scholarly journals PEST INFESTATION IDENTIFICATION IN COCONUT TREES USING DEEP LEARNING

2019 ◽  
Vol 01 (01) ◽  
pp. 10-18
Author(s):  
Abraham Chandy
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Learning Algorithm ◽  
Smart Phone ◽  
System On Chip ◽  
Machine Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Pest Infestation ◽  
On Chip ◽  
Timely Treatment

In this paper, we propose a precision agriculture technique to detect various pests in coconut trees with the help of NVIDIA Tegra System on Chip (SoC) along with a camera interfaced drone. The drone flies across the coconut farm and captures the images and processes the data using deep learning algorithm to identify the unhealthy and pest affected trees. The deep learning algorithm uses a set of sample pest database. The Artificial Intelligence (AI) machine learning algorithm is also capable of unsupervised learning from the images that are unstructured. The data is transferred directly to the farmer’s smart phone with the help of wi-fi. This helps in timely treatment of pest infected trees and to improve the yield of the trees.

scholarly journals Luminance Degradation Compensation Based on Multi-Stream Self-Attention to Address Thin Film Transistor-Organic Light Emitting Diode Burn-in

2021 ◽  
Author(s):  
Seongchel Park ◽  
Kwan-Ho Park ◽  
Joon-Hyuk Chang
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Light Emitting Diode ◽  
Thin Film Transistor ◽  
Light Emitting ◽  
Organic Light Emitting Diode ◽  
Deep Learning Algorithm ◽  
Organic Light ◽  
Deep Feature ◽  
On Chip

In this study, we propose a deep learning algorithm that directly compensates for luminance degradation owing to the deterioration of organic light emitting diode (OLED) devices to address the burn-in phenomenon of OLED displays. Conventional compensation circuits are encumbered by a high cost of development and manufacturing processes owing to their complexity. However, given that deep learning algorithms are typically mounted on a system on chip (SoC), the complexity of the circuit design is reduced, and the circuit can be reused by re-learning only the changed characteristics of the new pixel device. The proposed approach comprises deep feature generation and multi-stream self-attention, which decipher the importance of the variables, and the correlation between burn-in-related variables. It also utilizes a deep neural network that identifies the nonlinear relationship between the extracted features and luminance degradation. Thereafter, the luminance degradation is estimated from the burn-in-related variables, and the burn-in phenomenon can be addressed by compensating for the luminance degradation. The experimental results revealed that compensation was successfully achieved within an error range of 2.69%, and demonstrate the potential of a new approach that can mitigate the burn-in phenomenon by directly compensating for pixel-level luminance deviation.

scholarly journals Identification of Electrical Faults in Underground Cables Using Machine Learning Algorithm

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 20
Author(s):  
Paramasivam Alagumariappan ◽  
Mohamed Shuaib Y ◽  
Sonya A ◽  
Irum Fathima
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Transmission Lines ◽  
Performance Metrics ◽  
Intelligent System ◽  
Learning Algorithm ◽  
System On Chip ◽  
Machine Learning Algorithm ◽  
Thermal Camera ◽  
On Chip

Transmission and distribution play a vital role in delivering electricity. The presence of any fault in these systems may stop the delivery of electricity, which may create a huge problem in today’s world. Hence, fault detection has become essential for delivering uninterrupted power supply. In this work, a portable and intelligent system is designed, and the fault detection on underground transmission lines is done using a developed hardware system. Also, the proposed system has a thermal camera which is an 8 × 8 array of infrared thermal sensors interfaced with a system-on-chip device, which collects the real-time thermal images when connected to the device. Further, the thermal camera returns an array of 64 individual infrared temperature readings of the transmission line and locates the point of damage that might occur due to the aging of conductor insulation, physical force, etc. Also, 200 images with thermal information from the different instances and directions are utilized to train the adapted machine learning algorithm. The python software is utilized to code the machine learning algorithm inside the system-on-chip device. The convolutional neural network-based machine learning algorithm is adopted and validated using various performance metrics such as accuracy, sensitivity, specificity, precision, negative predicted value, and F1_score. Results demonstrate that the proposed hardware is highly capable of locating faults in underground transmission lines.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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