Approximated fuzzy logic controlled shunt active power filter for improved power quality

Shunt active power filters have been widely used for power quality improvement. With the advancement in artificial intelligence techniques, the applications of fuzzy logic‐based control systems have increased manifolds. This paper proposes a reduced rule fuzzy logic controller (FLC) in the voltage control loop of a shunt active power filter (APF), which is approximating a conventional large rule FLC. The difference between the controlled outputs of two controllers is compensated by proposed compensating factors. The dynamic response and harmonic compensation performance of proposed 4‐rule approximated fuzzy logic controller (AFLC) is compared with 25‐rule FLC. A three‐phase shunt APF is used for harmonic and reactive power compensation. The proposed scheme is tested with randomly varying single and multiple non‐linear loads. The simulation results presented under transient and steady‐state conditions confirm that the proposed 4‐rule AFLC efficiently approximates the 25‐rule FLC. The proposed control methodology takes less computational time and computational memory as the numbers of rules are reduced significantly.     

What kind of #conversation is Twitter? Mining #psycholinguistic cues for emergency coordination

The information overload created by social media messages in emergency situations challenges response organizations to find targeted content and users. We aim to select useful messages by detecting the presence of conversation as an indicator of coordinated citizen action. Using simple linguistic indicators drawn from conversation analysis in social science, we model the presence of coordination in the communication landscape of Twitter¹ using a corpus of 1.5 million tweets for various disaster and non-disaster events spanning different periods, lengths of time, and varied social significance. Within replies, retweets and tweets that mention other Twitter users, we found that domain-independent, linguistic cues distinguish likely conversation from non-conversation in this online form of mediated communication. We demonstrate that these likely conversation subsets potentially contain more information than non-conversation subsets, whether or not the tweets are replies, retweets, or mention other Twitter users, as long as they reflect conversational properties. From a practical perspective, we have developed a model for trimming the candidate tweet corpus to identify a much smaller subset of data for submission to deeper, domain-dependent semantic analyses for the identification of actionable information nuggets for coordinated emergency response.     

Multi-objective optimal reactive power dispatch considering voltage stability in power systems using HFMOEA

This paper proposes a new hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) based approach for solving complex multi-objective, mixed integer nonlinear problems such as optimal reactive power dispatch considering voltage stability (ORPD-VS). In HFMOEA based optimization approach, the two parameters like crossover probability (P_C) and mutation probability (P_M) are varied dynamically through the output of a fuzzy logic controller. The fuzzy logic controller is designed on the basis of expert knowledge to enhance the overall stochastic search capability for generating better pareto-optimal solution. Two detailed case studies are presented: Firstly, the performance of HFMOEA is tested on five benchmark test problems such as ZDT1, ZDT2, ZDT3, ZDT4 and ZDT6 as suggested by Zitzler, Deb and Thiele; Secondly, HFMOEA is applied to multi-objective ORPD-VS problem. In both the case studies, the optimization results obtained from HFMOEA are analysed and compared with the same obtained from two versions of elitist non-dominated sorting genetic algorithms such as NSGA-II and MNSGA-II in terms of various performance metrics. The simulation results are promising and confirm the ability of HFMOEA for generating better pareto-optimal fronts with superior convergence and diversity.     

Evaluation of satellite-altimetry-derived river stage variation for the braided Brahmaputra River

In this study, river stage variation derived from satellite altimetry was used to assess the water level, monthly discharge, and annual water yield at six virtual gauging stations at the braided reaches of the Brahmaputra River. The braided reaches of the river dynamically change their planform, thalweg line, and aggradation or degradation period. Stage records derived from the Envisat satellite of the European Space Agency and Topex/Poseidon of NASA/CNES were used for the period 2002–2010. Spatial interpolation and datum correction were applied on altimetry-derived river stage records before analysis. A correlation and error analysis between the in situ and satellite-altimetry-derived stages was carried out for these stations for both monsoon and non-monsoon seasons. Yearly optical satellite images were used for qualitative assessment of temporal variations in aggradation/degradation phases at the gauging stations. Using the pseudo-rating curve, discharges at two virtual gauging stations were estimated. The results show that the altimetry-estimated discharges are of good agreement with observed discharge for the monsoon months (June–September) as compared with the non-monsoon months (October–May). In order to assess the annual water yield variability, yearly variation in annual water yield from the altimetry data was also estimated and compared to that observed. The estimated annual water yields were 90% accurate. Similarly, the long-term averaged monthly discharge series estimated from satellite altimetry closely follows the temporal trend of that of the observed series.     

Compensation for subpixel roughness effects in thermal infrared images

Emissivity spectra recovered from spectral radiance images may have lowered spectral contrast due to irradiance from nearby surface elements (‘cavity effect’). For analysis based only on photointerpretation or Reststrahlen band identification, it is not always necessary to account for cavity effects, but for full spectral analysis it may be desirable. We present an approach to compensate thermal infrared (TIR) images for cavity radiation. This approach is based on optical estimates of subpixel surface roughness and estimation of cavity contribution for different natural surfaces using a TIR radiosity model. It was tested using tripod-mounted Hyper-Cam (Telops, Inc., Quebec City, Canada) hyperspectral TIR images of natural targets from the Mojave Desert, California, USA, along with centimetre-scale digital elevation models of similar targets measured by ground lidar. For remote subpixel roughness estimation, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) nadir- and aft-looking (27.6°) near-infrared (NIR) brightness ratios, as well as synthetic aperture radar (SAR) images calibrated to roughness root mean square (RMS), were used. The TIR compensation approach is adaptable for different spectral resolutions, including hyperspectral.     

Tropospheric ozone variability over the Indian coastline and adjacent land and sea

A tropospheric ozone variability study is carried out to investigate the spatial and temporal distribution over the coastline of the Indian peninsula and adjacent land and sea using NASA Langley Tropospheric Ozone Residual data set for the period 1979–2005. A strong seasonal cycle has been observed with large variation (～ 55%) over the upper eastern coast, followed by the upper and lower western coast, compared to the lower eastern coast (～ 33%). A negative gradient in ozone concentration is observed along eastern and western coasts during summer (slope ～ –0.78 and –0.65) and a positive gradient (slope ～ 0.16 and 0.21) during winter. The same is observed over the adjacent land and sea along the coastline with slight variation. This change in gradient can be attributed to the anthropogenic emission of precursor gases that reinforce localized photochemical production of ozone. In addition, topography, transport, seasonality of emission of precursor gases and the solar insolation cycle play a vital role.     

A G protein gamma subunit-like domain shared between RGS11 and other RGS proteins specifies binding to Gbeta5 subunits

Regulators of G protein signaling (RGS) proteins act as GTPase-activating proteins (GAPs) toward the alpha subunits of heterotrimeric, signal-transducing G proteins. RGS11 contains a G protein gamma subunit-like (GGL) domain between its Dishevelled/Egl-10/Pleckstrin and RGS domains. GGL domains are also found in RGS6, RGS7, RGS9, and the Caenorhabditis elegans protein EGL-10. Coexpression of RGS11 with different Gbeta subunits reveals specific interaction between RGS11 and Gbeta5. The expression of mRNA for RGS11 and Gbeta5 in human tissues overlaps. The Gbeta5/RGS11 heterodimer acts as a GAP on Galphao, apparently selectively. RGS proteins that contain GGL domains appear to act as GAPs for Galpha proteins and form complexes with specific Gbeta subunits, adding to the combinatorial complexity of G protein-mediated signaling pathways.     

Multiperson interaction recognition in images: A body keypoint based feature image analysis

Most interaction recognition approaches have been limited to single‐person action classification in videos. However, for still images where motion information is not available, the task becomes more complex. Aiming to this point, we propose an approach for multiperson human interaction recognition in images with keypoint‐based feature image analysis. Proposed method is a three‐stage framework. In the first stage, we propose feature‐based neural network (FCNN) for action recognition trained with feature images. Feature images are body features, that is, effective distances between a set of body part pairs and angular relation between body part triplets, rearranged in 2D gray‐scale image to learn effective representation of complex actions. In the later stage, we propose a voting‐based method for direction encoding to anticipate probable motion in steady images. Finally, our multiperson interaction recognition algorithm identifies which human pairs are interacting with each other using an interaction parameter. We evaluate our approach on two real‐world data sets, that is, UT‐interaction and SBU kinect interaction. The empirical experiments show that results are better than the state‐of‐the‐art methods with recognition accuracy of 95.83% on UT‐I set 1, 92.5% on UT‐I set 2, and 94.28% on SBU clean data set.     

An object-based coding scheme for frontal surface of defective fluted ingot

A novel image-based defect identification and coding technique has been proposed for fluted ingots, which are used for the production of locomotive wheels. The edge density map has been used for defect identification and an object-based coding approach has been applied for the storage of defective ingots. The complete scheme has been implemented in one of the integrated steel plants of India.