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An ANN Controlled Three-Phase Auto-Tuned Passive Filter for Harmonic and Reactive Power Compensation
Sindhu M.R,Manjula Nair,T.N.P. Nambiar 전력전자학회 2009 JOURNAL OF POWER ELECTRONICS Vol.9 No.3
Automatically tuned passive filters can improve power quality to a great extent in power systems. A novel three-phase shunt auto-tuned filter is designed to effectively compensate source current harmonics and to provide reactive power required by the non-linear load, which draws a highly reactive, harmonic-rich current from the supply. An artificial neural network (ANN) based controller selects filter component values in accordance with reactive power requirement and harmonic compensation. Traditional passive filters are permanently connected to the system and draw large amounts of source current even under light load conditions. By using auto-tuned filters, the passive filter components can be controlled according to load variations and, hence, draw only required source currents. The selection is done by the ANN with the help of a properly tuned knowledge base to provide instantaneous compensation using a digital controller.
Enhance Health Risks Prediction Mechanism in the Cloud Using RT-TKRIBC Technique
Konduru, Venkateswara Raju,Bharamgoudra, Manjula R The Korea Institute of Information and Commucation 2021 Journal of information and communication convergen Vol.19 No.3
A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.
( Uma L. K. Selvaraj ),( R. Manjula ),( G. Subramanian ),( Sanjay Nagarkar ) 한국미생물 · 생명공학회 2002 Journal of microbiology and biotechnology Vol.12 No.4
The current study demonstrates the ability of the marine cyanobacterium Oscillatoria willei BDU130511 to disinfect raw sewage. Within a holding time of 3 h under laboratory conditions, the organism drastically reduced in the total bacterial and coliform counts at various pH levels, in both unbuffered and buffered sewage, thereby suggesting a potential role for cyanobacteria in wastewater treatment.
Performance Evaluation and Analysis of Parallel Computers Workload
M.Narayana Moorthi,R.Manjula 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.1
The current and future computer industry is changing from single core CPU to Multicore Processors. The next generation computing systems are focusing on parallel computing to solve the problem in fast and high speed using parallel programming concepts. By running more than one task at the same time with multiple processors concurrently or parallel we can achieve high speed in our computing applications. Here the performance improvement is measured in terms of increase in the number of cores per machine and is analyzed for better optimal work load balance. The parallel computers follow different workload scheduler. In this paper we investigate how to tune the performance of threaded applications with balanced load for each core or processor. The focus here is the process of comparative analysis of single core and multicore systems to run an application program for faster execution time and optimize the scheduler for better performance.
Comparison of meat quality traits in muscovy ducks reared under two different management systems
( Maduki D. Umagiliya ),( Niranga Bandara ),( Dinesh D. Jayasena ),( Shemil P. Macelline ),( Shan R. Nawarathne ),( Prabuddha Manjula ) 한국축산학회 2022 축산기술과 산업 Vol.9 No.2
This study was designed to compare the meat quality traits in Muscovy ducks raised under extensive and semi-intensive management systems. Nine female birds from each management system were randomly selected and slaughtered at 18 wk of age. Meat samples were obtained from both breast (Pectoralis major) and thigh (Biceps femoris) meats and analyzed for physicochemical traits (color, pH, water holding capacity, cooking loss, and proximate analysis) and sensory properties. Results revealed that the ducks reared under the semi-intensive system had a significantly higher live weight than extensively reared ducks (p = 0.01). In contrast, ducks from the extensive system showed significantly higher (p < 0.05) relative weights for thigh and giblet. A higher crude fat content, water holding capacity, and a lower pH in meat were reported in ducks from the semi-intensive system compared to those from the extensive system (p < 0.05). Breast meat from semi-intensively reared ducks showed higher redness, WHC, and relative fat content than the extensive system (p < 0.05). However, the rearing system had no significant effect (p > 0.05) on meat lightness. Results of the sensory analysis revealed that meat from the extensive management system had higher scores for taste, odor, flavor, juiciness, tenderness, and overall acceptability, irrespective of the meat cut (p < 0.05). In conclusion, physiochemical traits in thigh and breast meats were significantly influenced (p < 0.05) by the management systems in Muscovy ducks.