http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : SANJEEVI, C. B (검색결과 10 건)
- 무료
- 기관 내 무료
- 유료
-
-
-
-
Optimization of RSW process parameters on tensile strength by taguchi method
S. Raja,K. Vignesh,M. Ravikumar,R. Sanjeevi 한양대학교 청정에너지연구소 2023 Journal of Ceramic Processing Research Vol.24 No.1
The aim of this research is to improve the Resistance Spot Welding (RSW) process characteristics of AISI SS316L joints ontensile shear load (TSL). The RSW process parameters like electrode diameter of 6mm to 8mm, welding current of 7 kA to9 kA, heating cycle periods of 7 to 9 are selected. The 2 mm sheet thickness has been selected for the welding trials and tensileshear test has been involved for all the trials for the purpose of identifying the TSL. Taguchi optimization technique is usedfor design the experiment with the help of Minitab software. It is observed that maximum TSL of 16 kN is observed by settingthe parameters at 6mm electrode diameter, 9 kA welding current and heating cycles of 9. The ANOVA table confirms thatwelding current majorly affects the quality of weld and all the trials are observed with interfacial failure.
-
P. Shanmugam,Yu Hwan Ahn,S. Sanjeevi,A. S. Manjunath 大韓遠隔探査學會 2003 大韓遠隔探査學會誌 Vol.19 No.5
As the launches of a series of remote sensing satellites, there are various multiresolution and multi-spectral images available nowadays. This diversity in remotely sensed image data has created a need to be able to integrate data from different sources. The C-band imaging radar of ERS-2 due to its high sensitivity to coastal wetlands holds tremendous potential in mapping and monitoring coastal wetland features. This paper investigates the advantages of using ERS-2 SAR data combined with IRS-1D LISS-3 data for mapping complex coastal wetland features of Tamil Nadu, southern India. We present a methodology in this paper that highlights the mapping potential of different combinations of filtering and integration techniques. The methodology adopted here consists of three major steps as following: (i) speckle noise reduction by comparative performance of different filtering algorithms, (ii) geometric rectification and coregistration, and (iii) application of different integration techniques. The results obtained from the analysis of optical and microwave image data have proved their potential use in improving interpretability of different coastal wetland features of southern India. Based visual and statistical analyzes, this study suggests that brovey transform will perform well in terms of preserving spatial and spectral content of the original image data. It was also realized that speckle filtering is very important before fusing optical and microwave data for mapping coastal mangrove wetland ecosystem.
-
Palanisamy Shanmugamt,Yu Hwan Ahn,Shanmugam Sanjeevi 大韓遠隔探査學會 2005 大韓遠隔探査學會誌 Vol.21 No.3
This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D USS-Ⅲ and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-Ⅲ and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their end-members. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated (r2=0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated (r2=0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.
-
Shanmugam, Palanisamy,Ahn, Yu-Hwan,Sanjeevi , Shanmugam The Korean Society of Remote Sensing 2005 大韓遠隔探査學會誌 Vol.21 No.3
This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.
-
Shanmugam, P.,Ahn, Yu-Hwan,Sanjeevi, S.,Manjunath, A.S. The Korean Society of Remote Sensing 2003 大韓遠隔探査學會誌 Vol.19 No.5
As the launches of a series of remote sensing satellites, there are various multiresolution and multi-spectral images available nowadays. This diversity in remotely sensed image data has created a need to be able to integrate data from different sources. The C-band imaging radar of ERS-2 due to its high sensitivity to coastal wetlands holds tremendous potential in mapping and monitoring coastal wetland features. This paper investigates the advantages of using ERS-2 SAR data combined with IRS-ID LISS-3 data for mapping complex coastal wetland features of Tamil Nadu, southern India. We present a methodology in this paper that highlights the mapping potential of different combinations of filtering and integration techniques. The methodology adopted here consists of three major steps as following: (i) speckle noise reduction by comparative performance of different filtering algorithms, (ii) geometric rectification and coregistration, and (iii) application of different integration techniques. The results obtained from the analysis of optical and microwave image data have proved their potential use in improving interpretability of different coastal wetland features of southern India. Based visual and statistical analyzes, this study suggests that brovey transform will perform well in terms of preserving spatial and spectral content of the original image data. It was also realized that speckle filtering is very important before fusing optical and microwave data for mapping coastal mangrove wetland ecosystem.
-
Association of SUMO4 M55V Polymorphism with Autoimmune Diabetes in Latvian Patients
SEDIMBI, S. K,SHASTRY, A.,PARK, Y.,RUMBA, I.,SANJEEVI, C. B Wiley (Blackwell Publishing) 2006 Annals of the New York Academy of Sciences Vol.1079 No.1
<P>Small ubiquitin-related modifier (SUMO4), located in IDDM5, has been identified as a potential susceptibility gene for type 1 diabetes mellitus (T1DM). The novel polymorphism M55V, causing an amino acid change in the evolutionarily conserved met55 residue has been shown to activate the nuclear factor kappaB (NF-kappaB), hence the suspected role of SUMO4 in the pathogenicity of T1DM. The M55V polymorphism has been shown to be associated with susceptibility to T1DM in Asians, but not in Caucasians. Latent autoimmune diabetes in adults (LADA) is a slowly progressive form of T1DM and SUMO4 M55V has not been studied in LADA to date. The current study aims to test whether Latvians are similar to Caucasians in susceptibility to autoimmune diabetes (T1DM and LADA), with respect to SUMO4 M55V. We studied, age- and sex-matched, Latvian T1DM patients (n = 100) and healthy controls (n = 90) and LADA patients (n = 45) and healthy controls (n = 95). SUMO4 M55V polymorphism was analyzed using polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP). The allelic frequencies of the A and G alleles were compared with HLA DR3-DR4-positive and HLA DR3-DR4-negative patients to identify any potential relation between HLA DR3-DR4 and SUMO4 M55V. We found no significant association between SUMO4 M55V and T1DM susceptibility in Latvians, the results being in concurrence with the previous studies in Caucasians of British and Canadian origin. Comparison of the A and G alleles with HLA DR3-DR4 did not result in any significant P values. No significant association was found between SUMO4 M55V and LADA. SUMO4 M55V is not associated with susceptibility to T1DM and LADA in Latvians, and Latvians exhibit similarity to other Caucasians with respect to association of SUMO4 M55V with autoimmune diabetes.</P>
-
Predominance of the Group A Killer Ig-Like Receptor Haplotypes in Korean Patients With T1D
PARK, Y.,CHOI, H.,PARK, H.,PARK, S.,YOO, E.-K.,KIM, D.,SANJEEVI, C. B Wiley (Blackwell Publishing) 2006 Annals of the New York Academy of Sciences Vol.1079 No.1
<P>Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease in which pancreatic beta cells are selectively destroyed. Although autoimmune diseases are driven by inappropriate adaptive immunity, innate immunity may play a role in the development of T1D. To study the potential involvement of innate immunity in the pathogenesis of autoimmune disease, we investigated associations of the genes for 14 different killer Ig-like receptors (KIRs), the well-characterized receptors in natural killer cells, with Korean T1D patients. Genetic association analyses revealed that some of the KIR genes were associated with T1D. KIR2DL5 and 2DS2 genes were present at significantly low frequency in Korean T1D patients (P < 10(-4)). We did not detect any influence of ligand distribution on KIR association. With the haplotype assignments, 53% of the KIR haplotypes in the control are of type A. Compared with the control (P < 10(-3)) and autoantibody-negative patients (P < 10(-2)), the group A haplotype predominates in Korean patients with T1D. The KIR gene is associated with T1D and distribution differences between T1D and controls were not influenced by the HLA genes (DR-DQ-A-C). T1D, at least in Koreans, is associated with KIR genes, especially in the group A KIR haplotypes. There is a close relationship between innate and adaptive immunity.</P>
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
