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نتیجه جستجو - Fuzzy expert systems

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing
ارزیابی تناسب اراضی برای پیش بینی عملکرد از این گونه گیاهان با استفاده از سیستم های خبره فازی جغرافیایی و سنجش از دور-2019
Cassava has the potential to be a promising crop that can adapt to changing climatic conditions in Indonesia due to its low water requirement and drought tolerance. However, inappropriate land selection decisions limit cassava yields and increase production-related costs to farmers. As a root crop, yield prediction using vegetation indices and biophysical properties is essential to maximize the yield of cassava before harvesting. Therefore, the purpose of this research was to develop a yield prediction model based on suitable areas that assess with land suitability analysis (LSA). For LSA, the priority indicators were identified using a fuzzy expert system combined with a multicriteria decision method including ecological categories. Furthermore, the yield prediction method was developed using satellite remote sensing datasets. In this analysis, Sentinel-2 datasets were collected and analyzed in SNAP® and ArcGIS® environments. The multisource database of ecological criteria for cassava production was built using the fuzzy membership function. The results showed that 42.17% of the land area was highly suitable for cassava production. Then, in the highly suitable area, the yield prediction model was developed using the vegetation indices based on Sentinel-2 datasets with 10m resolution for the accuracy assessment. The vegetation indices were used to predict cassava growth, biophysical condition, and phenology over the growing seasons. The NDVI, SAVI, IRECI, LAI, and fAPAR were used to develop the model for predicting cassava growth. The generated models were validated using regression analysis between observed and predicted yield. As the vegetation indices, NDVI showed higher accuracy in the yield prediction model (R2=0.62) compared to SAVI and IRECI. Meanwhile, LAI had a higher prediction accuracy (R2=0.70) than other biophysical properties, fAPAR. The combined model using NDVI, SAVI, IRECI, LAI, and fAPAR reported the highest accuracy (R2=0.77). The ground truth data were used for the evaluation of satellite remote sensing data in the comparison between the observed and predicted yields. This developed integrated model could be implemented for the management of land allocation and yield assessment in cassava production to ensure regional food security in Indonesia.
Keywords: Land suitability | Cassava | Yield prediction | Fuzzy expert systems | Remote sensing
مقاله انگلیسی
2 Artificial intelligence SF6 circuit breaker health assessment
ارزیابی سلامت سلامت قطع کننده مدار SF6 هوش مصنوعی-2019
This paper presents advance artificial intelligence (AI) methods for health assessment of high voltage SF6 circuit breakers. Paper presents an overview of monitoring and diagnostics of most important indicators of the state of high voltage SF6 circuit breakers. Special attention is devoted to identifying and determining indicator limit values which can be used by AI in order to create new health assessment. Fuzzy logic as a part of AI was applied to define fuzzy expert systems which will make decisions about the maintenance of circuit breakers. Three fuzzy expert systems are created to indicate the state of: contacts, the fluid for extinguishing the electric arc and the drive mechanism. Unsupervised machine learning (UML) was applied through the k-means cluster method and cluster tree for classifying and dividing the examined high-voltage circuit breakers into groups with similar state and probability of failure. Artificial neural network (ANN) as part of supervised machine learning (SML) is created in order to predict end-life and accelerated aging of tested circuit breakers. The presented AI methods can be used to improve health assessment of high-voltage SF6 circuit breakers
Keywords: Circuit breaker | Diagnostics | Artificial intelligence | Fuzzy logic | Machine learning
مقاله انگلیسی
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