed algorithm, for a two link arm with each link flexible and a Space Shuttle Remote Manipulator System (SSRMS) are presented. Numerical stability aspects of the algorithms are investigated using various criteria, namely, the zero eigenvalue phenomenon, energy drift method, etc. Numerical example of a SSRMS is taken up to show the efficiency and stability of the proposed algorithm. Physical interpretations of many terms associated with dynamic equations of flexible links, namely, the mass matrix of a composite flexible body, inertia wrench of a flexible link, etc. are also presented. The work has been carried out in the Dept. of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India.     

Detecting anomalous access patterns in relational databases

A considerable effort has been recently devoted to the development of Database Management Systems (DBMS) which guarantee high assurance and security. An important component of any strong security solution is represented by Intrusion Detection (ID) techniques, able to detect anomalous behavior of applications and users. To date, however, there have been few ID mechanisms proposed which are specifically tailored to function within the DBMS. In this paper, we propose such a mechanism. Our approach is based on mining SQL queries stored in database audit log files. The result of the mining process is used to form profiles that can model normal database access behavior and identify intruders. We consider two different scenarios while addressing the problem. In the first case, we assume that the database has a Role Based Access Control (RBAC) model in place. Under a RBAC system permissions are associated with roles, grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals while holding a specific role, behave differently than expected. An important advantage of providing an ID technique specifically tailored to RBAC databases is that it can help in protecting against insider threats. Furthermore, the existence of roles makes our approach usable even for databases with large user population. In the second scenario, we assume that there are no roles associated with users of the database. In this case, we look directly at the behavior of the users. We employ clustering algorithms to form concise profiles representing normal user behavior. For detection, we either use these clustered profiles as the roles or employ outlier detection techniques to identify behavior that deviates from the profiles. Our preliminary experimental evaluation on both real and synthetic database traces shows that our methods work well in practical situations. This material is based upon work supported by the National Science Foundation under Grant No. 0430274 and the sponsors of CERIAS.     

Thermophysical properties of densified pitch based carbon/carbon materials—II. Bidirectional composites

Bidirectional carbon/carbon composites were developed using high-pressure impregnation/carbonization technique with PAN based carbon fabric as reinforcement and coal tar and synthetic pitches as matrix precursors. Microstructure of these composites has been evaluated using scanning electron microscope and polarized light optical microscope. Thermophysical properties i.e. thermal conductivity and specific heat have been evaluated both at room temperature and between 40 and 300 °C. The temperature dependence of thermal diffusion, specific heat and thermal conductivity has been studied and correlated with microstructure of carbon/carbon composites. It is found that the specific heat of carbon/carbon composites shows increase with temperature with an inverse slope in the temperature range of 150-200 °C. Accordingly, though the thermal conductivity decrease with temperature due to increased phonon interactions, it shows reversible action between 150 and 200 °C.