A Novel Artificial Intelligence Based Timing Synchronization Scheme for Smart Grid Applications

The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.

     

Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D¹ and second derivative, D²) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R²?=?0.957 and RMSEP?=?3.386) using MSC, Savitzky–Golay and D¹, compressive energy (R²?=?0.907 and RMSEP?=?15.757) using the combination of MSC, Wavelet and D¹, moisture content (R²?=?0.907 and RMSEP?=?0.179) and apparent modulus of elasticity (R²?=?0.921 and RMSEP?=?2.366) employing combination of SNV, Wavelet and D¹, respectively. Moreover, Vis–NIR data correlated well with hardness (R²?=?0.876 and RMSEP?=?5.216) using SNV, Wavelet and D². These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels.     

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.

     

Thermal,electrical, and mechanical properties of tween 80/span 80–based organogels and its application in iontophoretic drug delivery

The present study describes the preparation and characterization of the Tween 80/Span 80 and sunflower oil–based organogels. Organogels were characterized using microscopy, X‐ray diffraction, thermal, mechanical, and electrical techniques. The properties were found to be dependent on the proportion of the water : surfactant mixture. The in vitro drug release studies were performed under electrical potential. The drug release in the presence of electrical current was compared with the passive drug release. The drug release from the organogels followed the zero‐order kinetics suggesting diffusion mediated release. The preliminary results suggested that the organogels may be used as drug carriers in iontophoretic drug delivery. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41419.     

A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison

This article uses an integrated methodology based on a chi-squared automatic interaction detection (CHAID) model combined with analytic hierarchy process (AHP) for pair-wise comparison to assess medium-scale landslide susceptibility in a catchment in the Inje region of South Korea. An inventory of 3596 landslide locations was collected using remote sensing, and a random sample comprising 30% of these was used to validate the model. The remaining portion (70%) was processed by the nearest-neighbour index (NNI) technique and used for extracting the cluster patterns at each location. These data were used for model training purposes. Ten landslide-conditioning factors (independent variables) representing four main domains, namely (1) topology, (2) geology, (3) hydrology, and (4) land cover, were used to produce two landslide-susceptibility maps. The first landslide-susceptibility map (LSM1) was produced by overlaying the terminal nodes of the CHAID result tree. The second landslide-susceptibility map (LSM2) was produced using the overlay result of AHP pair-wise comparisons of CHAID terminal nodes. The prediction rate curve results were better with LSM2 (area under the prediction curve (AUC) = 0.80) than with LSM1 (AUC = 0.76). The results confirmed that the integrated hybrid model has superior prediction performance and reliability, and it is recommended for future use in medium-scale landslide-susceptibility mapping.     

Near-field radiative cooling of nanostructures

We measure near-field radiative cooling of a thermally isolated nanostructure up to a few degrees and show that in principle this process can efficiently cool down localized hotspots by tens of degrees at submicrometer gaps. This process of cooling is achieved without any physical contact, in contrast to heat transfer through conduction, thus enabling novel cooling capabilities. We show that the measured trend of radiative cooling agrees well theoretical predictions and is limited mainly by the geometry of the probe used here as well as the minimum separation that could be achieved in our setup. These results also pave the way for realizing other new effects based on resonant heat transfer, like thermal rectification and negative thermal conductance.     

Band-to-band registration model for near-equatorial Earth observation satellite images with the use of automatic control point extraction

The near-equatorial orbit (NEqO) image bands have large differences in distortion, scaling, and skewing. Therefore band-to-band registration of a near-equatorial orbit image is challenging. This study thus proposes a technique to overcome the band-to-band registration of NEqO image bands. The proposed method involves the conversion of image bands into greyscale, followed by image compression and automatic control point (CP) generation through scale-invariant feature transform. CPs are used to perform registration with both first- and second-order polynomials and spline transformation to correct the misregistration between NEqO image bands. The images used in this study are from the Malaysian RazakSAT satellite. Results show accurate registration performance. Moreover, the root-mean-square errors of the first- and second-order polynomial transformations are 4 and 3 m, respectively. These values are lower than those obtained with the use of spline transformation. Verification is performed by comparing the results of both automatic and manual transformations to the use of polynomial transformation. The results confirm the effectiveness of the proposed algorithm.     

Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning.     

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Engineering with Computers - In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping...