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 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.

On Parallel Online Learning for Adaptive Embedded Systems

2014 ◽  
pp. 262-281
Author(s):  
Tapio Pahikkala ◽  
Antti Airola ◽  
Thomas Canhao Xu ◽  
Pasi Liljeberg ◽  
Hannu Tenhunen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Adaptive Systems ◽  
Learning Algorithm ◽  
Parallel Implementation ◽  
Recognition Task ◽  
Accurate Solution ◽  
Training Data ◽  
Machine Learning Algorithm ◽  
Systems Learning ◽  
On Chip

This chapter considers parallel implementation of the online multi-label regularized least-squares machine-learning algorithm for embedded hardware platforms. The authors focus on the following properties required in real-time adaptive systems: learning in online fashion, that is, the model improves with new data but does not require storing it; the method can fully utilize the computational abilities of modern embedded multi-core computer architectures; and the system efficiently learns to predict several labels simultaneously. They demonstrate on a hand-written digit recognition task that the online algorithm converges faster, with respect to the amount of training data processed, to an accurate solution than a stochastic gradient descent based baseline. Further, the authors show that our parallelization of the method scales well on a quad-core platform. Moreover, since Network-on-Chip (NoC) has been proposed as a promising candidate for future multi-core architectures, they implement a NoC system consisting of 16 cores. The proposed machine learning algorithm is evaluated in the NoC platform. Experimental results show that, by optimizing the cache behaviour of the program, cache/memory efficiency can improve significantly. Results from the chapter provide a guideline for designing future embedded multi-core machine learning devices.

scholarly journals MQTT Protocol based Smart Greenhouse Environment Monitoring System using Machine Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 278-285
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Optimal Growth ◽  
Growth And Yield ◽  
Weather Data ◽  
Machine Learning Algorithm ◽  
Smart Farming ◽  
Decision Tree Method

Internet of Things (IoT) allows connections among various devices using the internet with the ability to gather and exchange data. IoT has various connecting protocols like HTTPS, MQTT, CoAP, SMCP, etc. A lightweight protocol of all these protocols is the Message Queuing Telemetry Transport (MQTT) protocol. Agriculture is the backbone of India it plays a significant role in the growth of the economics of the country. The majority of the population in India focused on developing a good yield of the crop at their available space which is leading to the development of various greenhouse and smart farming methods. The technology developments will be enabling to design and develop a simple intelligent system for smart farming and maintaining the greenhouse environment. The proposed system is designed using an ARM Cortex processor with the other supporting peripherals for monitoring and constantly updating and controlling environmental parameter values to achieve optimal growth and yield of plants. In this paper, the proposed system consists of several sensors for measuring different parameters including temperature, humidity, soil moisture, air pressure, and fertilizer content. Further, the obtained data is sent to the cloud by using IoT based ThingSpeak with the secured MQTT protocol to monitor the parameters. An efficient Machine Learning algorithm is developed to predict the parameters like soil moisture, fertilizer content sprayed and weather data i.e., humidity, and temperature. The accuracy obtained using Machine Learning algorithm i.e. Decision Tree method is 97%.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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