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RISS 인기검색어
- 검색키워드
- 저자 : ( Niraj Tamrakar ) (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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A Comparison of Recurrent Neural Network for Forecasting Short Term Solar Irradiance
( Niraj Tamrakar ),( Jayanta Kumar Basak ),( Bhola Paudel ),( Nibas Chandra Deb ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.1
Efficient and timely supply of renewable energy relies heavily on accurate solar irradiance forecasting. The study aims to develop reliable short-term solar irradiance prediction models with a 5-minute time interval, using five different variants of recurrent neural networks (RNN). These models include long-short term memory (LSTM), gated recurrent unit (GRU), simple RNN, bidirectional LSTM (Bi-LSTM), and bidirectional GRU (Bi-GRU). The first three models are unidirectional, while the last two are bidirectional RNNs. The dataset used in this study spans 26 months of highly volatile weather conditions in Jinju city, South Korea. To achieve effective results, careful experimentation and selection of five hyper-parameters for each model were conducted. Additionally, the models were tested with varying levels of depth and width, and evaluated using a 9-fold cross-validation method to account for the high variability in the seasonal time-series dataset. Notably, the Bi-GRU model produced the lowest root mean square error (RMSE) and the highest R2 values of 46.1 and 0.958, respectively, and also incurred the lowest computational cost at 5.25*105 seconds per trainable parameter per epoch. In the 9-fold cross-validation test, all five models showed different performances across the four seasons, but on average, the bidirectional RNNs and the simple RNN model demonstrated high robustness with less data and high temporal data variability. However, the stronger architectures of the bidirectional models make their results more reliable.
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Classification of Pig Behaviour Experiencing Impaired Air Quality Due to Elevated GHG Concentration
( Niraj Tamrakar ),( Jayanta Kumar Basak ),( Nibas Chandra Deb ),( Sijan Karki ),( Myeongyong Kang ),( Daeyeong Kang ),( Seong Woo Jeon ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2
Pig welfare and health are the primary concerns in livestock management. Pig behaviors serve as important early indicators of pig stress. This research utilizes AI-based computer vision with a top-view RGB camera to monitor the behavior of pigs. The pigs were detected in an experimental pig barn using a CNN-based deep learning model, and pig motions were tracked in real-time using the Deep SORT algorithm. This approach helps segment different pig postures and assigns an activity score based on activity tracking. Various CNN-based deep learning models (Yolov7, Faster R-CNN, SSD ResNet101) were implemented to detect pigs in different postures. When comparing different pig identification and posture detection models, Yolov7 was found to be the fastest and most accurate, with a mean average precision (MAP) of 97.84%. Similarly, the accuracy of multiple object tracking was 93.2%, and the precision was 81.4% for the tracking algorithm. The study observed behavioral changes in both groups and individuals due to the natural elevation of GHG concentration in the experimental pig barn. Higher GHG concentrations were found to have a negative correlation with pig postures like standing, walking, and sternal lying activities, whereas lateral lying had a positive correlation.
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Machine learning approaches in modeling of digestible energy demand in mature phase of swine
( Nibas Chandra Deb ),( Jayanta Kumar Basak ),( Sijan Karki ),( Daeyeong Kang ),( Niraj Tamrakar ),( Eun-wan Seo ),( Seong Woo Jeon ),( Hyeon Tae Kim ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2
Digestible energy (DE) is an essential component of swine production, required to maintain proper growth, and reproducibility of swine. Therefore, the objective of this study was to evaluate the DE demand in mature phase of pigs by proving different types of feed in different pig barns during 2021 and 2022. A load cell and a livestock environment management system (LEMS) were used to measure the body weight of swine’s, room temperature, and humidity level inside the barn, respectively. A total of four ML models (MLR, SVR, RFR, and MLP) were used to forecast the DE demand, with the input parameters of room temperature, humidity, age, and body mass of swine's. The result of the study showed that age and body mass were positively correlated with DE (r>0.90). It was revealed that RFR model provided the best result (R2>0.94) compared to other models. However, MLR model provided the worst result (R2<0.94) compared to other models. Additionally, sensitivity analysis indicated that body mass had the greatest impact on forecasting DE compared to other input parameters. Finally, the study colcluded that RFR model can predict DE accurately, which can assist farmer's to proper management of DE in mature phase of swine production.
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