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RISS 인기검색어
- 검색키워드
- 저자 : M.Anusha (검색결과 3 건)
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IoT-Based Automatic Water Quality Monitoring System with Optimized Neural Network
Anusha Bamini A M,Chitra R,아가왈 사우랍,김현성,Punitha Stephan,Thompson Stephan 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.1
One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.
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N. Bindushree,A. Dhabale,M. S. Dhanush,A. Honakeri,A. Ankit,M. K. Anusha,R. Kumar,H. K. Choudhary,V. Khopkar,K. Chandra Sekhar,B. Sahoo 대한금속·재료학회 2020 ELECTRONIC MATERIALS LETTERS Vol.16 No.6
We report the method of tuning the thermal conductivity through the composition of multiwall carbon nanotube (MWCNT)dispersed ethylene glycol based nanofluids. The structure and properties of the MWCNTs were characterized by scanningelectron microscopy, transmission electron microscopy, X-ray diffraction, Raman spectroscopy and thermogravimetric analysis. A parallel plate thermal conductivity (PPTC) set up was fabricated and used for measurement of thermal conductivityof the nanofluids. We have prepared ethylene glycol based nanofluids containing 0.05, 0.1, 0.15, 0.20, 0.25 and 0.35 wt%of MWCNTs. The thermal conductivities of these fluids were measured by keeping them between the two (parallel) plates,referred as the hot and the cold plates, of the sample holder in the PPTC apparatus. The lower plate was water-cooled andthe upper plate was heated. The temperature of the hot plate was varied between 35 and 80 °C. The thermal conductivityof the fluids was calculated using the one-dimensional heat conduction equation. According to our observation, an efficientheat transfer occurs through the nanofluids with an optimum concentration of 0.20 wt% of CNTs. Our work demonstrates the importance of the composition of the nanofluids and their structural defects in heat transfer.
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Prognostication of Climate Using Sliding Window Algorithm
D.V.N. Koteswara Rao,M.Anusha,P. Nagendra Babu,M. Divya Sri,N.Rajesh,K. Sandeep Kumar 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.4
Weather forecasting is the task of determining future state of the atmosphere. To predict the future’s weather condition, the variation in the conditions in past years must be utilized. The probability that the weather condition of the day in consideration will match the same day in previous year is very less. But the probability that it will match within the span of adjacent sixty days of previous year is very high. A Sliding window algorithm is emerging as a leading methodology for the application of weather prediction. So, the prediction is made based on sliding window algorithm. So, sixty days are considered for previous year a sliding window is selected of size equivalent to fifteen days. Every thirty days of sliding window is then matched with that of current year’s thirty days in consideration. The best matched window is made to participate in the process of predicting weather conditions. The month wise results are being computed for four months to check the accuracy. The experimental results demonstrate that the applied technique gives better predicted weather conditions are quite efficient with an average accuracy of 94.21%.
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