Experimental Study on Air Decomposition By-Product Under Creepage Discharge Fault and Their Impact on Insulating Materials

Javed, Hassan,LI, Kang,Zhang, Guoqiang,Plesca, Adrian Traian The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.6

Creepage discharge faults in air on solid insulating material play a vital role in degradation and ageing of material which ultimately leads to breakdown of power equipment. And electric discharge decompose air in to its by-products such as Ozone and $NO_x$ gases. By analyzing air decomposition gases is a potential method for fault diagnostic in air. In this paper, experimental research has been conducted to study the effect of creepage discharge on rate of generation of air decomposition by-products using different insulating materials such as RTV, epoxy and fiberglass laminated sheet. Moreover XRF analysis has been done to analyze creepage discharge effect on these insulating materials. All experiments have been done in an open air test cell under constant temperature and pressure conditions. While analysis has been made for low and high humidity conditions. The results show that the overall concentration of air decomposition by-products under creepage discharge in low humidity is 4% higher than concentration measured in high humidity. Based on this study a mathematical relationship is also proposed for the rate of generation of air decomposition by-products under creepage discharge fault. This study leads to indirect way for diagnostic of creepage discharge propagation in air.

Differential antioxidative and biochemical responses to aluminium stress in Brassica juncea cultivars

Javed Ahmad,Mohd Affan Baig,Arlene Asthana Ali,Asma Abdulkareem Al‑Huqail,Mohamed Mohamed Ibrahim,Mohammad I rfan Q ureshi 한국원예학회 2018 Horticulture, Environment, and Biotechnology Vol.59 No.5

Aluminium (Al) toxicity in acidic soils limits crop production worldwide. We evaluated eleven genotypes of Brassica juncea (Mustard) under Al stress on basis of their growth and shortlisted two best among them for further comparative analysis. Our objective was to elucidate individual and differential oxidative damage and defence response elicited by Al treatment in selected mustard genotypes, ‘Pusa Tarak’ and ‘Pusa Vijay’. Thirty-day-old plants of both genotypes were subjected to Al stress for a period of 24 h and 72 h. Concentration of superoxides was visible much higher in leaves of ‘Pusa Vijay’ both at 24 h and 72 h, also confirmed by oxidative stress marker thiobarbituric acid reactive substances (TBARS). The activities of the enzymatic antioxidants superoxide dismutase (SOD), ascorbate peroxidase (APX), glutathione reductase (GR), glutathione S-transferase (GST), and catalase (CAT) were significantly higher in ‘Pusa Tarak’ compared to control and ‘Pusa Vijay’ at both time stages. Levels of non-enzymatic antioxidants glutathiones and ascorbates were already higher in ‘Pusa Vijay’; however, Al treatment increased their levels in both genotypes with non-significant changes on inter-genotypes basis. More and significant decline by Al in chlorophylls was observed in ‘Pusa Vijay’. Interestingly, increase in proline content by Al was much prominent in ‘Pusa Tarak’ compared to ‘Pusa Vijay’. The in vitro antioxidant capacity estimation of mustard genotypes, evaluated by measuring 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging and hydroxyl radical scavenging (HRSA) activities proved that extract of ‘Pusa Tarak’ can detoxify more radicals than ‘Pusa Vijay’. We conclude that ‘Pusa Tarak’ can upregulate cellular antioxidants and osmoregulation, and quench more radicals compared to ‘Pusa Vijay’. The results will contribute for selection of better Brassica genus to be grown in Al rich acidic soils, and help in designing strategies for crop breeding and cultivation.

High performance corrosion resistant polyaniline/alkyd ecofriendly coatings

Javed Alam,Ufana Riaz,Sharif Ahmad 한국물리학회 2009 Current Applied Physics Vol.9 No.1

The growing environmental concerns have led to the formulation of new coating strategies by employing inherently conductive polymers (ICPs) as a key component in order to eliminate the toxic heavy metals from protective coatings. Also, the renewable resources are given increasing priority within chemical industry and the energy community. Emphasis is therefore, being laid on the production and processing of polymers from renewable resources which show advantages when compared with petrochemical raw materials and are regarded as an ideal raw material. The present work reports the investigations on the corrosion-resistance performance of soya oil alkyd, containing nano polyaniline (PANI) against mild steel (MS). The corrosion-protective performance was evaluated in terms of physicomechanical properties, corrosion rate, open circuit potential measurements (OCP) and scanning electron microscopy (SEM) studies. The performance was compared to the reported PANI coatings. The growing environmental concerns have led to the formulation of new coating strategies by employing inherently conductive polymers (ICPs) as a key component in order to eliminate the toxic heavy metals from protective coatings. Also, the renewable resources are given increasing priority within chemical industry and the energy community. Emphasis is therefore, being laid on the production and processing of polymers from renewable resources which show advantages when compared with petrochemical raw materials and are regarded as an ideal raw material. The present work reports the investigations on the corrosion-resistance performance of soya oil alkyd, containing nano polyaniline (PANI) against mild steel (MS). The corrosion-protective performance was evaluated in terms of physicomechanical properties, corrosion rate, open circuit potential measurements (OCP) and scanning electron microscopy (SEM) studies. The performance was compared to the reported PANI coatings.

Anaerobic biodegradation of Benzene, Toluene, Ethylbenzene and Xylene (BTEX) and changes in microbial community by TRFLP analysis

Javed H. Niazi,Sun-Mi Lee,Byoung-In Sang 한국생물공학회 2007 한국생물공학회 학술대회 Vol.2007 No.4

Macroeconomic Response to Oil and Food Price Shocks: A Structural VAR Approach to the Indian Economy

Javed Ahmad Bhat,Aadil Ahmad ganaie,Naresh Kumar Sharma 한국국제경제학회 2018 International Economic Journal Vol.32 No.1

The study analyzed the dynamic impact of oil and food price shocks on the macroeconomy of India, using the monthly time series data from April 1994 to May 2016 in a structural vector autoregression (SVAR) framework. Being a net food exporter and net oil importer, the economy is found to face deleterious impacts of global oil and food price shocks on its macroeconomic performance. Output responds negatively to oil and food price hikes along with their volatility and positively to the fall in these prices. Inflation responds positively to all the three transformations of shocks with no signs of coming down, highlighting the price downward inflexibility in India. The study could not establish any evidence of negative demand shocks in face of oil and food price volatility. Central bank responds with a contractionary policy stance to negate the influences of external shocks. Forecast error variance decomposition points out the dominance of external shocks in influencing the domestic variables after their own shocks. Finally, the inflation downward rigidity is observed even in the long run.

Toxicity assays of phthalate compounds using toxicity specific signature bacterial sensors

Javed H. Niazi,Ee Taek Hwang,Byoung-In Sang,Man Bock Gu 한국생물공학회 2007 한국생물공학회 학술대회 Vol.2007 No.10

Background–Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering

Javed, Sajid,Mahmood, Arif,Bouwmans, Thierry,Soon Ki Jung IEEE 2017 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.26 No.12

<P>Background estimation and foreground segmentation are important steps in many high-level vision tasks. Many existing methods estimate background as a low-rank component and foreground as a sparse matrix without incorporating the structural information. Therefore, these algorithms exhibit degraded performance in the presence of dynamic backgrounds, photometric variations, jitter, shadows, and large occlusions. We observe that these backgrounds often span multiple manifolds. Therefore, constraints that ensure continuity on those manifolds will result in better background estimation. Hence, we propose to incorporate the spatial and temporal sparse subspace clustering into the robust principal component analysis (RPCA) framework. To that end, we compute a spatial and temporal graph for a given sequence using motion-aware correlation coefficient. The information captured by both graphs is utilized by estimating the proximity matrices using both the normalized Euclidean and geodesic distances. The low-rank component must be able to efficiently partition the spatiotemporal graphs using these Laplacian matrices. Embedded with the RPCA objective function, these Laplacian matrices constrain the background model to be spatially and temporally consistent, both on linear and nonlinear manifolds. The solution of the proposed objective function is computed by using the linearized alternating direction method with adaptive penalty optimization scheme. Experiments are performed on challenging sequences from five publicly available datasets and are compared with the 23 existing state-of-the-art methods. The results demonstrate excellent performance of the proposed algorithm for both the background estimation and foreground segmentation.</P>

Moving Object Detection in Complex Scene Using Spatiotemporal Structured-Sparse RPCA

Javed, Sajid,Mahmood, Arif,Al-Maadeed, Somaya,Bouwmans, Thierry,Jung, Soon Ki IEEE 2019 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.28 No.2

<P>Moving object detection is a fundamental step in various computer vision applications. <I>Robust principal component analysis</I> (RPCA)-based methods have often been employed for this task. However, the performance of these methods deteriorates in the presence of dynamic background scenes, camera jitter, camouflaged moving objects, and/or variations in illumination. It is because of an underlying assumption that the elements in the sparse component are mutually independent, and thus the spatiotemporal structure of the moving objects is lost. To address this issue, we propose a spatiotemporal structured sparse RPCA algorithm for moving objects detection, where we impose spatial and temporal regularization on the sparse component in the form of graph Laplacians. Each Laplacian corresponds to a multi-feature graph constructed over superpixels in the input matrix. We enforce the sparse component to act as eigenvectors of the spatial and temporal graph Laplacians while minimizing the RPCA objective function. These constraints incorporate a spatiotemporal subspace structure within the sparse component. Thus, we obtain a novel objective function for separating moving objects in the presence of complex backgrounds. The proposed objective function is solved using a linearized alternating direction method of multipliers based batch optimization. Moreover, we also propose an online optimization algorithm for real-time applications. We evaluated both the batch and online solutions using six publicly available data sets that included most of the aforementioned challenges. Our experiments demonstrated the superior performance of the proposed algorithms compared with the current state-of-the-art methods.</P>

Spatiotemporal Low-Rank Modeling for Complex Scene Background Initialization

Javed, Sajid,Mahmood, Arif,Bouwmans, Thierry,Jung, Soon Ki IEEE 2018 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDE Vol.28 No.6

<P>Background modeling constitutes the building block of many computer-vision tasks. Traditional schemes model the background as a low rank matrix with corrupted entries. These schemes operate in batch mode and do not scale well with the data size. Moreover, without enforcing spatiotemporal information in the low-rank component, and because of occlusions by foreground objects and redundancy in video data, the design of a background initialization method robust against outliers is very challenging. To overcome these limitations, this paper presents a spatiotemporal low-rank modeling method on dynamic video clips for estimating the robust background model. The proposed method encodes spatiotemporal constraints by regularizing spectral graphs. Initially, a motion-compensated binary matrix is generated using optical flow information to remove redundant data and to create a set of dynamic frames from the input video sequence. Then two graphs are constructed, one between frames for temporal consistency and the other between features for spatial consistency, to encode the local structure for continuously promoting the intrinsic behavior of the low-rank model against outliers. These two terms are then incorporated in the iterative <I>Matrix Completion</I> framework for improved segmentation of background. Rigorous evaluation on severely occluded and dynamic background sequences demonstrates the superior performance of the proposed method over state-of-the-art approaches.</P>

Analysis of Global Gene Response to Silver-nanoparticles in Yeast by DNA-microarray

Javed H. Niazi,Jung Eui Hong,Byoung-In Sang,Man Bock Gu 환경독성보건학회 2008 한국독성학회 심포지움 및 학술발표회 Vol.- No.-