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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.
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>
Reducing the seat vibration of vehicle by semi active force control technique
Sathishkumar. P.,Jancirani. J.,Dennie John 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.2
This article focusses on reducing the axis acceleration and minimizing the vertical displacement by using an air spring actuator and activeforce control as a main control element. In active force control loop track the developed force of an air spring actuator is fed as afeedback to the actuator. Mamdani and sugeno type fuzzy interference system are used to develop a desired force and to estimate mass ofthe system respectively. The performance of the system is analyzed for both time and frequency domains and contrasted with passivesuspension due to the irregular road disturbances. While developing the simulation model, quarter car suspension with seat as three degreeof freedom and an air spring actuator acting as a force generator are modeled as non-linear system. The simulation result shows theeffectiveness of the proposed control scheme in suppressing the undesirable effects of the suspension system.
Hourly Steel Industry Energy Consumption Prediction Using Machine Learning Algorithms
( 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
Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.
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.