Uncertainty management in expert systems using fuzzy Petri nets

The paper aims at developing new techniques for uncertainty management in expert systems for two generic class of problems using fuzzy Petri nets that represent logical connectivity among a set of imprecise propositions. One class of problems deals with the computation of fuzzy belief of any proposition from the fuzzy beliefs of a set of independent initiating propositions in a given network. The other class of problems is concerned with the computation of steady-state fuzzy beliefs of the propositions embedded in the network, from their initial fuzzy beliefs through a process called belief revision. During belief revision, a fuzzy Petri net with cycles may exhibit “limit cycle behavior” of fuzzy beliefs for some propositions in the network. No decisions can be arrived at from a fuzzy Petri net with such behavior. To circumvent this problem, techniques have been developed for the detection and elimination of limit cycles. Further, an algorithm for selecting one evidence from each set of mutually inconsistent evidences, referred to as nonmonotonic reasoning, has also been presented in connection with the problems of belief revision. Finally, the concepts proposed for solving the problems of belief revision have been applied successfully for tackling imprecision, uncertainty, and nonmonotonicity of evidences in an illustrative expert system for criminal investigation     

Harmony search algorithm for infinite impulse response system identification

In this paper, optimal sets of filter coefficients are searched by a meta-heuristic optimization technique called Harmony Search (HS) algorithm for infinite impulse response (IIR) system identification problem. For different optimization problems, HS algorithm undergoes three basic rules; namely Random Selection (RS), Harmony Memory Consideration (HMC), and Pitch Adjustment (PA) rules, which are inspired from the process that the musicians use to improvise a perfect state of harmony with the consummate skill of blending notes in tune. With the help of the properly selected control parameters, a perfect balance is achieved in exploration and exploitation in searching phases. The detailed analysis of simulation results emphasizes the strength of HS algorithm to find the near-global optimal solution, quality of convergence profile and the speed of convergence while tested against standard benchmark examples for same and reduced order models.     

Applying a normalized ratio scale technique to assess influences of urban expansion on land surface temperature of the semi-arid city of Erbil

The difference between surface and air temperature within a city and its surrounding area is a result of variations in surface cover, thermal capacity, and 3-dimensional geometry. This research has examined and quantified the decreasing daytime land surface temperature (LST) in Erbil, Kurdistan region of Iraq, and the influence of rapid urban expansion on urban heat/cool island effect over a 20 year period. Land-use/land-cover change across this time period is also established using pixel samples. The current study proposes the application of the normalized ratio scale (NRS) to adjust the temperature of images acquired at different dates to the same range. Eleven satellite images acquired by Landsat 4, 5, 7, and 8 during the period 1992–2013 are used to retrieve LST. The results indicate that 55.3 km² of city land cover changed from bare soil to urban; consequently, the mean LST of the new urbanized area decreased by 2.28°C. The normalized difference vegetation index (NDVI) of Sami Abdul-Rahman (S.A.) Park increased from 0.09 ± 0.01 to 0.32 ± 0.11, resulting in a decrease of the mean LST by 7.29°C. This study shows that the NRS method is appropriate for detecting temperature trends from urbanization using remote-sensing data. It also highlights that urban expansion may lead to a decrease in daytime LST in drylands.     

Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach

Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.     

Complete discriminant evaluation and feature extraction in kernel space for face recognition

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.

Sensitivity improvement using optimized heater design for dual axis thermal accelerometers

This paper investigates the sensitivity improvement of an air filled cavity based thermal accelerometer. The accelerometer does not contain any solid proof mass and it is realizable in CMOS technology. The sensitivity has been improved by a new square ring shaped heater structure. The sensitivity of accelerometer performance is compared with other different heater shapes. It is found that, heater design having higher heat dissipation in the suspended beam gives higher sensitivity. The simulation is carried out using commercial FEM simulator COMSOL Multiphysics. For the peak heater temperature of 609 K, the proposed square ring shaped heater provides a sensitivity of 0.335 K/g. Under same conditions, sensitivity of an accelerometer with a meander shaped heater structure is only 0.098 K/g and diamond shaped structure is 0.229 K/g.     

Anonymization of moving objects databases by clustering and perturbation

Preserving individual privacy when publishing data is a problem that is receiving increasing attention. Thanks to its simplicity the concept of k-anonymity, introduced by Samarati and Sweeney [1], established itself as one fundamental principle for privacy preserving data publishing. According to the k-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k−1 other individuals.     

Application of Strong Arcs in m-Polar Fuzzy Graphs

Neural Processing Letters - Recently, m-polar fuzzy graph (mPF graph) becomes a growing research topic as the generalization of fuzzy graph. In this paper, at first mPF path, mPF cycle in an mPF...     

Application of evolutionary optimization techniques for finding the optimal set of concentric circular antenna array

In this paper the maximum sidelobe level (SLL) reductions, optimal beam patterns and optimal beam widths of various designs of three-ring planar concentric circular antenna arrays (PCCAA) are examined using three different classes of evolutionary optimization techniques to finally determine the global optimal three-ring PCCAA design and then establish some sort of ranking among the techniques. Apart from physical construction of a PCCAA, one may broadly classify its design into two major categories: uniformly excited arrays and non-uniformly excited arrays. The present paper assumes non-uniform excitations and uniform spacing of excitation elements in each three-ring PCCAA design and a design goal of maximizing SLL reduction associated with optimal beam patterns and beam widths. The design problem is modeled as an optimization problem for each PCCAA design and solved using different evolutionary optimization techniques to determine an optimum set of normalized excitation weights for PCCAA elements, which, when incorporated, results in a radiation pattern with optimal (maximum) SLL reduction. Among the various PCCAA designs, one which yields the global minimum SLL with global minimum first null beamwidth is the global optimal design. In this work the three-ring PCCAA containing (N₁ = 4, N₂ = 6, N₃ = 8) elements proves to be such global optimal design. The optimization techniques employed are real coded GA (RGA), canonical PSO (CPSO), craziness based PSO (CRPSO), evolutionary programming (BEP), hybrid evolutionary programming (HEP). While ranking the techniques after 30 total runs for each design, HEP, CRPSO, RGA, CPSO, BGA hold the first five ranks in order of optimization capability. HEP yields global minimum SLL (?32.86 dB) and global minimum BWFN (77.0°) for the optimal design. BEP often changes the rank from second to fifth depending on the design set. Further, when compared to a uniformly excited PCCAA having equal number of elements and same radii a reduction of major lobe beamwidth is also observed in the optimal non-uniformly excited case.