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An Energy Consumption Prediction Model for Smart Factory Using Data Mining Algorithms
Sathishkumar V E,이명배,임종현,김유빈,신창선,박장우,조용윤 한국정보처리학회 2020 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.9 No.5
Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.
Predicting Daily Nutrient Water Consumption by Strawberry Plants in a Greenhouse Environment
( Sathishkumar V E ),( Myeong-bae Lee ),( Jong-hyun Lim ),( Chang-sun Shin ),( Chang-woo Park ),( Yong Yun Cho ) 한국정보처리학회 2019 한국정보처리학회 학술대회논문집 Vol.26 No.2
Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.
Sathishkumar, Natarajan,Sathiyamoorthy, Subramaniyam,Ramya, Mathiyalagan,Yang, Dong-Uk,Lee, Hee Nyeong,Yang, Deok-Chun Informa Healthcare 2012 Journal of enzyme inhibition and medicinal chemist Vol.27 No.5
<P>Anti-apoptotic proteins such as BCL-2, BCL-XL and MCL-1 bind with pro-apoptotic proteins to induce apoptosis mechanism. BCL-2 family proteins are key regulators of apoptosis process. Over expression of these anti-apoptotic proteins lead to several cancers by preventing apoptosis. A number of studies revealed that ginseng derivatives reduce tumor growth. Ginseng, the most valuable medicinal herb found in eastern Asia belongs to Araliaceae family. In this study, docking simulations were performed for anti-apoptotic proteins with several ginsenosides from Panax ginseng. Our finding shows ginsenosides Rf, Rg1, Rg3 and Rh2 have more binding affinity with BCL-2, BCL-XL and MCL-1 and other ginsenosides also interact with each anti-apoptotic proteins. Therefore, ginseng derivatives represent a novel class of potent inhibitors and could be used for cancer chemotherapy.</P>
Sathishkumar Chinnasamy,Sivasubramanian Ramanathan 한양대학교 세라믹연구소 2020 Journal of Ceramic Processing Research Vol.21 No.1
In this work, ruthenium(II)bis(2,2'-bipyridyl4,4'-dicarboxylicacid)(pyrazine)bis(tetrafluroborate) [Ru(II)(dcbpy)2(pyz)]n(BF4)2n]based metal organic polymer (RuMOP-Pyz-1), was synthesized at room temperature under inert atmosphere and theirperformance in dye-sensitized solar cells (DSSC) were studied. The metal organic polymer was prepared by coupling pyrazineas linker units with the Ru(II) dicarboxylated based mono metallic complex. The UV-visible absorption profiles covered abroad range of absorption and the formation of polymer leads to shift in the absorption wavelength. The metal mediated π-conjugation units in the polymer complex exhibit a strong emission at λem = 552 nm with an excitation wavelength of 395 nm. The synthesized metallo-polymer was employed as a sensitizer in DSSC and the maximum power conversion efficiency (PCE)value of ɳ = 1.507 % with short circuit current (Jsc) of 3.22 mA/cm2; open circuit voltage (Voc) 0.684 V and Fill Factor (FF)of 68.45 % under Air Mass (AM) 1.5 G simulated sunlight at a light intensity of 100 mW/cm2 was obtained. The efficiencyof the device and its photovoltaic performances were found to be satisfactory. The enhanced performance of the device isattributed to the presence of extended conjugation of the metallo polymer which helps in a facile electron transfer from theHOMO to LUMO of TiO2.
Sathishkumar Natarajan,Senthil Kumar Thamilarasan,Jong-In Park,Hee-Jeong Jung,Ill-Sup Nou 한국육종학회 2014 한국육종학회 심포지엄 Vol.2014 No.07
Cabbage (Brassica oleracea) is one of the most important vegetable crops in the world. Yet, its sensitivity to cold stress, especially at the seedling stage, could limit the production. Until now, only, few studies about heritably durable cold tolerance were carried out in cabbage. Hence this study was done to characterize the transcriptome profiles of two cabbage genotypes with contrasting responses to cold stress using Illumina Hiseq short read (paired-end) sequencing technology. MicroRNAs (miRNAs) represent a class of short, non-coding, endogenous RNAs which play important roles in post-transcriptional regulation of gene expression. Thisstudy,wesoughttoprovideamorecomprehensivepredictionofB. oleracea cold responsive miRNAs based on high through put sequencing using two contrasting genotypes. The raw sequences were processed for removal of poor-quality and adaptor sequences. Then, the high quality unigenes (58,094) reads were applied for length filtering. Then, unigenes reads were used in a BLASTN search against of Rfam database and known miRNA database (miRBase 18.0) to removal of non-coding RNA’s and identifies conserved miRNA’s in B. oleracea. Further, novel reads were searched against B. oleracea genome. Their flanking sequences in the genome were used to predict their secondary structures, target prediction, and functional analysis. This is first report to identify novel miRNAs for cold stress through high throughput techniques. Our findings will provide an overview of potential miRNAs involved in cold stress, which may provide important clues on the function of miRNAs in from B. oleracea and other closely related Brassica species.
Green fabrication of zirconia nano-chains using novel Curcuma longa tuber extract
Sathishkumar, M.,Sneha, K.,Yun, Y.S. North-Holland 2013 Materials letters Vol.98 No.-
Bioreduction using plant materials for nanoparticle synthesis is a well known technique, but application of plant materials for hydrolytic process to synthesize nanoparticles has not much attempted earlier. One such unexplored natural material consisting of a wide range of organics is Curcuma longa tuber, which was tested for its potential use in the synthesis of zirconia nanoparticles (ZrNP). The linear chains of ZrNP formed were with an average size in the range of 41-45nm. Water soluble organics that were present in the plant materials were mainly responsible for hydrolysis of ZrF<SUB>6</SUB><SUP>2-</SUP> ions to ZrNP. The production of ZrNP was better at higher pH values. The zeta potential studies showed that the surface charge of the formed nanoparticles was highly negative and increased with increasing pH.