Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

Water Resources Management - Reference evapotranspiration (ET0) is a crucial element for deriving irrigation scheduling of major crops. Thus, precise projection of ET0 is essential for better...     

Self and static interference mitigation scheme for coexisting wireless networks

High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.     

Evaluation of Grid-Connected Photovoltaic Plants Based on Clustering Methods

Photovoltaic (PV) systems are electric power systems designed to supply usable solar power by means of photovoltaics, which is the conversion of light into electricity using semiconducting materials. PV systems have gained much attention and are a very attractive energy resource nowadays. The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun. PV systems play an important role in reducing feeder losses, improving voltage profiles and providing ancillary services to local loads. However, large PV grid-connected systems may have a destructive impact on the stability of the electric grid. This is due to the fluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels. Thus, the electrical distribution system with high penetration of PV systems is subject to performance degradation and instabilities. For that, this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants. To accomplish this, an indicative representation of solar irradiation datasets is used for planning and power flow studies of the electric network prior to PV systems installation. Those datasets contain lengthy historical observations of solar energy data, that requires extensive analysis and simulations. To overcome that the lengthy historical datasets are reduced and clustered while preserving the original data characteristics. The resultant clusters can be utilized in the planning stage and simulation studies. Accordingly, studies related to PV systems integration into the electric grid are conducted in an efficient manner, avoiding computing resources and processing times with easier and practical implementation.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Application of artificial intelligence to improve quality of service in computer networks

Resource sharing between book-ahead (BA) and instantaneous request (IR) reservation often results in high preemption rates for ongoing IR calls in computer networks. High IR call preemption rates cause interruptions to service continuity, which is considered detrimental in a QoS-enabled network. A number of call admission control models have been proposed in the literature to reduce preemption rates for ongoing IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an artificial neural network (ANN) model to dynamically control the preemption rate of ongoing calls in a QoS-enabled network. The model maps network traffic parameters and desired operating preemption rate by network operator providing the best for the network under consideration into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired operating preemption rates. Simulation results show that the preemption rate attained by the model closely matches with the target rate.     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

A ROBUST MART ALGORITHM FOR TOMOGRAPHIC APPLICATIONS

The present work is concerned with the development of a robust reconstruction algorithm for applications involving tomography. In an earlier study it was shown that among the ART family of algorithms, the multiplicative algebraic reconstruction algorithm (MART) was the most appropriate for tomographic reconstruction [1]. In the present work, the MART algorithm has been extended so that (a) its performance is now acceptable over a wider range of relaxation factors, (b) the time requirement for convergence to a solution is lower, and (c) its performance is less sensitive to noise in the projection data. Applications considered for evaluating the proposed algorithms are (1) a circular region with holes, (2) a three-dimensional temperature field in a differentially heated fluid layer, and (3) experimental data recorded in a differentially heated fluid layer using an interferometer. The proposed algorithms are seen to be an improvement over those presently available, for all three examples considered.     

Isolation and characterization of the Shiga toxin gene (stx)-bearing Escherichia coli O157 and non-O157 from retail meats in Shandong Province, China, and characterization of the O157-derived stx2 phages

Infection by Shiga toxin (Stx)-producing Escherichia coli of non-O157 and O157 serotypes are rare in China, but infection by O157 serotype was found in Shandong Province and three other provinces in China. To understand the reason for these rare infections and to determine the safety of retail meats in Shandong Province, we examined the distribution of Shiga toxin gene (stx)-bearing E. coli in retail meats and characterized the isolated stx-bearing strains. We used hybridization with DNA probes and isolated stx1- and/or stx2-positive E. coli from 31 (58%) of 53 retail meat samples, with beef showing the highest frequency (68%). Of 42 stx-positive isolates, none belonged to O157. Using the O157-specific immunomagnetic bead technique, we isolated E. coli O157 carrying the eae and stx2 genes from eight beef samples (26%). These strains produced little or no Stx2 and carried a unique q gene. Replication of the stx2 phages was detected in these strains, whereas stx2 phage replication was not detected in our previous study in which we examined similar stx2-bearing E. coli O157 strains from other Asian countries. Analysis of E. coli C600 lysogenized with the stx2 phages found in this study suggests that the lack of Stx2 production is due to changes in non-q gene region(s) of the phage genome or chromosomal mutation(s) in the host. Our data and reports by other workers suggest it is necessary to determine if various stx2-bearing E. coli O157 strains producing Stx2 to varying degrees are distributed in meats in various locations in China.     

Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging

The purpose of this study was to develop and test a hyperspectral imaging system (900–1700 nm) to predict instrumental and sensory tenderness of lamb meat. Warner–Bratzler shear force (WBSF) values and sensory scores by trained panellists were collected as the indicator of instrumental and sensory tenderness, respectively. Partial least squares regression models were developed for predicting instrumental and sensory tenderness with reasonable accuracy (R_cv = 0.84 for WBSF and 0.69 for sensory tenderness). Overall, the results confirmed that the spectral data could become an interesting screening tool to quickly categorise lamb steaks in good (i.e. tender) and bad (i.e. tough) based on WBSF values and sensory scores with overall accuracy of about 94.51% and 91%, respectively. Successive projections algorithm (SPA) was used to select the most important wavelengths for WBSF prediction. Additionally, textural features from Gray Level Co-occurrence Matrix (GLCM) were extracted to determine the correlation between textural features and WBSF values.